diff --git a/NeuralSeq/LICENSE b/NeuralSeq/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..563c90c56cc4bea1f10c6307123319d92441b556 --- /dev/null +++ b/NeuralSeq/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Jinglin Liu + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/NeuralSeq/README.md b/NeuralSeq/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac390032c587ed007db56faa13d6100dce7b2a76 --- /dev/null +++ b/NeuralSeq/README.md @@ -0,0 +1,9 @@ +--- +title: DiffSinger🎶 Diffusion for Singing Voice Synthesis +emoji: 🎶 +colorFrom: purple +colorTo: blue +sdk: gradio +app_file: "inference/svs/gradio/infer.py" +pinned: false +--- diff --git a/NeuralSeq/configs/config_base.yaml b/NeuralSeq/configs/config_base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8519ba64caf62da365571b0b3edcace16e54c46c --- /dev/null +++ b/NeuralSeq/configs/config_base.yaml @@ -0,0 +1,42 @@ +# task +binary_data_dir: '' +work_dir: '' # experiment directory. +infer: false # infer +seed: 1234 +debug: false +save_codes: + - configs + - modules + - tasks + - utils + - usr + +############# +# dataset +############# +ds_workers: 1 +test_num: 100 +valid_num: 100 +endless_ds: false +sort_by_len: true + +######### +# train and eval +######### +load_ckpt: '' +save_ckpt: true +save_best: false +num_ckpt_keep: 3 +clip_grad_norm: 0 +accumulate_grad_batches: 1 +log_interval: 100 +num_sanity_val_steps: 5 # steps of validation at the beginning +check_val_every_n_epoch: 10 +val_check_interval: 2000 +max_epochs: 1000 +max_updates: 160000 +max_tokens: 31250 +max_sentences: 100000 +max_eval_tokens: -1 +max_eval_sentences: -1 +test_input_dir: '' diff --git a/NeuralSeq/configs/singing/base.yaml b/NeuralSeq/configs/singing/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d211a1a544d86b1ccc4698a68e0c0dd94a4eec0c --- /dev/null +++ b/NeuralSeq/configs/singing/base.yaml @@ -0,0 +1,42 @@ +base_config: + - configs/tts/base.yaml + - configs/tts/base_zh.yaml + + +datasets: [] +test_prefixes: [] +test_num: 0 +valid_num: 0 + +pre_align_cls: data_gen.singing.pre_align.SingingPreAlign +binarizer_cls: data_gen.singing.binarize.SingingBinarizer +pre_align_args: + use_tone: false # for ZH + forced_align: mfa + use_sox: true +hop_size: 128 # Hop size. +fft_size: 512 # FFT size. +win_size: 512 # FFT size. +max_frames: 8000 +fmin: 50 # Minimum freq in mel basis calculation. +fmax: 11025 # Maximum frequency in mel basis calculation. +pitch_type: frame + +hidden_size: 256 +mel_loss: "ssim:0.5|l1:0.5" +lambda_f0: 0.0 +lambda_uv: 0.0 +lambda_energy: 0.0 +lambda_ph_dur: 0.0 +lambda_sent_dur: 0.0 +lambda_word_dur: 0.0 +predictor_grad: 0.0 +use_spk_embed: true +use_spk_id: false + +max_tokens: 20000 +max_updates: 400000 +num_spk: 100 +save_f0: true +use_gt_dur: true +use_gt_f0: true diff --git a/NeuralSeq/configs/singing/fs2.yaml b/NeuralSeq/configs/singing/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..83fe12394dd07f9baa948c96ea1dcfcfbcaa4bc3 --- /dev/null +++ b/NeuralSeq/configs/singing/fs2.yaml @@ -0,0 +1,3 @@ +base_config: + - configs/tts/fs2.yaml + - configs/singing/base.yaml diff --git a/NeuralSeq/configs/tts/base.yaml b/NeuralSeq/configs/tts/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6d249ff47479a9693e6dd97ae0cb00aa436d98f --- /dev/null +++ b/NeuralSeq/configs/tts/base.yaml @@ -0,0 +1,95 @@ +# task +base_config: configs/config_base.yaml +task_cls: '' +############# +# dataset +############# +raw_data_dir: '' +processed_data_dir: '' +binary_data_dir: '' +dict_dir: '' +pre_align_cls: '' +binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer +pre_align_args: + use_tone: true # for ZH + forced_align: mfa + use_sox: false + txt_processor: en + allow_no_txt: false + denoise: false +binarization_args: + shuffle: false + with_txt: true + with_wav: false + with_align: true + with_spk_embed: true + with_f0: true + with_f0cwt: true + +loud_norm: false +endless_ds: true +reset_phone_dict: true + +test_num: 100 +valid_num: 100 +max_frames: 1550 +max_input_tokens: 1550 +audio_num_mel_bins: 80 +audio_sample_rate: 22050 +hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) +win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) +fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) +fmax: 7600 # To be increased/reduced depending on data. +fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter +min_level_db: -100 +num_spk: 1 +mel_vmin: -6 +mel_vmax: 1.5 +ds_workers: 4 + +######### +# model +######### +dropout: 0.1 +enc_layers: 4 +dec_layers: 4 +hidden_size: 384 +num_heads: 2 +prenet_dropout: 0.5 +prenet_hidden_size: 256 +stop_token_weight: 5.0 +enc_ffn_kernel_size: 9 +dec_ffn_kernel_size: 9 +ffn_act: gelu +ffn_padding: 'SAME' + + +########### +# optimization +########### +lr: 2.0 +warmup_updates: 8000 +optimizer_adam_beta1: 0.9 +optimizer_adam_beta2: 0.98 +weight_decay: 0 +clip_grad_norm: 1 + + +########### +# train and eval +########### +max_tokens: 30000 +max_sentences: 100000 +max_eval_sentences: 1 +max_eval_tokens: 60000 +train_set_name: 'train' +valid_set_name: 'valid' +test_set_name: 'test' +vocoder: pwg +vocoder_ckpt: '' +profile_infer: false +out_wav_norm: false +save_gt: false +save_f0: false +gen_dir_name: '' +use_denoise: false diff --git a/NeuralSeq/configs/tts/base_zh.yaml b/NeuralSeq/configs/tts/base_zh.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a43a9ad399233b5520c9dc7476ff611d3fa6e9bb --- /dev/null +++ b/NeuralSeq/configs/tts/base_zh.yaml @@ -0,0 +1,3 @@ +pre_align_args: + txt_processor: zh_g2pM +binarizer_cls: data_gen.tts.binarizer_zh.ZhBinarizer \ No newline at end of file diff --git a/NeuralSeq/configs/tts/emotion/base_text2mel.yaml b/NeuralSeq/configs/tts/emotion/base_text2mel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cef7eca03c268f3619759b733c9370acb3669e98 --- /dev/null +++ b/NeuralSeq/configs/tts/emotion/base_text2mel.yaml @@ -0,0 +1,17 @@ +raw_data_dir: 'data/raw/ESD' +processed_data_dir: 'data/processed/emotion' +binary_data_dir: 'data/binary/emotion' +pre_align_cls: configs.tts.emotion.pre_align.EmoPreAlign +audio_sample_rate: 16000 +binarization_args: + shuffle: true +binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer +use_spk_id: true +test_num: 200 +num_spk: 10 +pitch_type: frame +min_frames: 128 +num_test_samples: 30 +mel_loss: "ssim:0.5|l1:0.5" +vocoder_ckpt: '' +use_emotion: true \ No newline at end of file diff --git a/NeuralSeq/configs/tts/emotion/pre_align.py b/NeuralSeq/configs/tts/emotion/pre_align.py new file mode 100644 index 0000000000000000000000000000000000000000..3b625295a118845c01a3677004070714d11c162b --- /dev/null +++ b/NeuralSeq/configs/tts/emotion/pre_align.py @@ -0,0 +1,25 @@ +import os + +from data_gen.tts.base_preprocess import BasePreprocessor +import glob +import re + +class EmoPreAlign(BasePreprocessor): + + def meta_data(self): + spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020'] + pattern = re.compile('[\t\n ]+') + for spk in spks: + for line in open(f"{self.raw_data_dir}/{spk}/{spk}.txt", 'r'): # 打开文件 + line = re.sub(pattern, ' ', line) + if line == ' ': continue + split_ = line.split(' ') + txt = ' '.join(split_[1: -2]) + item_name = split_[0] + emotion = split_[-2] + wav_fn = f'{self.raw_data_dir}/{spk}/{emotion}/{item_name}.wav' + yield item_name, wav_fn, txt, spk, emotion + + +if __name__ == "__main__": + EmoPreAlign().process() diff --git a/NeuralSeq/configs/tts/fs2.yaml b/NeuralSeq/configs/tts/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd6aa37175542f503d85b2e9f9cbe86238b32700 --- /dev/null +++ b/NeuralSeq/configs/tts/fs2.yaml @@ -0,0 +1,80 @@ +base_config: configs/tts/base.yaml +task_cls: tasks.tts.fs2.FastSpeech2Task + +# model +hidden_size: 256 +dropout: 0.1 +encoder_type: fft # fft|tacotron|tacotron2|conformer +encoder_K: 8 # for tacotron encoder +decoder_type: fft # fft|rnn|conv|conformer +use_pos_embed: true + +# duration +predictor_hidden: -1 +predictor_kernel: 5 +predictor_layers: 2 +dur_predictor_kernel: 3 +dur_predictor_layers: 2 +predictor_dropout: 0.5 + +# pitch and energy +use_pitch_embed: true +pitch_type: ph # frame|ph|cwt +use_uv: true +cwt_hidden_size: 128 +cwt_layers: 2 +cwt_loss: l1 +cwt_add_f0_loss: false +cwt_std_scale: 0.8 + +pitch_ar: false +#pitch_embed_type: 0q +pitch_loss: 'l1' # l1|l2|ssim +pitch_norm: log +use_energy_embed: false + +# reference encoder and speaker embedding +use_spk_id: false +use_split_spk_id: false +use_spk_embed: false +use_var_enc: false +lambda_commit: 0.25 +ref_norm_layer: bn +pitch_enc_hidden_stride_kernel: + - 0,2,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size + - 0,2,5 + - 0,2,5 +dur_enc_hidden_stride_kernel: + - 0,2,3 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size + - 0,2,3 + - 0,1,3 + + +# mel +mel_loss: l1:0.5|ssim:0.5 # l1|l2|gdl|ssim or l1:0.5|ssim:0.5 + +# loss lambda +lambda_f0: 1.0 +lambda_uv: 1.0 +lambda_energy: 0.1 +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 + +# train and eval +pretrain_fs_ckpt: '' +warmup_updates: 2000 +max_tokens: 32000 +max_sentences: 100000 +max_eval_sentences: 1 +max_updates: 120000 +num_valid_plots: 5 +num_test_samples: 0 +test_ids: [] +use_gt_dur: false +use_gt_f0: false + +# exp +dur_loss: mse # huber|mol +norm_type: gn \ No newline at end of file diff --git a/NeuralSeq/configs/tts/hifigan.yaml b/NeuralSeq/configs/tts/hifigan.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ecd7390bcbd3c34a8303057c40a95bc841dd9db --- /dev/null +++ b/NeuralSeq/configs/tts/hifigan.yaml @@ -0,0 +1,21 @@ +base_config: configs/tts/pwg.yaml +task_cls: tasks.vocoder.hifigan.HifiGanTask +resblock: "1" +adam_b1: 0.8 +adam_b2: 0.99 +upsample_rates: [ 8,8,2,2 ] +upsample_kernel_sizes: [ 16,16,4,4 ] +upsample_initial_channel: 128 +resblock_kernel_sizes: [ 3,7,11 ] +resblock_dilation_sizes: [ [ 1,3,5 ], [ 1,3,5 ], [ 1,3,5 ] ] + +lambda_mel: 45.0 + +max_samples: 8192 +max_sentences: 16 + +generator_params: + lr: 0.0002 # Generator's learning rate. + aux_context_window: 0 # Context window size for auxiliary feature. +discriminator_optimizer_params: + lr: 0.0002 # Discriminator's learning rate. \ No newline at end of file diff --git a/NeuralSeq/configs/tts/libritts/__pycache__/pre_align.cpython-38.pyc b/NeuralSeq/configs/tts/libritts/__pycache__/pre_align.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..02f74151f4803603868e392d039f183f37972fbd Binary files /dev/null and b/NeuralSeq/configs/tts/libritts/__pycache__/pre_align.cpython-38.pyc differ diff --git a/NeuralSeq/configs/tts/libritts/base_text2mel.yaml b/NeuralSeq/configs/tts/libritts/base_text2mel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52eb977c2cd837d2df25f62951592328f6861714 --- /dev/null +++ b/NeuralSeq/configs/tts/libritts/base_text2mel.yaml @@ -0,0 +1,14 @@ +raw_data_dir: 'data/raw/LibriTTS' +processed_data_dir: 'data/processed/libritts' +binary_data_dir: 'data/binary/libritts' +pre_align_cls: configs.tts.libritts.pre_align.LibrittsPreAlign +binarization_args: + shuffle: true +use_spk_id: true +test_num: 200 +num_spk: 2320 +pitch_type: frame +min_frames: 128 +num_test_samples: 30 +mel_loss: "ssim:0.5|l1:0.5" +vocoder_ckpt: '' \ No newline at end of file diff --git a/NeuralSeq/configs/tts/libritts/fs2.yaml b/NeuralSeq/configs/tts/libritts/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5250ba5677e94643ea0e72083a908fe4cd2bb87 --- /dev/null +++ b/NeuralSeq/configs/tts/libritts/fs2.yaml @@ -0,0 +1,3 @@ +base_config: + - configs/tts/fs2.yaml + - ./base_text2mel.yaml diff --git a/NeuralSeq/configs/tts/libritts/pre_align.py b/NeuralSeq/configs/tts/libritts/pre_align.py new file mode 100644 index 0000000000000000000000000000000000000000..8f04d01361430a4ad6b02421ac4e20d797f31dc8 --- /dev/null +++ b/NeuralSeq/configs/tts/libritts/pre_align.py @@ -0,0 +1,27 @@ +import os + +from data_gen.tts.base_preprocess import BasePreprocessor +import glob + + +class LibrittsPreAlign(BasePreprocessor): + def meta_data(self): + wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav')) + for wav_fn in wav_fns: + item_name = os.path.basename(wav_fn)[:-4] + txt_fn = f'{wav_fn[:-4]}.normalized.txt' + with open(txt_fn, 'r') as f: + txt = f.readlines() + f.close() + spk = item_name.split("_")[0] + # Example: + # + # 'item_name': '103_1241_000000_000001' + # 'wav_fn': 'LibriTTS/train-clean-100/103/1241/103_1241_000000_000001.wav' + # 'txt': 'matthew Cuthbert is surprised' + # 'spk_name': '103' + yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt[0], 'spk_name': spk} + + +if __name__ == "__main__": + LibrittsPreAlign().process() diff --git a/NeuralSeq/configs/tts/libritts/pwg.yaml b/NeuralSeq/configs/tts/libritts/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a0fd70869274c3f8a7ac02a9ca5d7e1202dc895d --- /dev/null +++ b/NeuralSeq/configs/tts/libritts/pwg.yaml @@ -0,0 +1,8 @@ +base_config: egs/egs_bases/tts/vocoder/pwg.yaml +raw_data_dir: 'data/raw/LibriTTS' +processed_data_dir: 'data/processed/libritts' +binary_data_dir: 'data/binary/libritts_wav' +generator_params: + kernel_size: 5 +num_spk: 400 +max_samples: 20480 diff --git a/NeuralSeq/configs/tts/lj/base_mel2wav.yaml b/NeuralSeq/configs/tts/lj/base_mel2wav.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f99da439f539cee27f29d60bfbe2edcc06858bac --- /dev/null +++ b/NeuralSeq/configs/tts/lj/base_mel2wav.yaml @@ -0,0 +1,3 @@ +raw_data_dir: 'data/raw/LJSpeech-1.1' +processed_data_dir: 'data/processed/ljspeech' +binary_data_dir: 'data/binary/ljspeech_wav' diff --git a/NeuralSeq/configs/tts/lj/base_text2mel.yaml b/NeuralSeq/configs/tts/lj/base_text2mel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e0f3ce585226bb0f44533281033098d174ca7fb --- /dev/null +++ b/NeuralSeq/configs/tts/lj/base_text2mel.yaml @@ -0,0 +1,13 @@ +raw_data_dir: 'data/raw/LJSpeech-1.1' +processed_data_dir: 'data/processed/ljspeech' +binary_data_dir: 'data/binary/ljspeech' +pre_align_cls: data_gen.tts.lj.pre_align.LJPreAlign + +pitch_type: cwt +mel_loss: l1 +num_test_samples: 20 +test_ids: [ 68, 70, 74, 87, 110, 172, 190, 215, 231, 294, + 316, 324, 402, 422, 485, 500, 505, 508, 509, 519 ] +use_energy_embed: false +test_num: 523 +valid_num: 348 \ No newline at end of file diff --git a/NeuralSeq/configs/tts/lj/fs2.yaml b/NeuralSeq/configs/tts/lj/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc894b16c094b3d94771a0ad73566c28381c61e4 --- /dev/null +++ b/NeuralSeq/configs/tts/lj/fs2.yaml @@ -0,0 +1,3 @@ +base_config: + - configs/tts/fs2.yaml + - configs/tts/lj/base_text2mel.yaml \ No newline at end of file diff --git a/NeuralSeq/configs/tts/lj/hifigan.yaml b/NeuralSeq/configs/tts/lj/hifigan.yaml new file mode 100644 index 0000000000000000000000000000000000000000..68354a46f3c38b420283b12bc7d62761bfd38d5e --- /dev/null +++ b/NeuralSeq/configs/tts/lj/hifigan.yaml @@ -0,0 +1,3 @@ +base_config: + - configs/tts/hifigan.yaml + - configs/tts/lj/base_mel2wav.yaml \ No newline at end of file diff --git a/NeuralSeq/configs/tts/lj/pwg.yaml b/NeuralSeq/configs/tts/lj/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..72d539eaabd8035b8e38a0ded5d579efa8d42520 --- /dev/null +++ b/NeuralSeq/configs/tts/lj/pwg.yaml @@ -0,0 +1,3 @@ +base_config: + - configs/tts/pwg.yaml + - configs/tts/lj/base_mel2wav.yaml \ No newline at end of file diff --git a/NeuralSeq/configs/tts/pwg.yaml b/NeuralSeq/configs/tts/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb443d584b64223ac467417c12f62f6f403fa52f --- /dev/null +++ b/NeuralSeq/configs/tts/pwg.yaml @@ -0,0 +1,110 @@ +base_config: configs/tts/base.yaml +task_cls: tasks.vocoder.pwg.PwgTask + +binarization_args: + with_wav: true + with_spk_embed: false + with_align: false +test_input_dir: '' + +########### +# train and eval +########### +max_samples: 25600 +max_sentences: 5 +max_eval_sentences: 1 +max_updates: 1000000 +val_check_interval: 2000 + + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +sampling_rate: 22050 # Sampling rate. +fft_size: 1024 # FFT size. +hop_size: 256 # Hop size. +win_length: null # Window length. +# If set to null, it will be the same as fft_size. +window: "hann" # Window function. +num_mels: 80 # Number of mel basis. +fmin: 80 # Minimum freq in mel basis calculation. +fmax: 7600 # Maximum frequency in mel basis calculation. +format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. + +########################################################### +# GENERATOR NETWORK ARCHITECTURE SETTING # +########################################################### +generator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Kernel size of dilated convolution. + layers: 30 # Number of residual block layers. + stacks: 3 # Number of stacks i.e., dilation cycles. + residual_channels: 64 # Number of channels in residual conv. + gate_channels: 128 # Number of channels in gated conv. + skip_channels: 64 # Number of channels in skip conv. + aux_channels: 80 # Number of channels for auxiliary feature conv. + # Must be the same as num_mels. + aux_context_window: 2 # Context window size for auxiliary feature. + # If set to 2, previous 2 and future 2 frames will be considered. + dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. + upsample_params: # Upsampling network parameters. + upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. + use_pitch_embed: false + +########################################################### +# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # +########################################################### +discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Number of output channels. + layers: 10 # Number of conv layers. + conv_channels: 64 # Number of chnn layers. + bias: true # Whether to use bias parameter in conv. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. + nonlinear_activation_params: # Nonlinear function parameters + negative_slope: 0.2 # Alpha in LeakyReLU. + +########################################################### +# STFT LOSS SETTING # +########################################################### +stft_loss_params: + fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. + hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss + win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. + window: "hann_window" # Window function for STFT-based loss +use_mel_loss: false + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_adv: 4.0 # Loss balancing coefficient. + +########################################################### +# OPTIMIZER & SCHEDULER SETTING # +########################################################### +generator_optimizer_params: + lr: 0.0001 # Generator's learning rate. + eps: 1.0e-6 # Generator's epsilon. + weight_decay: 0.0 # Generator's weight decay coefficient. +generator_scheduler_params: + step_size: 200000 # Generator's scheduler step size. + gamma: 0.5 # Generator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +generator_grad_norm: 10 # Generator's gradient norm. +discriminator_optimizer_params: + lr: 0.00005 # Discriminator's learning rate. + eps: 1.0e-6 # Discriminator's epsilon. + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + step_size: 200000 # Discriminator's scheduler step size. + gamma: 0.5 # Discriminator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +discriminator_grad_norm: 1 # Discriminator's gradient norm. +disc_start_steps: 40000 # Number of steps to start to train discriminator. diff --git a/NeuralSeq/data_gen/tts/__pycache__/base_binarizer.cpython-38.pyc b/NeuralSeq/data_gen/tts/__pycache__/base_binarizer.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..727180589707ff1f0d10edf1894f9c0a88ba6250 Binary files /dev/null and b/NeuralSeq/data_gen/tts/__pycache__/base_binarizer.cpython-38.pyc differ diff --git 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/dev/null and b/NeuralSeq/data_gen/tts/__pycache__/data_gen_utils.cpython-37.pyc differ diff --git a/NeuralSeq/data_gen/tts/__pycache__/data_gen_utils.cpython-38.pyc b/NeuralSeq/data_gen/tts/__pycache__/data_gen_utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..436672bdaa39e014f6e9d4cb0bacd2cb4e4d545c Binary files /dev/null and b/NeuralSeq/data_gen/tts/__pycache__/data_gen_utils.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/base_binarizer.py b/NeuralSeq/data_gen/tts/base_binarizer.py new file mode 100644 index 0000000000000000000000000000000000000000..b30a20c1cdc3403214ff527d68a50806befafeb9 --- /dev/null +++ b/NeuralSeq/data_gen/tts/base_binarizer.py @@ -0,0 +1,224 @@ +import os +os.environ["OMP_NUM_THREADS"] = "1" + +from utils.multiprocess_utils import chunked_multiprocess_run +import random +import traceback +import json +from resemblyzer import VoiceEncoder +from tqdm import tqdm +from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder +from utils.hparams import set_hparams, hparams +import numpy as np +from utils.indexed_datasets import IndexedDatasetBuilder +from vocoders.base_vocoder import VOCODERS +import pandas as pd + + +class BinarizationError(Exception): + pass + + +class BaseBinarizer: + def __init__(self, processed_data_dir=None): + if processed_data_dir is None: + processed_data_dir = hparams['processed_data_dir'] + self.processed_data_dirs = processed_data_dir.split(",") + self.binarization_args = hparams['binarization_args'] + self.pre_align_args = hparams['pre_align_args'] + self.forced_align = self.pre_align_args['forced_align'] + tg_dir = None + if self.forced_align == 'mfa': + tg_dir = 'mfa_outputs' + if self.forced_align == 'kaldi': + tg_dir = 'kaldi_outputs' + self.item2txt = {} + self.item2ph = {} + self.item2wavfn = {} + self.item2tgfn = {} + self.item2spk = {} + for ds_id, processed_data_dir in enumerate(self.processed_data_dirs): + self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str) + for r_idx, r in self.meta_df.iterrows(): + item_name = raw_item_name = r['item_name'] + if len(self.processed_data_dirs) > 1: + item_name = f'ds{ds_id}_{item_name}' + self.item2txt[item_name] = r['txt'] + self.item2ph[item_name] = r['ph'] + self.item2wavfn[item_name] = os.path.join(hparams['raw_data_dir'], 'wavs', os.path.basename(r['wav_fn']).split('_')[1]) + self.item2spk[item_name] = r.get('spk', 'SPK1') + if len(self.processed_data_dirs) > 1: + self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}" + if tg_dir is not None: + self.item2tgfn[item_name] = f"{processed_data_dir}/{tg_dir}/{raw_item_name}.TextGrid" + self.item_names = sorted(list(self.item2txt.keys())) + if self.binarization_args['shuffle']: + random.seed(1234) + random.shuffle(self.item_names) + + @property + def train_item_names(self): + return self.item_names[hparams['test_num']+hparams['valid_num']:] + + @property + def valid_item_names(self): + return self.item_names[0: hparams['test_num']+hparams['valid_num']] # + + @property + def test_item_names(self): + return self.item_names[0: hparams['test_num']] # Audios for MOS testing are in 'test_ids' + + def build_spk_map(self): + spk_map = set() + for item_name in self.item_names: + spk_name = self.item2spk[item_name] + spk_map.add(spk_name) + spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))} + assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) + return spk_map + + def item_name2spk_id(self, item_name): + return self.spk_map[self.item2spk[item_name]] + + def _phone_encoder(self): + ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" + ph_set = [] + if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn): + for processed_data_dir in self.processed_data_dirs: + ph_set += [x.split(' ')[0] for x in open(f'{processed_data_dir}/dict.txt').readlines()] + ph_set = sorted(set(ph_set)) + json.dump(ph_set, open(ph_set_fn, 'w')) + else: + ph_set = json.load(open(ph_set_fn, 'r')) + print("| phone set: ", ph_set) + return build_phone_encoder(hparams['binary_data_dir']) + + def meta_data(self, prefix): + if prefix == 'valid': + item_names = self.valid_item_names + elif prefix == 'test': + item_names = self.test_item_names + else: + item_names = self.train_item_names + for item_name in item_names: + ph = self.item2ph[item_name] + txt = self.item2txt[item_name] + tg_fn = self.item2tgfn.get(item_name) + wav_fn = self.item2wavfn[item_name] + spk_id = self.item_name2spk_id(item_name) + yield item_name, ph, txt, tg_fn, wav_fn, spk_id + + def process(self): + os.makedirs(hparams['binary_data_dir'], exist_ok=True) + self.spk_map = self.build_spk_map() + print("| spk_map: ", self.spk_map) + spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json" + json.dump(self.spk_map, open(spk_map_fn, 'w')) + + self.phone_encoder = self._phone_encoder() + self.process_data('valid') + self.process_data('test') + self.process_data('train') + + def process_data(self, prefix): + data_dir = hparams['binary_data_dir'] + args = [] + builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}') + lengths = [] + f0s = [] + total_sec = 0 + if self.binarization_args['with_spk_embed']: + voice_encoder = VoiceEncoder().cuda() + + meta_data = list(self.meta_data(prefix)) + for m in meta_data: + args.append(list(m) + [self.phone_encoder, self.binarization_args]) + num_workers = int(os.getenv('N_PROC', os.cpu_count() // 3)) + for f_id, (_, item) in enumerate( + zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))): + if item is None: + continue + item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ + if self.binarization_args['with_spk_embed'] else None + if not self.binarization_args['with_wav'] and 'wav' in item: + print("del wav") + del item['wav'] + builder.add_item(item) + lengths.append(item['len']) + total_sec += item['sec'] + if item.get('f0') is not None: + f0s.append(item['f0']) + builder.finalize() + np.save(f'{data_dir}/{prefix}_lengths.npy', lengths) + if len(f0s) > 0: + f0s = np.concatenate(f0s, 0) + f0s = f0s[f0s != 0] + np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()]) + print(f"| {prefix} total duration: {total_sec:.3f}s") + + @classmethod + def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args): + if hparams['vocoder'] in VOCODERS: + wav, mel = VOCODERS[hparams['vocoder']].wav2spec(wav_fn) + else: + wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(wav_fn) + res = { + 'item_name': item_name, 'txt': txt, 'ph': ph, 'mel': mel, 'wav': wav, 'wav_fn': wav_fn, + 'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0], 'spk_id': spk_id + } + try: + if binarization_args['with_f0']: + cls.get_pitch(wav, mel, res) + if binarization_args['with_f0cwt']: + cls.get_f0cwt(res['f0'], res) + if binarization_args['with_txt']: + try: + phone_encoded = res['phone'] = encoder.encode(ph) + except: + traceback.print_exc() + raise BinarizationError(f"Empty phoneme") + if binarization_args['with_align']: + cls.get_align(tg_fn, ph, mel, phone_encoded, res) + except BinarizationError as e: + print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}") + return None + return res + + @staticmethod + def get_align(tg_fn, ph, mel, phone_encoded, res): + if tg_fn is not None and os.path.exists(tg_fn): + mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams) + else: + raise BinarizationError(f"Align not found") + if mel2ph.max() - 1 >= len(phone_encoded): + raise BinarizationError( + f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}") + res['mel2ph'] = mel2ph + res['dur'] = dur + + @staticmethod + def get_pitch(wav, mel, res): + f0, pitch_coarse = get_pitch(wav, mel, hparams) + if sum(f0) == 0: + raise BinarizationError("Empty f0") + res['f0'] = f0 + res['pitch'] = pitch_coarse + + @staticmethod + def get_f0cwt(f0, res): + from utils.cwt import get_cont_lf0, get_lf0_cwt + uv, cont_lf0_lpf = get_cont_lf0(f0) + logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf) + cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org + Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) + if np.any(np.isnan(Wavelet_lf0)): + raise BinarizationError("NaN CWT") + res['cwt_spec'] = Wavelet_lf0 + res['cwt_scales'] = scales + res['f0_mean'] = logf0s_mean_org + res['f0_std'] = logf0s_std_org + + +if __name__ == "__main__": + set_hparams() + BaseBinarizer().process() diff --git a/NeuralSeq/data_gen/tts/base_binarizer_emotion.py b/NeuralSeq/data_gen/tts/base_binarizer_emotion.py new file mode 100644 index 0000000000000000000000000000000000000000..2193a39417781a4673c11210cecb146401be2d09 --- /dev/null +++ b/NeuralSeq/data_gen/tts/base_binarizer_emotion.py @@ -0,0 +1,352 @@ +import os + +os.environ["OMP_NUM_THREADS"] = "1" +import torch +from collections import Counter +from utils.text_encoder import TokenTextEncoder +from data_gen.tts.emotion import inference as EmotionEncoder +from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance +from data_gen.tts.emotion.inference import preprocess_wav +from utils.multiprocess_utils import chunked_multiprocess_run +import random +import traceback +import json +from resemblyzer import VoiceEncoder +from tqdm import tqdm +from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder, is_sil_phoneme +from utils.hparams import hparams, set_hparams +import numpy as np +from utils.indexed_datasets import IndexedDatasetBuilder +from vocoders.base_vocoder import get_vocoder_cls +import pandas as pd + + +class BinarizationError(Exception): + pass + + +class EmotionBinarizer: + def __init__(self, processed_data_dir=None): + if processed_data_dir is None: + processed_data_dir = hparams['processed_data_dir'] + self.processed_data_dirs = processed_data_dir.split(",") + self.binarization_args = hparams['binarization_args'] + self.pre_align_args = hparams['pre_align_args'] + self.item2txt = {} + self.item2ph = {} + self.item2wavfn = {} + self.item2tgfn = {} + self.item2spk = {} + self.item2emo = {} + + def load_meta_data(self): + for ds_id, processed_data_dir in enumerate(self.processed_data_dirs): + self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str) + for r_idx, r in tqdm(self.meta_df.iterrows(), desc='Loading meta data.'): + item_name = raw_item_name = r['item_name'] + if len(self.processed_data_dirs) > 1: + item_name = f'ds{ds_id}_{item_name}' + self.item2txt[item_name] = r['txt'] + self.item2ph[item_name] = r['ph'] + self.item2wavfn[item_name] = r['wav_fn'] + self.item2spk[item_name] = r.get('spk_name', 'SPK1') \ + if self.binarization_args['with_spk_id'] else 'SPK1' + if len(self.processed_data_dirs) > 1: + self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}" + self.item2tgfn[item_name] = f"{processed_data_dir}/mfa_outputs/{raw_item_name}.TextGrid" + self.item2emo[item_name] = r.get('others', '"Neutral"') + self.item_names = sorted(list(self.item2txt.keys())) + if self.binarization_args['shuffle']: + random.seed(1234) + random.shuffle(self.item_names) + + @property + def train_item_names(self): + return self.item_names[hparams['test_num']:] + + @property + def valid_item_names(self): + return self.item_names[:hparams['test_num']] + + @property + def test_item_names(self): + return self.valid_item_names + + def build_spk_map(self): + spk_map = set() + for item_name in self.item_names: + spk_name = self.item2spk[item_name] + spk_map.add(spk_name) + spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))} + print("| #Spk: ", len(spk_map)) + assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) + return spk_map + + def build_emo_map(self): + emo_map = set() + for item_name in self.item_names: + emo_name = self.item2emo[item_name] + emo_map.add(emo_name) + emo_map = {x: i for i, x in enumerate(sorted(list(emo_map)))} + print("| #Emo: ", len(emo_map)) + return emo_map + + def item_name2spk_id(self, item_name): + return self.spk_map[self.item2spk[item_name]] + + def item_name2emo_id(self, item_name): + return self.emo_map[self.item2emo[item_name]] + + def _phone_encoder(self): + ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" + ph_set = [] + if self.binarization_args['reset_phone_dict'] or not os.path.exists(ph_set_fn): + for ph_sent in self.item2ph.values(): + ph_set += ph_sent.split(' ') + ph_set = sorted(set(ph_set)) + json.dump(ph_set, open(ph_set_fn, 'w')) + print("| Build phone set: ", ph_set) + else: + ph_set = json.load(open(ph_set_fn, 'r')) + print("| Load phone set: ", ph_set) + return build_phone_encoder(hparams['binary_data_dir']) + + def _word_encoder(self): + fn = f"{hparams['binary_data_dir']}/word_set.json" + word_set = [] + if self.binarization_args['reset_word_dict']: + for word_sent in self.item2txt.values(): + word_set += [x for x in word_sent.split(' ') if x != ''] + word_set = Counter(word_set) + total_words = sum(word_set.values()) + word_set = word_set.most_common(hparams['word_size']) + num_unk_words = total_words - sum([x[1] for x in word_set]) + word_set = [x[0] for x in word_set] + json.dump(word_set, open(fn, 'w')) + print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words}," + f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.") + else: + word_set = json.load(open(fn, 'r')) + print("| Load word set. Size: ", len(word_set), word_set[:10]) + return TokenTextEncoder(None, vocab_list=word_set, replace_oov='') + + def meta_data(self, prefix): + if prefix == 'valid': + item_names = self.valid_item_names + elif prefix == 'test': + item_names = self.test_item_names + else: + item_names = self.train_item_names + for item_name in item_names: + ph = self.item2ph[item_name] + txt = self.item2txt[item_name] + tg_fn = self.item2tgfn.get(item_name) + wav_fn = self.item2wavfn[item_name] + spk_id = self.item_name2spk_id(item_name) + emotion = self.item_name2emo_id(item_name) + yield item_name, ph, txt, tg_fn, wav_fn, spk_id, emotion + + def process(self): + self.load_meta_data() + os.makedirs(hparams['binary_data_dir'], exist_ok=True) + self.spk_map = self.build_spk_map() + print("| spk_map: ", self.spk_map) + spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json" + json.dump(self.spk_map, open(spk_map_fn, 'w')) + + self.emo_map = self.build_emo_map() + print("| emo_map: ", self.emo_map) + emo_map_fn = f"{hparams['binary_data_dir']}/emo_map.json" + json.dump(self.emo_map, open(emo_map_fn, 'w')) + + self.phone_encoder = self._phone_encoder() + self.word_encoder = None + EmotionEncoder.load_model(hparams['emotion_encoder_path']) + + if self.binarization_args['with_word']: + self.word_encoder = self._word_encoder() + self.process_data('valid') + self.process_data('test') + self.process_data('train') + + def process_data(self, prefix): + data_dir = hparams['binary_data_dir'] + args = [] + builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}') + ph_lengths = [] + mel_lengths = [] + f0s = [] + total_sec = 0 + if self.binarization_args['with_spk_embed']: + voice_encoder = VoiceEncoder().cuda() + + meta_data = list(self.meta_data(prefix)) + for m in meta_data: + args.append(list(m) + [(self.phone_encoder, self.word_encoder), self.binarization_args]) + num_workers = self.num_workers + for f_id, (_, item) in enumerate( + zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))): + if item is None: + continue + item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ + if self.binarization_args['with_spk_embed'] else None + processed_wav = preprocess_wav(item['wav_fn']) + item['emo_embed'] = Embed_utterance(processed_wav) + if not self.binarization_args['with_wav'] and 'wav' in item: + del item['wav'] + builder.add_item(item) + mel_lengths.append(item['len']) + if 'ph_len' in item: + ph_lengths.append(item['ph_len']) + total_sec += item['sec'] + if item.get('f0') is not None: + f0s.append(item['f0']) + builder.finalize() + np.save(f'{data_dir}/{prefix}_lengths.npy', mel_lengths) + if len(ph_lengths) > 0: + np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_lengths) + if len(f0s) > 0: + f0s = np.concatenate(f0s, 0) + f0s = f0s[f0s != 0] + np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()]) + print(f"| {prefix} total duration: {total_sec:.3f}s") + + @classmethod + def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, emotion, encoder, binarization_args): + res = {'item_name': item_name, 'txt': txt, 'ph': ph, 'wav_fn': wav_fn, 'spk_id': spk_id, 'emotion': emotion} + if binarization_args['with_linear']: + wav, mel, linear_stft = get_vocoder_cls(hparams).wav2spec(wav_fn) # , return_linear=True + res['linear'] = linear_stft + else: + wav, mel = get_vocoder_cls(hparams).wav2spec(wav_fn) + wav = wav.astype(np.float16) + res.update({'mel': mel, 'wav': wav, + 'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]}) + try: + if binarization_args['with_f0']: + cls.get_pitch(res) + if binarization_args['with_f0cwt']: + cls.get_f0cwt(res) + if binarization_args['with_txt']: + ph_encoder, word_encoder = encoder + try: + res['phone'] = ph_encoder.encode(ph) + res['ph_len'] = len(res['phone']) + except: + traceback.print_exc() + raise BinarizationError(f"Empty phoneme") + if binarization_args['with_align']: + cls.get_align(tg_fn, res) + if binarization_args['trim_eos_bos']: + bos_dur = res['dur'][0] + eos_dur = res['dur'][-1] + res['mel'] = mel[bos_dur:-eos_dur] + res['f0'] = res['f0'][bos_dur:-eos_dur] + res['pitch'] = res['pitch'][bos_dur:-eos_dur] + res['mel2ph'] = res['mel2ph'][bos_dur:-eos_dur] + res['wav'] = wav[bos_dur * hparams['hop_size']:-eos_dur * hparams['hop_size']] + res['dur'] = res['dur'][1:-1] + res['len'] = res['mel'].shape[0] + if binarization_args['with_word']: + cls.get_word(res, word_encoder) + except BinarizationError as e: + print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}") + return None + except Exception as e: + traceback.print_exc() + print(f"| Skip item. item_name: {item_name}, wav_fn: {wav_fn}") + return None + return res + + @staticmethod + def get_align(tg_fn, res): + ph = res['ph'] + mel = res['mel'] + phone_encoded = res['phone'] + if tg_fn is not None and os.path.exists(tg_fn): + mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams) + else: + raise BinarizationError(f"Align not found") + if mel2ph.max() - 1 >= len(phone_encoded): + raise BinarizationError( + f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}") + res['mel2ph'] = mel2ph + res['dur'] = dur + + @staticmethod + def get_pitch(res): + wav, mel = res['wav'], res['mel'] + f0, pitch_coarse = get_pitch(wav, mel, hparams) + if sum(f0) == 0: + raise BinarizationError("Empty f0") + res['f0'] = f0 + res['pitch'] = pitch_coarse + + @staticmethod + def get_f0cwt(res): + from utils.cwt import get_cont_lf0, get_lf0_cwt + f0 = res['f0'] + uv, cont_lf0_lpf = get_cont_lf0(f0) + logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf) + cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org + Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) + if np.any(np.isnan(Wavelet_lf0)): + raise BinarizationError("NaN CWT") + res['cwt_spec'] = Wavelet_lf0 + res['cwt_scales'] = scales + res['f0_mean'] = logf0s_mean_org + res['f0_std'] = logf0s_std_org + + @staticmethod + def get_word(res, word_encoder): + ph_split = res['ph'].split(" ") + # ph side mapping to word + ph_words = [] # ['', 'N_AW1_', ',', 'AE1_Z_|', 'AO1_L_|', 'B_UH1_K_S_|', 'N_AA1_T_|', ....] + ph2word = np.zeros([len(ph_split)], dtype=int) + last_ph_idx_for_word = [] # [2, 11, ...] + for i, ph in enumerate(ph_split): + if ph == '|': + last_ph_idx_for_word.append(i) + elif not ph[0].isalnum(): + if ph not in ['']: + last_ph_idx_for_word.append(i - 1) + last_ph_idx_for_word.append(i) + start_ph_idx_for_word = [0] + [i + 1 for i in last_ph_idx_for_word[:-1]] + for i, (s_w, e_w) in enumerate(zip(start_ph_idx_for_word, last_ph_idx_for_word)): + ph_words.append(ph_split[s_w:e_w + 1]) + ph2word[s_w:e_w + 1] = i + ph2word = ph2word.tolist() + ph_words = ["_".join(w) for w in ph_words] + + # mel side mapping to word + mel2word = [] + dur_word = [0 for _ in range(len(ph_words))] + for i, m2p in enumerate(res['mel2ph']): + word_idx = ph2word[m2p - 1] + mel2word.append(ph2word[m2p - 1]) + dur_word[word_idx] += 1 + ph2word = [x + 1 for x in ph2word] # 0预留给padding + mel2word = [x + 1 for x in mel2word] # 0预留给padding + res['ph_words'] = ph_words # [T_word] + res['ph2word'] = ph2word # [T_ph] + res['mel2word'] = mel2word # [T_mel] + res['dur_word'] = dur_word # [T_word] + words = [x for x in res['txt'].split(" ") if x != ''] + while len(words) > 0 and is_sil_phoneme(words[0]): + words = words[1:] + while len(words) > 0 and is_sil_phoneme(words[-1]): + words = words[:-1] + words = [''] + words + [''] + word_tokens = word_encoder.encode(" ".join(words)) + res['words'] = words + res['word_tokens'] = word_tokens + assert len(words) == len(ph_words), [words, ph_words] + + @property + def num_workers(self): + return int(os.getenv('N_PROC', hparams.get('N_PROC', os.cpu_count()))) + + +if __name__ == "__main__": + set_hparams() + EmotionBinarizer().process() diff --git a/NeuralSeq/data_gen/tts/base_preprocess.py b/NeuralSeq/data_gen/tts/base_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..56c69875ad74ad45e774efa4029c5e3d858166ce --- /dev/null +++ b/NeuralSeq/data_gen/tts/base_preprocess.py @@ -0,0 +1,254 @@ +import json +import os +import random +import re +import traceback +from collections import Counter +from functools import partial +import pandas as pd +import librosa +from tqdm import tqdm +from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls +from data_gen.tts.wav_processors.base_processor import get_wav_processor_cls +from utils.hparams import hparams +from utils.multiprocess_utils import multiprocess_run_tqdm +from utils.os_utils import link_file, move_file, remove_file +from data_gen.tts.data_gen_utils import is_sil_phoneme, build_token_encoder + + +class BasePreprocessor: + def __init__(self): + self.preprocess_args = hparams['preprocess_args'] + txt_processor = self.preprocess_args['txt_processor'] + self.txt_processor = get_txt_processor_cls(txt_processor) + self.raw_data_dir = hparams['raw_data_dir'] + self.processed_dir = hparams['processed_data_dir'] + self.spk_map_fn = f"{self.processed_dir}/spk_map.json" + + def meta_data(self): + """ + :return: {'item_name': Str, 'wav_fn': Str, 'txt': Str, 'spk_name': Str, 'txt_loader': None or Func} + """ + raise NotImplementedError + + def process(self): + processed_dir = self.processed_dir + wav_processed_tmp_dir = f'{processed_dir}/processed_tmp' + remove_file(wav_processed_tmp_dir) + os.makedirs(wav_processed_tmp_dir, exist_ok=True) + wav_processed_dir = f'{processed_dir}/{self.wav_processed_dirname}' + remove_file(wav_processed_dir) + os.makedirs(wav_processed_dir, exist_ok=True) + + meta_data = list(tqdm(self.meta_data(), desc='Load meta data')) + item_names = [d['item_name'] for d in meta_data] + assert len(item_names) == len(set(item_names)), 'Key `item_name` should be Unique.' + + # preprocess data + phone_list = [] + word_list = [] + spk_names = set() + process_item = partial(self.preprocess_first_pass, + txt_processor=self.txt_processor, + wav_processed_dir=wav_processed_dir, + wav_processed_tmp=wav_processed_tmp_dir, + preprocess_args=self.preprocess_args) + items = [] + args = [{ + 'item_name': item_raw['item_name'], + 'txt_raw': item_raw['txt'], + 'wav_fn': item_raw['wav_fn'], + 'txt_loader': item_raw.get('txt_loader'), + 'others': item_raw.get('others', None) + } for item_raw in meta_data] + for item_, (item_id, item) in zip(meta_data, multiprocess_run_tqdm(process_item, args, desc='Preprocess')): + if item is not None: + item_.update(item) + item = item_ + if 'txt_loader' in item: + del item['txt_loader'] + item['id'] = item_id + item['spk_name'] = item.get('spk_name', '') + item['others'] = item.get('others', None) + phone_list += item['ph'].split(" ") + word_list += item['word'].split(" ") + spk_names.add(item['spk_name']) + items.append(item) + + # add encoded tokens + ph_encoder, word_encoder = self._phone_encoder(phone_list), self._word_encoder(word_list) + spk_map = self.build_spk_map(spk_names) + args = [{ + 'ph': item['ph'], 'word': item['word'], 'spk_name': item['spk_name'], + 'word_encoder': word_encoder, 'ph_encoder': ph_encoder, 'spk_map': spk_map + } for item in items] + for idx, item_new_kv in multiprocess_run_tqdm(self.preprocess_second_pass, args, desc='Add encoded tokens'): + items[idx].update(item_new_kv) + + # build mfa data + if self.preprocess_args['use_mfa']: + mfa_dict = set() + mfa_input_dir = f'{processed_dir}/mfa_inputs' + remove_file(mfa_input_dir) + # group MFA inputs for better parallelism + mfa_groups = [i // self.preprocess_args['nsample_per_mfa_group'] for i in range(len(items))] + if self.preprocess_args['mfa_group_shuffle']: + random.seed(hparams['seed']) + random.shuffle(mfa_groups) + args = [{ + 'item': item, 'mfa_input_dir': mfa_input_dir, + 'mfa_group': mfa_group, 'wav_processed_tmp': wav_processed_tmp_dir, + 'preprocess_args': self.preprocess_args + } for item, mfa_group in zip(items, mfa_groups)] + for i, (ph_gb_word_nosil, new_wav_align_fn) in multiprocess_run_tqdm( + self.build_mfa_inputs, args, desc='Build MFA data'): + items[i]['wav_align_fn'] = new_wav_align_fn + for w in ph_gb_word_nosil.split(" "): + mfa_dict.add(f"{w} {w.replace('_', ' ')}") + mfa_dict = sorted(mfa_dict) + with open(f'{processed_dir}/mfa_dict.txt', 'w') as f: + f.writelines([f'{l}\n' for l in mfa_dict]) + with open(f"{processed_dir}/{self.meta_csv_filename}.json", 'w') as f: + f.write(re.sub(r'\n\s+([\d+\]])', r'\1', json.dumps(items, ensure_ascii=False, sort_keys=False, indent=1))) + remove_file(wav_processed_tmp_dir) + + + @classmethod + def preprocess_first_pass(cls, item_name, txt_raw, txt_processor, + wav_fn, wav_processed_dir, wav_processed_tmp, + preprocess_args, txt_loader=None, others=None): + try: + if txt_loader is not None: + txt_raw = txt_loader(txt_raw) + ph, txt, word, ph2word, ph_gb_word = cls.txt_to_ph(txt_processor, txt_raw, preprocess_args) + wav_fn, wav_align_fn = cls.process_wav( + item_name, wav_fn, + hparams['processed_data_dir'], + wav_processed_tmp, preprocess_args) + + # wav for binarization + ext = os.path.splitext(wav_fn)[1] + os.makedirs(wav_processed_dir, exist_ok=True) + new_wav_fn = f"{wav_processed_dir}/{item_name}{ext}" + move_link_func = move_file if os.path.dirname(wav_fn) == wav_processed_tmp else link_file + move_link_func(wav_fn, new_wav_fn) + return { + 'txt': txt, 'txt_raw': txt_raw, 'ph': ph, + 'word': word, 'ph2word': ph2word, 'ph_gb_word': ph_gb_word, + 'wav_fn': new_wav_fn, 'wav_align_fn': wav_align_fn, + 'others': others + } + except: + traceback.print_exc() + print(f"| Error is caught. item_name: {item_name}.") + return None + + @staticmethod + def txt_to_ph(txt_processor, txt_raw, preprocess_args): + txt_struct, txt = txt_processor.process(txt_raw, preprocess_args) + ph = [p for w in txt_struct for p in w[1]] + ph_gb_word = ["_".join(w[1]) for w in txt_struct] + words = [w[0] for w in txt_struct] + # word_id=0 is reserved for padding + ph2word = [w_id + 1 for w_id, w in enumerate(txt_struct) for _ in range(len(w[1]))] + return " ".join(ph), txt, " ".join(words), ph2word, " ".join(ph_gb_word) + + @staticmethod + def process_wav(item_name, wav_fn, processed_dir, wav_processed_tmp, preprocess_args): + processors = [get_wav_processor_cls(v) for v in preprocess_args['wav_processors']] + processors = [k() for k in processors if k is not None] + if len(processors) >= 1: + sr_file = librosa.core.get_samplerate(wav_fn) + output_fn_for_align = None + ext = os.path.splitext(wav_fn)[1] + input_fn = f"{wav_processed_tmp}/{item_name}{ext}" + link_file(wav_fn, input_fn) + for p in processors: + outputs = p.process(input_fn, sr_file, wav_processed_tmp, processed_dir, item_name, preprocess_args) + if len(outputs) == 3: + input_fn, sr, output_fn_for_align = outputs + else: + input_fn, sr = outputs + if output_fn_for_align is None: + return input_fn, input_fn + else: + return input_fn, output_fn_for_align + else: + return wav_fn, wav_fn + + def _phone_encoder(self, ph_set): + ph_set_fn = f"{self.processed_dir}/phone_set.json" + if self.preprocess_args['reset_phone_dict'] or not os.path.exists(ph_set_fn): + ph_set = sorted(set(ph_set)) + json.dump(ph_set, open(ph_set_fn, 'w'), ensure_ascii=False) + print("| Build phone set: ", ph_set) + else: + ph_set = json.load(open(ph_set_fn, 'r')) + print("| Load phone set: ", ph_set) + return build_token_encoder(ph_set_fn) + + def _word_encoder(self, word_set): + word_set_fn = f"{self.processed_dir}/word_set.json" + if self.preprocess_args['reset_word_dict']: + word_set = Counter(word_set) + total_words = sum(word_set.values()) + word_set = word_set.most_common(hparams['word_dict_size']) + num_unk_words = total_words - sum([x[1] for x in word_set]) + word_set = ['', ''] + [x[0] for x in word_set] + word_set = sorted(set(word_set)) + json.dump(word_set, open(word_set_fn, 'w'), ensure_ascii=False) + print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words}," + f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.") + else: + word_set = json.load(open(word_set_fn, 'r')) + print("| Load word set. Size: ", len(word_set), word_set[:10]) + return build_token_encoder(word_set_fn) + + @classmethod + def preprocess_second_pass(cls, word, ph, spk_name, word_encoder, ph_encoder, spk_map): + word_token = word_encoder.encode(word) + ph_token = ph_encoder.encode(ph) + spk_id = spk_map[spk_name] + return {'word_token': word_token, 'ph_token': ph_token, 'spk_id': spk_id} + + def build_spk_map(self, spk_names): + spk_map = {x: i for i, x in enumerate(sorted(list(spk_names)))} + assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map) + print(f"| Number of spks: {len(spk_map)}, spk_map: {spk_map}") + json.dump(spk_map, open(self.spk_map_fn, 'w'), ensure_ascii=False) + return spk_map + + @classmethod + def build_mfa_inputs(cls, item, mfa_input_dir, mfa_group, wav_processed_tmp, preprocess_args): + item_name = item['item_name'] + wav_align_fn = item['wav_align_fn'] + ph_gb_word = item['ph_gb_word'] + ext = os.path.splitext(wav_align_fn)[1] + mfa_input_group_dir = f'{mfa_input_dir}/{mfa_group}' + os.makedirs(mfa_input_group_dir, exist_ok=True) + new_wav_align_fn = f"{mfa_input_group_dir}/{item_name}{ext}" + move_link_func = move_file if os.path.dirname(wav_align_fn) == wav_processed_tmp else link_file + move_link_func(wav_align_fn, new_wav_align_fn) + ph_gb_word_nosil = " ".join(["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) + for w in ph_gb_word.split(" ") if not is_sil_phoneme(w)]) + with open(f'{mfa_input_group_dir}/{item_name}.lab', 'w') as f_txt: + f_txt.write(ph_gb_word_nosil) + return ph_gb_word_nosil, new_wav_align_fn + + def load_spk_map(self, base_dir): + spk_map_fn = f"{base_dir}/spk_map.json" + spk_map = json.load(open(spk_map_fn, 'r')) + return spk_map + + def load_dict(self, base_dir): + ph_encoder = build_token_encoder(f'{base_dir}/phone_set.json') + word_encoder = build_token_encoder(f'{base_dir}/word_set.json') + return ph_encoder, word_encoder + + @property + def meta_csv_filename(self): + return 'metadata' + + @property + def wav_processed_dirname(self): + return 'wav_processed' \ No newline at end of file diff --git a/NeuralSeq/data_gen/tts/binarizer_zh.py b/NeuralSeq/data_gen/tts/binarizer_zh.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd424a1a669ecf1a74cb69d690a690d0d39fe55 --- /dev/null +++ b/NeuralSeq/data_gen/tts/binarizer_zh.py @@ -0,0 +1,59 @@ +import os + +os.environ["OMP_NUM_THREADS"] = "1" + +from data_gen.tts.txt_processors.zh_g2pM import ALL_SHENMU +from data_gen.tts.base_binarizer import BaseBinarizer, BinarizationError +from data_gen.tts.data_gen_utils import get_mel2ph +from utils.hparams import set_hparams, hparams +import numpy as np + + +class ZhBinarizer(BaseBinarizer): + @staticmethod + def get_align(tg_fn, ph, mel, phone_encoded, res): + if tg_fn is not None and os.path.exists(tg_fn): + _, dur = get_mel2ph(tg_fn, ph, mel, hparams) + else: + raise BinarizationError(f"Align not found") + ph_list = ph.split(" ") + assert len(dur) == len(ph_list) + mel2ph = [] + # 分隔符的时长分配给韵母 + dur_cumsum = np.pad(np.cumsum(dur), [1, 0], mode='constant', constant_values=0) + for i in range(len(dur)): + p = ph_list[i] + if p[0] != '<' and not p[0].isalpha(): + uv_ = res['f0'][dur_cumsum[i]:dur_cumsum[i + 1]] == 0 + j = 0 + while j < len(uv_) and not uv_[j]: + j += 1 + dur[i - 1] += j + dur[i] -= j + if dur[i] < 100: + dur[i - 1] += dur[i] + dur[i] = 0 + # 声母和韵母等长 + for i in range(len(dur)): + p = ph_list[i] + if p in ALL_SHENMU: + p_next = ph_list[i + 1] + if not (dur[i] > 0 and p_next[0].isalpha() and p_next not in ALL_SHENMU): + print(f"assert dur[i] > 0 and p_next[0].isalpha() and p_next not in ALL_SHENMU, " + f"dur[i]: {dur[i]}, p: {p}, p_next: {p_next}.") + continue + total = dur[i + 1] + dur[i] + dur[i] = total // 2 + dur[i + 1] = total - dur[i] + for i in range(len(dur)): + mel2ph += [i + 1] * dur[i] + mel2ph = np.array(mel2ph) + if mel2ph.max() - 1 >= len(phone_encoded): + raise BinarizationError(f"| Align does not match: {(mel2ph.max() - 1, len(phone_encoded))}") + res['mel2ph'] = mel2ph + res['dur'] = dur + + +if __name__ == "__main__": + set_hparams() + ZhBinarizer().process() diff --git a/NeuralSeq/data_gen/tts/data_gen_utils.py b/NeuralSeq/data_gen/tts/data_gen_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..57ccb7c0200de5124908db2cba0347baf2663adc --- /dev/null +++ b/NeuralSeq/data_gen/tts/data_gen_utils.py @@ -0,0 +1,357 @@ +import warnings + +warnings.filterwarnings("ignore") + +import parselmouth +import os +import torch +from skimage.transform import resize +from utils.text_encoder import TokenTextEncoder +from utils.pitch_utils import f0_to_coarse +import struct +import webrtcvad +from scipy.ndimage.morphology import binary_dilation +import librosa +import numpy as np +from utils import audio +import pyloudnorm as pyln +import re +import json +from collections import OrderedDict + +PUNCS = '!,.?;:' + +int16_max = (2 ** 15) - 1 + + +def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12): + """ + Ensures that segments without voice in the waveform remain no longer than a + threshold determined by the VAD parameters in params.py. + :param wav: the raw waveform as a numpy array of floats + :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have. + :return: the same waveform with silences trimmed away (length <= original wav length) + """ + + ## Voice Activation Detection + # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. + # This sets the granularity of the VAD. Should not need to be changed. + sampling_rate = 16000 + wav_raw, sr = librosa.core.load(path, sr=sr) + + if norm: + meter = pyln.Meter(sr) # create BS.1770 meter + loudness = meter.integrated_loudness(wav_raw) + wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0) + if np.abs(wav_raw).max() > 1.0: + wav_raw = wav_raw / np.abs(wav_raw).max() + + wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best') + + vad_window_length = 30 # In milliseconds + # Number of frames to average together when performing the moving average smoothing. + # The larger this value, the larger the VAD variations must be to not get smoothed out. + vad_moving_average_width = 8 + + # Compute the voice detection window size + samples_per_window = (vad_window_length * sampling_rate) // 1000 + + # Trim the end of the audio to have a multiple of the window size + wav = wav[:len(wav) - (len(wav) % samples_per_window)] + + # Convert the float waveform to 16-bit mono PCM + pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) + + # Perform voice activation detection + voice_flags = [] + vad = webrtcvad.Vad(mode=3) + for window_start in range(0, len(wav), samples_per_window): + window_end = window_start + samples_per_window + voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], + sample_rate=sampling_rate)) + voice_flags = np.array(voice_flags) + + # Smooth the voice detection with a moving average + def moving_average(array, width): + array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) + ret = np.cumsum(array_padded, dtype=float) + ret[width:] = ret[width:] - ret[:-width] + return ret[width - 1:] / width + + audio_mask = moving_average(voice_flags, vad_moving_average_width) + audio_mask = np.round(audio_mask).astype(np.bool) + + # Dilate the voiced regions + audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) + audio_mask = np.repeat(audio_mask, samples_per_window) + audio_mask = resize(audio_mask, (len(wav_raw),)) > 0 + if return_raw_wav: + return wav_raw, audio_mask, sr + return wav_raw[audio_mask], audio_mask, sr + + +def process_utterance(wav_path, + fft_size=1024, + hop_size=256, + win_length=1024, + window="hann", + num_mels=80, + fmin=80, + fmax=7600, + eps=1e-6, + sample_rate=22050, + loud_norm=False, + min_level_db=-100, + return_linear=False, + trim_long_sil=False, vocoder='pwg'): + if isinstance(wav_path, str): + if trim_long_sil: + wav, _, _ = trim_long_silences(wav_path, sample_rate) + else: + wav, _ = librosa.core.load(wav_path, sr=sample_rate) + else: + wav = wav_path + + if loud_norm: + meter = pyln.Meter(sample_rate) # create BS.1770 meter + loudness = meter.integrated_loudness(wav) + wav = pyln.normalize.loudness(wav, loudness, -22.0) + if np.abs(wav).max() > 1: + wav = wav / np.abs(wav).max() + + # get amplitude spectrogram + x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size, + win_length=win_length, window=window, pad_mode="constant") + spc = np.abs(x_stft) # (n_bins, T) + + # get mel basis + fmin = 0 if fmin == -1 else fmin + fmax = sample_rate / 2 if fmax == -1 else fmax + mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax) + mel = mel_basis @ spc + + if vocoder == 'pwg': + mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T) + else: + assert False, f'"{vocoder}" is not in ["pwg"].' + + l_pad, r_pad = audio.librosa_pad_lr(wav, fft_size, hop_size, 1) + wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0) + wav = wav[:mel.shape[1] * hop_size] + + if not return_linear: + return wav, mel + else: + spc = audio.amp_to_db(spc) + spc = audio.normalize(spc, {'min_level_db': min_level_db}) + return wav, mel, spc + + +def get_pitch(wav_data, mel, hparams): + """ + + :param wav_data: [T] + :param mel: [T, 80] + :param hparams: + :return: + """ + time_step = hparams['hop_size'] / hparams['audio_sample_rate'] * 1000 + f0_min = 80 + f0_max = 750 + + if hparams['hop_size'] == 128: + pad_size = 4 + elif hparams['hop_size'] == 256: + pad_size = 2 + else: + assert False + + f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac( + time_step=time_step / 1000, voicing_threshold=0.6, + pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] + lpad = pad_size * 2 + rpad = len(mel) - len(f0) - lpad + f0 = np.pad(f0, [[lpad, rpad]], mode='constant') + # mel and f0 are extracted by 2 different libraries. we should force them to have the same length. + # Attention: we find that new version of some libraries could cause ``rpad'' to be a negetive value... + # Just to be sure, we recommend users to set up the same environments as them in requirements_auto.txt (by Anaconda) + delta_l = len(mel) - len(f0) + assert np.abs(delta_l) <= 8 + if delta_l > 0: + f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0) + f0 = f0[:len(mel)] + pitch_coarse = f0_to_coarse(f0) + return f0, pitch_coarse + + +def remove_empty_lines(text): + """remove empty lines""" + assert (len(text) > 0) + assert (isinstance(text, list)) + text = [t.strip() for t in text] + if "" in text: + text.remove("") + return text + + +class TextGrid(object): + def __init__(self, text): + text = remove_empty_lines(text) + self.text = text + self.line_count = 0 + self._get_type() + self._get_time_intval() + self._get_size() + self.tier_list = [] + self._get_item_list() + + def _extract_pattern(self, pattern, inc): + """ + Parameters + ---------- + pattern : regex to extract pattern + inc : increment of line count after extraction + Returns + ------- + group : extracted info + """ + try: + group = re.match(pattern, self.text[self.line_count]).group(1) + self.line_count += inc + except AttributeError: + raise ValueError("File format error at line %d:%s" % (self.line_count, self.text[self.line_count])) + return group + + def _get_type(self): + self.file_type = self._extract_pattern(r"File type = \"(.*)\"", 2) + + def _get_time_intval(self): + self.xmin = self._extract_pattern(r"xmin = (.*)", 1) + self.xmax = self._extract_pattern(r"xmax = (.*)", 2) + + def _get_size(self): + self.size = int(self._extract_pattern(r"size = (.*)", 2)) + + def _get_item_list(self): + """Only supports IntervalTier currently""" + for itemIdx in range(1, self.size + 1): + tier = OrderedDict() + item_list = [] + tier_idx = self._extract_pattern(r"item \[(.*)\]:", 1) + tier_class = self._extract_pattern(r"class = \"(.*)\"", 1) + if tier_class != "IntervalTier": + raise NotImplementedError("Only IntervalTier class is supported currently") + tier_name = self._extract_pattern(r"name = \"(.*)\"", 1) + tier_xmin = self._extract_pattern(r"xmin = (.*)", 1) + tier_xmax = self._extract_pattern(r"xmax = (.*)", 1) + tier_size = self._extract_pattern(r"intervals: size = (.*)", 1) + for i in range(int(tier_size)): + item = OrderedDict() + item["idx"] = self._extract_pattern(r"intervals \[(.*)\]", 1) + item["xmin"] = self._extract_pattern(r"xmin = (.*)", 1) + item["xmax"] = self._extract_pattern(r"xmax = (.*)", 1) + item["text"] = self._extract_pattern(r"text = \"(.*)\"", 1) + item_list.append(item) + tier["idx"] = tier_idx + tier["class"] = tier_class + tier["name"] = tier_name + tier["xmin"] = tier_xmin + tier["xmax"] = tier_xmax + tier["size"] = tier_size + tier["items"] = item_list + self.tier_list.append(tier) + + def toJson(self): + _json = OrderedDict() + _json["file_type"] = self.file_type + _json["xmin"] = self.xmin + _json["xmax"] = self.xmax + _json["size"] = self.size + _json["tiers"] = self.tier_list + return json.dumps(_json, ensure_ascii=False, indent=2) + + +def get_mel2ph(tg_fn, ph, mel, hparams): + ph_list = ph.split(" ") + with open(tg_fn, "r") as f: + tg = f.readlines() + tg = remove_empty_lines(tg) + tg = TextGrid(tg) + tg = json.loads(tg.toJson()) + split = np.ones(len(ph_list) + 1, np.float) * -1 + tg_idx = 0 + ph_idx = 0 + tg_align = [x for x in tg['tiers'][-1]['items']] + tg_align_ = [] + for x in tg_align: + x['xmin'] = float(x['xmin']) + x['xmax'] = float(x['xmax']) + if x['text'] in ['sil', 'sp', '', 'SIL', 'PUNC']: + x['text'] = '' + if len(tg_align_) > 0 and tg_align_[-1]['text'] == '': + tg_align_[-1]['xmax'] = x['xmax'] + continue + tg_align_.append(x) + tg_align = tg_align_ + tg_len = len([x for x in tg_align if x['text'] != '']) + ph_len = len([x for x in ph_list if not is_sil_phoneme(x)]) + assert tg_len == ph_len, (tg_len, ph_len, tg_align, ph_list, tg_fn) + while tg_idx < len(tg_align) or ph_idx < len(ph_list): + if tg_idx == len(tg_align) and is_sil_phoneme(ph_list[ph_idx]): + split[ph_idx] = 1e8 + ph_idx += 1 + continue + x = tg_align[tg_idx] + if x['text'] == '' and ph_idx == len(ph_list): + tg_idx += 1 + continue + assert ph_idx < len(ph_list), (tg_len, ph_len, tg_align, ph_list, tg_fn) + ph = ph_list[ph_idx] + if x['text'] == '' and not is_sil_phoneme(ph): + assert False, (ph_list, tg_align) + if x['text'] != '' and is_sil_phoneme(ph): + ph_idx += 1 + else: + assert (x['text'] == '' and is_sil_phoneme(ph)) \ + or x['text'].lower() == ph.lower() \ + or x['text'].lower() == 'sil', (x['text'], ph) + split[ph_idx] = x['xmin'] + if ph_idx > 0 and split[ph_idx - 1] == -1 and is_sil_phoneme(ph_list[ph_idx - 1]): + split[ph_idx - 1] = split[ph_idx] + ph_idx += 1 + tg_idx += 1 + assert tg_idx == len(tg_align), (tg_idx, [x['text'] for x in tg_align]) + assert ph_idx >= len(ph_list) - 1, (ph_idx, ph_list, len(ph_list), [x['text'] for x in tg_align], tg_fn) + mel2ph = np.zeros([mel.shape[0]], np.int) + split[0] = 0 + split[-1] = 1e8 + for i in range(len(split) - 1): + assert split[i] != -1 and split[i] <= split[i + 1], (split[:-1],) + split = [int(s * hparams['audio_sample_rate'] / hparams['hop_size'] + 0.5) for s in split] + for ph_idx in range(len(ph_list)): + mel2ph[split[ph_idx]:split[ph_idx + 1]] = ph_idx + 1 + mel2ph_torch = torch.from_numpy(mel2ph) + T_t = len(ph_list) + dur = mel2ph_torch.new_zeros([T_t + 1]).scatter_add(0, mel2ph_torch, torch.ones_like(mel2ph_torch)) + dur = dur[1:].numpy() + return mel2ph, dur + + +def build_phone_encoder(data_dir): + phone_list_file = os.path.join(data_dir, 'phone_set.json') + phone_list = json.load(open(phone_list_file)) + return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') + + +def build_word_encoder(data_dir): + word_list_file = os.path.join(data_dir, 'word_set.json') + word_list = json.load(open(word_list_file)) + return TokenTextEncoder(None, vocab_list=word_list, replace_oov=',') + +def is_sil_phoneme(p): + return not p[0].isalpha() + + +def build_token_encoder(token_list_file): + token_list = json.load(open(token_list_file)) + return TokenTextEncoder(None, vocab_list=token_list, replace_oov='') diff --git a/NeuralSeq/data_gen/tts/emotion/__pycache__/audio.cpython-38.pyc b/NeuralSeq/data_gen/tts/emotion/__pycache__/audio.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..43f7ae5d1841b4e6b80652d46ef9b85920319179 Binary files /dev/null and b/NeuralSeq/data_gen/tts/emotion/__pycache__/audio.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/emotion/__pycache__/inference.cpython-38.pyc b/NeuralSeq/data_gen/tts/emotion/__pycache__/inference.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..39fd3fcfce30afa3e8cacd24ed124a0e64300e86 Binary files /dev/null and b/NeuralSeq/data_gen/tts/emotion/__pycache__/inference.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/emotion/__pycache__/model.cpython-38.pyc b/NeuralSeq/data_gen/tts/emotion/__pycache__/model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..80dce017ae64c9265602e206e3f5f429df0b27e2 Binary files /dev/null and b/NeuralSeq/data_gen/tts/emotion/__pycache__/model.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/emotion/__pycache__/params_data.cpython-38.pyc b/NeuralSeq/data_gen/tts/emotion/__pycache__/params_data.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b5eb696abf3900b3b6af1b996dc9be6e1cd37188 Binary files /dev/null and b/NeuralSeq/data_gen/tts/emotion/__pycache__/params_data.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/emotion/__pycache__/params_model.cpython-38.pyc b/NeuralSeq/data_gen/tts/emotion/__pycache__/params_model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d7b679b2370b42002c5aade745dbce07af9de027 Binary files /dev/null and b/NeuralSeq/data_gen/tts/emotion/__pycache__/params_model.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/emotion/audio.py b/NeuralSeq/data_gen/tts/emotion/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..2a5eed96f08995c4f7e872a0f28a02bf0f0ad4ab --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/audio.py @@ -0,0 +1,107 @@ +from scipy.ndimage.morphology import binary_dilation +from data_gen.tts.emotion.params_data import * +from pathlib import Path +from typing import Optional, Union +import numpy as np +import webrtcvad +import librosa +import struct + +int16_max = (2 ** 15) - 1 + + +def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], + source_sr: Optional[int] = None): + """ + Applies the preprocessing operations used in training the Speaker Encoder to a waveform + either on disk or in memory. The waveform will be resampled to match the data hyperparameters. + + :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not + just .wav), either the waveform as a numpy array of floats. + :param source_sr: if passing an audio waveform, the sampling rate of the waveform before + preprocessing. After preprocessing, the waveform's sampling rate will match the data + hyperparameters. If passing a filepath, the sampling rate will be automatically detected and + this argument will be ignored. + """ + # Load the wav from disk if needed + if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): + wav, source_sr = librosa.load(str(fpath_or_wav), sr=None) + else: + wav = fpath_or_wav + + # Resample the wav if needed + if source_sr is not None and source_sr != sampling_rate: + wav = librosa.resample(wav, source_sr, sampling_rate) + + # Apply the preprocessing: normalize volume and shorten long silences + wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True) + wav = trim_long_silences(wav) + + return wav + + +def wav_to_mel_spectrogram(wav): + """ + Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform. + Note: this not a log-mel spectrogram. + """ + frames = librosa.feature.melspectrogram( + wav, + sampling_rate, + n_fft=int(sampling_rate * mel_window_length / 1000), + hop_length=int(sampling_rate * mel_window_step / 1000), + n_mels=mel_n_channels + ) + return frames.astype(np.float32).T + + +def trim_long_silences(wav): + """ + Ensures that segments without voice in the waveform remain no longer than a + threshold determined by the VAD parameters in params.py. + + :param wav: the raw waveform as a numpy array of floats + :return: the same waveform with silences trimmed away (length <= original wav length) + """ + # Compute the voice detection window size + samples_per_window = (vad_window_length * sampling_rate) // 1000 + + # Trim the end of the audio to have a multiple of the window size + wav = wav[:len(wav) - (len(wav) % samples_per_window)] + + # Convert the float waveform to 16-bit mono PCM + pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) + + # Perform voice activation detection + voice_flags = [] + vad = webrtcvad.Vad(mode=3) + for window_start in range(0, len(wav), samples_per_window): + window_end = window_start + samples_per_window + voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], + sample_rate=sampling_rate)) + voice_flags = np.array(voice_flags) + + # Smooth the voice detection with a moving average + def moving_average(array, width): + array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) + ret = np.cumsum(array_padded, dtype=float) + ret[width:] = ret[width:] - ret[:-width] + return ret[width - 1:] / width + + audio_mask = moving_average(voice_flags, vad_moving_average_width) + audio_mask = np.round(audio_mask).astype(np.bool) + + # Dilate the voiced regions + audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) + audio_mask = np.repeat(audio_mask, samples_per_window) + + return wav[audio_mask == True] + + +def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False): + if increase_only and decrease_only: + raise ValueError("Both increase only and decrease only are set") + dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2)) + if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only): + return wav + return wav * (10 ** (dBFS_change / 20)) diff --git a/NeuralSeq/data_gen/tts/emotion/inference.py b/NeuralSeq/data_gen/tts/emotion/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..0bda414e67e4f2a6e829930c85352a0a41a7f6d9 --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/inference.py @@ -0,0 +1,177 @@ +from data_gen.tts.emotion.params_data import * +from data_gen.tts.emotion.model import EmotionEncoder +from data_gen.tts.emotion.audio import preprocess_wav # We want to expose this function from here +from matplotlib import cm +from data_gen.tts.emotion import audio +from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +import torch + +_model = None # type: EmotionEncoder +_device = None # type: torch.device + + +def load_model(weights_fpath: Path, device=None): + """ + Loads the model in memory. If this function is not explicitely called, it will be run on the + first call to embed_frames() with the default weights file. + + :param weights_fpath: the path to saved model weights. + :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The + model will be loaded and will run on this device. Outputs will however always be on the cpu. + If None, will default to your GPU if it"s available, otherwise your CPU. + """ + # TODO: I think the slow loading of the encoder might have something to do with the device it + # was saved on. Worth investigating. + global _model, _device + if device is None: + _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + elif isinstance(device, str): + _device = torch.device(device) + _model = EmotionEncoder(_device, torch.device("cpu")) + checkpoint = torch.load(weights_fpath) + _model.load_state_dict(checkpoint["model_state"]) + _model.eval() + print("Loaded encoder trained to step %d" % (checkpoint["step"])) + + +def is_loaded(): + return _model is not None + + +def embed_frames_batch(frames_batch): + """ + Computes embeddings for a batch of mel spectrogram. + + :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape + (batch_size, n_frames, n_channels) + :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size) + """ + if _model is None: + raise Exception("Model was not loaded. Call load_model() before inference.") + + frames = torch.from_numpy(frames_batch).to(_device) + embed = _model.inference(frames).detach().cpu().numpy() + return embed + + +def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames, + min_pad_coverage=0.75, overlap=0.5): + """ + Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain + partial utterances of each. Both the waveform and the mel + spectrogram slices are returned, so as to make each partial utterance waveform correspond to + its spectrogram. This function assumes that the mel spectrogram parameters used are those + defined in params_data.py. + + The returned ranges may be indexing further than the length of the waveform. It is + recommended that you pad the waveform with zeros up to wave_slices[-1].stop. + + :param n_samples: the number of samples in the waveform + :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial + utterance + :param min_pad_coverage: when reaching the last partial utterance, it may or may not have + enough frames. If at least of are present, + then the last partial utterance will be considered, as if we padded the audio. Otherwise, + it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial + utterance, this parameter is ignored so that the function always returns at least 1 slice. + :param overlap: by how much the partial utterance should overlap. If set to 0, the partial + utterances are entirely disjoint. + :return: the waveform slices and mel spectrogram slices as lists of array slices. Index + respectively the waveform and the mel spectrogram with these slices to obtain the partial + utterances. + """ + assert 0 <= overlap < 1 + assert 0 < min_pad_coverage <= 1 + + samples_per_frame = int((sampling_rate * mel_window_step / 1000)) + n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) + frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1) + + # Compute the slices + wav_slices, mel_slices = [], [] + steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1) + for i in range(0, steps, frame_step): + mel_range = np.array([i, i + partial_utterance_n_frames]) + wav_range = mel_range * samples_per_frame + mel_slices.append(slice(*mel_range)) + wav_slices.append(slice(*wav_range)) + + # Evaluate whether extra padding is warranted or not + last_wav_range = wav_slices[-1] + coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) + if coverage < min_pad_coverage and len(mel_slices) > 1: + mel_slices = mel_slices[:-1] + wav_slices = wav_slices[:-1] + + return wav_slices, mel_slices + + +def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs): + """ + Computes an embedding for a single utterance. + + # TODO: handle multiple wavs to benefit from batching on GPU + :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32 + :param using_partials: if True, then the utterance is split in partial utterances of + frames and the utterance embedding is computed from their + normalized average. If False, the utterance is instead computed from feeding the entire + spectogram to the network. + :param return_partials: if True, the partial embeddings will also be returned along with the + wav slices that correspond to the partial embeddings. + :param kwargs: additional arguments to compute_partial_splits() + :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If + is True, the partial utterances as a numpy array of float32 of shape + (n_partials, model_embedding_size) and the wav partials as a list of slices will also be + returned. If is simultaneously set to False, both these values will be None + instead. + """ + # Process the entire utterance if not using partials + if not using_partials: + frames = audio.wav_to_mel_spectrogram(wav) + embed = embed_frames_batch(frames[None, ...])[0] + if return_partials: + return embed, None, None + return embed + + # Compute where to split the utterance into partials and pad if necessary + wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs) + max_wave_length = wave_slices[-1].stop + if max_wave_length >= len(wav): + wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") + + # Split the utterance into partials + frames = audio.wav_to_mel_spectrogram(wav) + frames_batch = np.array([frames[s] for s in mel_slices]) + partial_embeds = embed_frames_batch(frames_batch) + + # Compute the utterance embedding from the partial embeddings + raw_embed = np.mean(partial_embeds, axis=0) + embed = raw_embed / np.linalg.norm(raw_embed, 2) + + if return_partials: + return embed, partial_embeds, wave_slices + return embed + + +def embed_speaker(wavs, **kwargs): + raise NotImplemented() + + +def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)): + if ax is None: + ax = plt.gca() + + if shape is None: + height = int(np.sqrt(len(embed))) + shape = (height, -1) + embed = embed.reshape(shape) + + cmap = cm.get_cmap() + mappable = ax.imshow(embed, cmap=cmap) + cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04) + cbar.set_clim(*color_range) + + ax.set_xticks([]), ax.set_yticks([]) + ax.set_title(title) diff --git a/NeuralSeq/data_gen/tts/emotion/model.py b/NeuralSeq/data_gen/tts/emotion/model.py new file mode 100644 index 0000000000000000000000000000000000000000..63dbae5a5f38d370466536646a55defd6df7de3d --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/model.py @@ -0,0 +1,78 @@ + +from data_gen.tts.emotion.params_model import * +from data_gen.tts.emotion.params_data import * +from torch.nn.utils import clip_grad_norm_ +from scipy.optimize import brentq +from torch import nn +import numpy as np +import torch + + +class EmotionEncoder(nn.Module): + def __init__(self, device, loss_device): + super().__init__() + self.loss_device = loss_device + + # Network defition + self.lstm = nn.LSTM(input_size=mel_n_channels, + hidden_size=model_hidden_size, + num_layers=model_num_layers, + batch_first=True).to(device) + self.linear = nn.Linear(in_features=model_hidden_size, + out_features=model_embedding_size).to(device) + self.relu = torch.nn.ReLU().to(device) + + + # Cosine similarity scaling (with fixed initial parameter values) + self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device) + self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device) + + # Loss + self.loss_fn = nn.CrossEntropyLoss().to(loss_device) + + def do_gradient_ops(self): + # Gradient scale + self.similarity_weight.grad *= 0.01 + self.similarity_bias.grad *= 0.01 + + # Gradient clipping + clip_grad_norm_(self.parameters(), 3, norm_type=2) + + def forward(self, utterances, hidden_init=None): + """ + Computes the embeddings of a batch of utterance spectrograms. + + :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape + (batch_size, n_frames, n_channels) + :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, + batch_size, hidden_size). Will default to a tensor of zeros if None. + :return: the embeddings as a tensor of shape (batch_size, embedding_size) + """ + # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state + # and the final cell state. + out, (hidden, cell) = self.lstm(utterances, hidden_init) + + # We take only the hidden state of the last layer + embeds_raw = self.relu(self.linear(hidden[-1])) + + # L2-normalize it + embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) + + return embeds + + def inference(self, utterances, hidden_init=None): + """ + Computes the embeddings of a batch of utterance spectrograms. + + :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape + (batch_size, n_frames, n_channels) + :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, + batch_size, hidden_size). Will default to a tensor of zeros if None. + :return: the embeddings as a tensor of shape (batch_size, embedding_size) + """ + # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state + # and the final cell state. + + out, (hidden, cell) = self.lstm(utterances, hidden_init) + + return hidden[-1] \ No newline at end of file diff --git a/NeuralSeq/data_gen/tts/emotion/params_data.py b/NeuralSeq/data_gen/tts/emotion/params_data.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb1716ed45617f2b127a7fb8885afe6cc74fb71 --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/params_data.py @@ -0,0 +1,29 @@ + +## Mel-filterbank +mel_window_length = 25 # In milliseconds +mel_window_step = 10 # In milliseconds +mel_n_channels = 40 + + +## Audio +sampling_rate = 16000 +# Number of spectrogram frames in a partial utterance +partials_n_frames = 160 # 1600 ms +# Number of spectrogram frames at inference +inference_n_frames = 80 # 800 ms + + +## Voice Activation Detection +# Window size of the VAD. Must be either 10, 20 or 30 milliseconds. +# This sets the granularity of the VAD. Should not need to be changed. +vad_window_length = 30 # In milliseconds +# Number of frames to average together when performing the moving average smoothing. +# The larger this value, the larger the VAD variations must be to not get smoothed out. +vad_moving_average_width = 8 +# Maximum number of consecutive silent frames a segment can have. +vad_max_silence_length = 6 + + +## Audio volume normalization +audio_norm_target_dBFS = -30 + diff --git a/NeuralSeq/data_gen/tts/emotion/params_model.py b/NeuralSeq/data_gen/tts/emotion/params_model.py new file mode 100644 index 0000000000000000000000000000000000000000..48f8e564e772649e3207c7a90bff1bee9e6b3a47 --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/params_model.py @@ -0,0 +1,11 @@ + +## Model parameters +model_hidden_size = 256 +model_embedding_size = 256 +model_num_layers = 3 + + +## Training parameters +learning_rate_init = 1e-4 +speakers_per_batch = 6 +utterances_per_speaker = 20 diff --git a/NeuralSeq/data_gen/tts/emotion/test_emotion.py b/NeuralSeq/data_gen/tts/emotion/test_emotion.py new file mode 100644 index 0000000000000000000000000000000000000000..97d702e2b426e0e9e8fb821a8f91183cf7631224 --- /dev/null +++ b/NeuralSeq/data_gen/tts/emotion/test_emotion.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Run inference for pre-processed data with a trained model. +""" + +import logging +import math +import numpy, math, pdb, sys, random +import time, os, itertools, shutil, importlib +import argparse +import os +import sys +import glob +from sklearn import metrics +import soundfile as sf +#import sentencepiece as spm +import torch +import inference as encoder +import torch.nn as nn +import torch.nn.functional as F +from pathlib import Path +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +from resemblyzer import VoiceEncoder, preprocess_wav + + +def tuneThresholdfromScore(scores, labels, target_fa, target_fr=None): + fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=1) + fnr = 1 - tpr + + fnr = fnr * 100 + fpr = fpr * 100 + + tunedThreshold = []; + if target_fr: + for tfr in target_fr: + idx = numpy.nanargmin(numpy.absolute((tfr - fnr))) + tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]]); + + for tfa in target_fa: + idx = numpy.nanargmin(numpy.absolute((tfa - fpr))) # numpy.where(fpr<=tfa)[0][-1] + tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]]); + + idxE = numpy.nanargmin(numpy.absolute((fnr - fpr))) + eer = max(fpr[idxE], fnr[idxE]) + + return (tunedThreshold, eer, fpr, fnr); + + +def loadWAV(filename, max_frames, evalmode=True, num_eval=10): + # Maximum audio length + max_audio = max_frames * 160 + 240 + + # Read wav file and convert to torch tensor + audio,sample_rate = sf.read(filename) + + feats_v0 = torch.from_numpy(audio).float() + audiosize = audio.shape[0] + + if audiosize <= max_audio: + shortage = math.floor((max_audio - audiosize + 1) / 2) + audio = numpy.pad(audio, (shortage, shortage), 'constant', constant_values=0) + audiosize = audio.shape[0] + + if evalmode: + startframe = numpy.linspace(0, audiosize - max_audio, num=num_eval) + else: + startframe = numpy.array([numpy.int64(random.random() * (audiosize - max_audio))]) + feats = [] + if evalmode and max_frames == 0: + feats.append(audio) + else: + for asf in startframe: + feats.append(audio[int(asf):int(asf) + max_audio]) + feat = numpy.stack(feats, axis=0) + feat = torch.FloatTensor(feat) + return feat; + +def evaluateFromList(listfilename, print_interval=100, test_path='', multi=False): + + lines = [] + files = [] + feats = {} + tstart = time.time() + + ## Read all lines + with open(listfilename) as listfile: + while True: + line = listfile.readline(); + if (not line): + break; + + data = line.split(); + + ## Append random label if missing + if len(data) == 2: data = [random.randint(0,1)] + data + + files.append(data[1]) + files.append(data[2]) + lines.append(line) + + setfiles = list(set(files)) + setfiles.sort() + ## Save all features to file + for idx, file in enumerate(setfiles): + # preprocessed_wav = encoder.preprocess_wav(os.path.join(test_path,file)) + # embed = encoder.embed_utterance(preprocessed_wav) + processed_wav = preprocess_wav(os.path.join(test_path,file)) + embed = voice_encoder.embed_utterance(processed_wav) + + torch.cuda.empty_cache() + ref_feat = torch.from_numpy(embed).unsqueeze(0) + + feats[file] = ref_feat + + telapsed = time.time() - tstart + + if idx % print_interval == 0: + sys.stdout.write("\rReading %d of %d: %.2f Hz, embedding size %d"%(idx,len(setfiles),idx/telapsed,ref_feat.size()[1])); + + print('') + all_scores = []; + all_labels = []; + all_trials = []; + tstart = time.time() + + ## Read files and compute all scores + for idx, line in enumerate(lines): + + data = line.split(); + ## Append random label if missing + if len(data) == 2: data = [random.randint(0,1)] + data + + ref_feat = feats[data[1]] + com_feat = feats[data[2]] + ref_feat = ref_feat.cuda() + com_feat = com_feat.cuda() + # normalize feats + ref_feat = F.normalize(ref_feat, p=2, dim=1) + com_feat = F.normalize(com_feat, p=2, dim=1) + + dist = F.pairwise_distance(ref_feat.unsqueeze(-1), com_feat.unsqueeze(-1)).detach().cpu().numpy(); + + score = -1 * numpy.mean(dist); + + all_scores.append(score); + all_labels.append(int(data[0])); + all_trials.append(data[1]+" "+data[2]) + + if idx % print_interval == 0: + telapsed = time.time() - tstart + sys.stdout.write("\rComputing %d of %d: %.2f Hz"%(idx,len(lines),idx/telapsed)); + sys.stdout.flush(); + + print('\n') + + return (all_scores, all_labels, all_trials); + + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser("baseline") + parser.add_argument("--data_root", type=str, help="", required=True) + parser.add_argument("--list", type=str, help="", required=True) + parser.add_argument("--model_dir", type=str, help="model parameters for AudioEncoder", required=True) + + args = parser.parse_args() + + + # Load the models one by one. + print("Preparing the encoder...") + # encoder.load_model(Path(args.model_dir)) + print("Insert the wav file name...") + voice_encoder = VoiceEncoder().cuda() + + sc, lab, trials = evaluateFromList(args.list, print_interval=100, test_path=args.data_root) + result = tuneThresholdfromScore(sc, lab, [1, 0.1]); + print('EER %2.4f'%result[1]) diff --git a/NeuralSeq/data_gen/tts/txt_processors/__init__.py b/NeuralSeq/data_gen/tts/txt_processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7bff3e9af7d634363116c6605f22a52aad614dea --- /dev/null +++ b/NeuralSeq/data_gen/tts/txt_processors/__init__.py @@ -0,0 +1 @@ +from . import en \ No newline at end of file diff --git a/NeuralSeq/data_gen/tts/txt_processors/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dfadb1d8e3c9b4f1fa86618338afee2a7f226414 Binary files /dev/null and b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/__init__.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/txt_processors/__pycache__/base_text_processor.cpython-38.pyc b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/base_text_processor.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f10e03f97d5e3287538681ef1a113f3b3a8f21b9 Binary files /dev/null and b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/base_text_processor.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/txt_processors/__pycache__/en.cpython-38.pyc b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/en.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40d91e96ee4417bef57c5922ad9f5f359c71bd54 Binary files /dev/null and b/NeuralSeq/data_gen/tts/txt_processors/__pycache__/en.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/txt_processors/base_text_processor.py b/NeuralSeq/data_gen/tts/txt_processors/base_text_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..69d51201dcb191c1c208ae1c87a34b5c97e6307f --- /dev/null +++ b/NeuralSeq/data_gen/tts/txt_processors/base_text_processor.py @@ -0,0 +1,47 @@ +from data_gen.tts.data_gen_utils import is_sil_phoneme + +REGISTERED_TEXT_PROCESSORS = {} + +def register_txt_processors(name): + def _f(cls): + REGISTERED_TEXT_PROCESSORS[name] = cls + return cls + + return _f + + +def get_txt_processor_cls(name): + return REGISTERED_TEXT_PROCESSORS.get(name, None) + + +class BaseTxtProcessor: + @staticmethod + def sp_phonemes(): + return ['|'] + + @classmethod + def process(cls, txt, preprocess_args): + raise NotImplementedError + + @classmethod + def postprocess(cls, txt_struct, preprocess_args): + # remove sil phoneme in head and tail + while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[0][0]): + txt_struct = txt_struct[1:] + while len(txt_struct) > 0 and is_sil_phoneme(txt_struct[-1][0]): + txt_struct = txt_struct[:-1] + if preprocess_args['with_phsep']: + txt_struct = cls.add_bdr(txt_struct) + if preprocess_args['add_eos_bos']: + txt_struct = [["", [""]]] + txt_struct + [["", [""]]] + return txt_struct + + @classmethod + def add_bdr(cls, txt_struct): + txt_struct_ = [] + for i, ts in enumerate(txt_struct): + txt_struct_.append(ts) + if i != len(txt_struct) - 1 and \ + not is_sil_phoneme(txt_struct[i][0]) and not is_sil_phoneme(txt_struct[i + 1][0]): + txt_struct_.append(['|', ['|']]) + return txt_struct_ \ No newline at end of file diff --git a/NeuralSeq/data_gen/tts/txt_processors/en.py b/NeuralSeq/data_gen/tts/txt_processors/en.py new file mode 100644 index 0000000000000000000000000000000000000000..6f755d5ab1f2cf4407daee08cc3639a05e941a97 --- /dev/null +++ b/NeuralSeq/data_gen/tts/txt_processors/en.py @@ -0,0 +1,77 @@ +import re +import unicodedata + +from g2p_en import G2p +from g2p_en.expand import normalize_numbers +from nltk import pos_tag +from nltk.tokenize import TweetTokenizer + +from data_gen.tts.txt_processors.base_text_processor import BaseTxtProcessor, register_txt_processors +from data_gen.tts.data_gen_utils import is_sil_phoneme, PUNCS + +class EnG2p(G2p): + word_tokenize = TweetTokenizer().tokenize + + def __call__(self, text): + # preprocessing + words = EnG2p.word_tokenize(text) + tokens = pos_tag(words) # tuples of (word, tag) + + # steps + prons = [] + for word, pos in tokens: + if re.search("[a-z]", word) is None: + pron = [word] + + elif word in self.homograph2features: # Check homograph + pron1, pron2, pos1 = self.homograph2features[word] + if pos.startswith(pos1): + pron = pron1 + else: + pron = pron2 + elif word in self.cmu: # lookup CMU dict + pron = self.cmu[word][0] + else: # predict for oov + pron = self.predict(word) + + prons.extend(pron) + prons.extend([" "]) + + return prons[:-1] + + +@register_txt_processors('en') +class TxtProcessor(BaseTxtProcessor): + g2p = EnG2p() + + @staticmethod + def preprocess_text(text): + text = normalize_numbers(text) + text = ''.join(char for char in unicodedata.normalize('NFD', text) + if unicodedata.category(char) != 'Mn') # Strip accents + text = text.lower() + text = re.sub("[\'\"()]+", "", text) + text = re.sub("[-]+", " ", text) + text = re.sub(f"[^ a-z{PUNCS}]", "", text) + text = re.sub(f" ?([{PUNCS}]) ?", r"\1", text) # !! -> ! + text = re.sub(f"([{PUNCS}])+", r"\1", text) # !! -> ! + text = text.replace("i.e.", "that is") + text = text.replace("i.e.", "that is") + text = text.replace("etc.", "etc") + text = re.sub(f"([{PUNCS}])", r" \1 ", text) + text = re.sub(rf"\s+", r" ", text) + return text + + @classmethod + def process(cls, txt, preprocess_args): + txt = cls.preprocess_text(txt).strip() + phs = cls.g2p(txt) + txt_struct = [[w, []] for w in txt.split(" ")] + i_word = 0 + for p in phs: + if p == ' ': + i_word += 1 + else: + txt_struct[i_word][1].append(p) + txt_struct = cls.postprocess(txt_struct, preprocess_args) + return txt_struct, txt \ No newline at end of file diff --git a/NeuralSeq/data_gen/tts/txt_processors/zh.py b/NeuralSeq/data_gen/tts/txt_processors/zh.py new file mode 100644 index 0000000000000000000000000000000000000000..ab155ed172dd1fd4e0d8802ce482a84bf897ce60 --- /dev/null +++ b/NeuralSeq/data_gen/tts/txt_processors/zh.py @@ -0,0 +1,43 @@ +import re +import jieba +from pypinyin import pinyin, Style +from data_gen.tts.data_gen_utils import PUNCS +from data_gen.tts.txt_processors.base_text_processor import BaseTxtProcessor +from utils.text_norm import NSWNormalizer + + +class TxtProcessor(BaseTxtProcessor): + table = {ord(f): ord(t) for f, t in zip( + u'：，。！？【】（）％＃＠＆１２３４５６７８９０', + u':,.!?[]()%#@&1234567890')} + + @staticmethod + def preprocess_text(text): + text = text.translate(TxtProcessor.table) + text = NSWNormalizer(text).normalize(remove_punc=False) + text = re.sub("[\'\"()]+", "", text) + text = re.sub("[-]+", " ", text) + text = re.sub(f"[^ A-Za-z\u4e00-\u9fff{PUNCS}]", "", text) + text = re.sub(f"([{PUNCS}])+", r"\1", text) # !! -> ! + text = re.sub(f"([{PUNCS}])", r" \1 ", text) + text = re.sub(rf"\s+", r"", text) + text = re.sub(rf"[A-Za-z]+", r"$", text) + return text + + @classmethod + def process(cls, txt, pre_align_args): + txt = cls.preprocess_text(txt) + shengmu = pinyin(txt, style=Style.INITIALS) # https://blog.csdn.net/zhoulei124/article/details/89055403 + yunmu_finals = pinyin(txt, style=Style.FINALS) + yunmu_tone3 = pinyin(txt, style=Style.FINALS_TONE3) + yunmu = [[t[0] + '5'] if t[0] == f[0] else t for f, t in zip(yunmu_finals, yunmu_tone3)] \ + if pre_align_args['use_tone'] else yunmu_finals + + assert len(shengmu) == len(yunmu) + phs = ["|"] + for a, b, c in zip(shengmu, yunmu, yunmu_finals): + if a[0] == c[0]: + phs += [a[0], "|"] + else: + phs += [a[0], b[0], "|"] + return phs, txt diff --git a/NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py b/NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py new file mode 100644 index 0000000000000000000000000000000000000000..a3dbc7f2feb4c1dc3bc2d745a260cd0a7922e140 --- /dev/null +++ b/NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py @@ -0,0 +1,72 @@ +import re +import jieba +from pypinyin import pinyin, Style +from data_gen.tts.data_gen_utils import PUNCS +from data_gen.tts.txt_processors import zh +from g2pM import G2pM + +ALL_SHENMU = ['zh', 'ch', 'sh', 'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'j', + 'q', 'x', 'r', 'z', 'c', 's', 'y', 'w'] +ALL_YUNMU = ['a', 'ai', 'an', 'ang', 'ao', 'e', 'ei', 'en', 'eng', 'er', 'i', 'ia', 'ian', + 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'iu', 'ng', 'o', 'ong', 'ou', + 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn'] + + +class TxtProcessor(zh.TxtProcessor): + model = G2pM() + + @staticmethod + def sp_phonemes(): + return ['|', '#'] + + @classmethod + def process(cls, txt, pre_align_args): + txt = cls.preprocess_text(txt) + ph_list = cls.model(txt, tone=pre_align_args['use_tone'], char_split=True) + seg_list = '#'.join(jieba.cut(txt)) + assert len(ph_list) == len([s for s in seg_list if s != '#']), (ph_list, seg_list) + + # 加入词边界'#' + ph_list_ = [] + seg_idx = 0 + for p in ph_list: + p = p.replace("u:", "v") + if seg_list[seg_idx] == '#': + ph_list_.append('#') + seg_idx += 1 + else: + ph_list_.append("|") + seg_idx += 1 + if re.findall('[\u4e00-\u9fff]', p): + if pre_align_args['use_tone']: + p = pinyin(p, style=Style.TONE3, strict=True)[0][0] + if p[-1] not in ['1', '2', '3', '4', '5']: + p = p + '5' + else: + p = pinyin(p, style=Style.NORMAL, strict=True)[0][0] + + finished = False + if len([c.isalpha() for c in p]) > 1: + for shenmu in ALL_SHENMU: + if p.startswith(shenmu) and not p.lstrip(shenmu).isnumeric(): + ph_list_ += [shenmu, p.lstrip(shenmu)] + finished = True + break + if not finished: + ph_list_.append(p) + + ph_list = ph_list_ + + # 去除静音符号周围的词边界标记 [..., '#', ',', '#', ...] + sil_phonemes = list(PUNCS) + TxtProcessor.sp_phonemes() + ph_list_ = [] + for i in range(0, len(ph_list), 1): + if ph_list[i] != '#' or (ph_list[i - 1] not in sil_phonemes and ph_list[i + 1] not in sil_phonemes): + ph_list_.append(ph_list[i]) + ph_list = ph_list_ + return ph_list, txt + + +if __name__ == '__main__': + phs, txt = TxtProcessor.process('他来到了，网易杭研大厦', {'use_tone': True}) + print(phs) diff --git a/NeuralSeq/data_gen/tts/wav_processors/__init__.py b/NeuralSeq/data_gen/tts/wav_processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4be97b377dcb95a0e6bceb876ac0ce93c8290249 --- /dev/null +++ b/NeuralSeq/data_gen/tts/wav_processors/__init__.py @@ -0,0 +1,2 @@ +from . import base_processor +from . import common_processors diff --git a/NeuralSeq/data_gen/tts/wav_processors/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8df69223969f0fd45fce9c20f5d340e99583e928 Binary files /dev/null and b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/__init__.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/wav_processors/__pycache__/base_processor.cpython-38.pyc b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/base_processor.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8530475f0849abfbdaec8f1a220f4f1e8e182094 Binary files /dev/null and b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/base_processor.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/wav_processors/__pycache__/common_processors.cpython-38.pyc b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/common_processors.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b8adea3b8093308a2d0e0b9f47c2c0f516fc67d8 Binary files /dev/null and b/NeuralSeq/data_gen/tts/wav_processors/__pycache__/common_processors.cpython-38.pyc differ diff --git a/NeuralSeq/data_gen/tts/wav_processors/base_processor.py b/NeuralSeq/data_gen/tts/wav_processors/base_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..e8200dc58a9388ac94a5ec34b8a65f75e380255b --- /dev/null +++ b/NeuralSeq/data_gen/tts/wav_processors/base_processor.py @@ -0,0 +1,25 @@ +REGISTERED_WAV_PROCESSORS = {} + + +def register_wav_processors(name): + def _f(cls): + REGISTERED_WAV_PROCESSORS[name] = cls + return cls + + return _f + + +def get_wav_processor_cls(name): + return REGISTERED_WAV_PROCESSORS.get(name, None) + + +class BaseWavProcessor: + @property + def name(self): + raise NotImplementedError + + def output_fn(self, input_fn): + return f'{input_fn[:-4]}_{self.name}.wav' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + raise NotImplementedError diff --git a/NeuralSeq/data_gen/tts/wav_processors/common_processors.py b/NeuralSeq/data_gen/tts/wav_processors/common_processors.py new file mode 100644 index 0000000000000000000000000000000000000000..8b0c62d5e1485ed9612b4452a656f0e837c2d693 --- /dev/null +++ b/NeuralSeq/data_gen/tts/wav_processors/common_processors.py @@ -0,0 +1,85 @@ +import os +import subprocess +import librosa +import numpy as np +from data_gen.tts.wav_processors.base_processor import BaseWavProcessor, register_wav_processors +from data_gen.tts.data_gen_utils import trim_long_silences +from utils.audio import save_wav, rnnoise +from utils.hparams import hparams + + +@register_wav_processors(name='sox_to_wav') +class ConvertToWavProcessor(BaseWavProcessor): + @property + def name(self): + return 'ToWav' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + if input_fn[-4:] == '.wav': + return input_fn, sr + else: + output_fn = self.output_fn(input_fn) + subprocess.check_call(f'sox -v 0.95 "{input_fn}" -t wav "{output_fn}"', shell=True) + return output_fn, sr + + +@register_wav_processors(name='sox_resample') +class ResampleProcessor(BaseWavProcessor): + @property + def name(self): + return 'Resample' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + output_fn = self.output_fn(input_fn) + sr_file = librosa.core.get_samplerate(input_fn) + if sr != sr_file: + subprocess.check_call(f'sox -v 0.95 "{input_fn}" -r{sr} "{output_fn}"', shell=True) + y, _ = librosa.core.load(input_fn, sr=sr) + y, _ = librosa.effects.trim(y) + save_wav(y, output_fn, sr) + return output_fn, sr + else: + return input_fn, sr + + +@register_wav_processors(name='trim_sil') +class TrimSILProcessor(BaseWavProcessor): + @property + def name(self): + return 'TrimSIL' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + output_fn = self.output_fn(input_fn) + y, _ = librosa.core.load(input_fn, sr=sr) + y, _ = librosa.effects.trim(y) + save_wav(y, output_fn, sr) + return output_fn + + +@register_wav_processors(name='trim_all_sil') +class TrimAllSILProcessor(BaseWavProcessor): + @property + def name(self): + return 'TrimSIL' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + output_fn = self.output_fn(input_fn) + y, audio_mask, _ = trim_long_silences( + input_fn, vad_max_silence_length=preprocess_args.get('vad_max_silence_length', 12)) + save_wav(y, output_fn, sr) + if preprocess_args['save_sil_mask']: + os.makedirs(f'{processed_dir}/sil_mask', exist_ok=True) + np.save(f'{processed_dir}/sil_mask/{item_name}.npy', audio_mask) + return output_fn, sr + + +@register_wav_processors(name='denoise') +class DenoiseProcessor(BaseWavProcessor): + @property + def name(self): + return 'Denoise' + + def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): + output_fn = self.output_fn(input_fn) + rnnoise(input_fn, output_fn, out_sample_rate=sr) + return output_fn, sr diff --git a/NeuralSeq/egs/datasets/audio/emotion/base_text2mel.yaml b/NeuralSeq/egs/datasets/audio/emotion/base_text2mel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b10ae9c7e6ca6db81c75cd748280614911c640fa --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/emotion/base_text2mel.yaml @@ -0,0 +1,17 @@ +raw_data_dir: 'data/raw/ESD' +processed_data_dir: 'data/processed/emotion' +binary_data_dir: 'data/binary/emotion' +pre_align_cls: egs.datasets.audio.emotion.pre_align.EmoPreAlign +audio_sample_rate: 16000 +binarization_args: + shuffle: true +binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer +use_spk_id: true +test_num: 200 +num_spk: 10 +pitch_type: frame +min_frames: 128 +num_test_samples: 30 +mel_loss: "ssim:0.5|l1:0.5" +vocoder_ckpt: '' +use_emotion: true \ No newline at end of file diff --git a/NeuralSeq/egs/datasets/audio/emotion/pre_align.py b/NeuralSeq/egs/datasets/audio/emotion/pre_align.py new file mode 100644 index 0000000000000000000000000000000000000000..3b625295a118845c01a3677004070714d11c162b --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/emotion/pre_align.py @@ -0,0 +1,25 @@ +import os + +from data_gen.tts.base_preprocess import BasePreprocessor +import glob +import re + +class EmoPreAlign(BasePreprocessor): + + def meta_data(self): + spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020'] + pattern = re.compile('[\t\n ]+') + for spk in spks: + for line in open(f"{self.raw_data_dir}/{spk}/{spk}.txt", 'r'): # 打开文件 + line = re.sub(pattern, ' ', line) + if line == ' ': continue + split_ = line.split(' ') + txt = ' '.join(split_[1: -2]) + item_name = split_[0] + emotion = split_[-2] + wav_fn = f'{self.raw_data_dir}/{spk}/{emotion}/{item_name}.wav' + yield item_name, wav_fn, txt, spk, emotion + + +if __name__ == "__main__": + EmoPreAlign().process() diff --git a/NeuralSeq/egs/datasets/audio/libritts/__pycache__/pre_align.cpython-38.pyc b/NeuralSeq/egs/datasets/audio/libritts/__pycache__/pre_align.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eb11362f1f7dba53bda29be2d0905e167d066fdc Binary files /dev/null and b/NeuralSeq/egs/datasets/audio/libritts/__pycache__/pre_align.cpython-38.pyc differ diff --git a/NeuralSeq/egs/datasets/audio/libritts/base_text2mel.yaml b/NeuralSeq/egs/datasets/audio/libritts/base_text2mel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..85c389a45a7bc2be4927867d07fa881754814a2c --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/libritts/base_text2mel.yaml @@ -0,0 +1,14 @@ +raw_data_dir: 'data/raw/LibriTTS' +processed_data_dir: 'data/processed/libritts' +binary_data_dir: 'data/binary/libritts' +pre_align_cls: egs.datasets.audio.libritts.pre_align.LibrittsPreAlign +binarization_args: + shuffle: true +use_spk_id: true +test_num: 200 +num_spk: 2320 +pitch_type: frame +min_frames: 128 +num_test_samples: 30 +mel_loss: "ssim:0.5|l1:0.5" +vocoder_ckpt: '' \ No newline at end of file diff --git a/NeuralSeq/egs/datasets/audio/libritts/fs2.yaml b/NeuralSeq/egs/datasets/audio/libritts/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..95ae09730837aa10606f9771f41f40429b99d9ac --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/libritts/fs2.yaml @@ -0,0 +1,3 @@ +base_config: + - egs/egs_bases/tts/fs2.yaml + - ./base_text2mel.yaml diff --git a/NeuralSeq/egs/datasets/audio/libritts/pre_align.py b/NeuralSeq/egs/datasets/audio/libritts/pre_align.py new file mode 100644 index 0000000000000000000000000000000000000000..8f04d01361430a4ad6b02421ac4e20d797f31dc8 --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/libritts/pre_align.py @@ -0,0 +1,27 @@ +import os + +from data_gen.tts.base_preprocess import BasePreprocessor +import glob + + +class LibrittsPreAlign(BasePreprocessor): + def meta_data(self): + wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav')) + for wav_fn in wav_fns: + item_name = os.path.basename(wav_fn)[:-4] + txt_fn = f'{wav_fn[:-4]}.normalized.txt' + with open(txt_fn, 'r') as f: + txt = f.readlines() + f.close() + spk = item_name.split("_")[0] + # Example: + # + # 'item_name': '103_1241_000000_000001' + # 'wav_fn': 'LibriTTS/train-clean-100/103/1241/103_1241_000000_000001.wav' + # 'txt': 'matthew Cuthbert is surprised' + # 'spk_name': '103' + yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt[0], 'spk_name': spk} + + +if __name__ == "__main__": + LibrittsPreAlign().process() diff --git a/NeuralSeq/egs/datasets/audio/libritts/pwg.yaml b/NeuralSeq/egs/datasets/audio/libritts/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a0fd70869274c3f8a7ac02a9ca5d7e1202dc895d --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/libritts/pwg.yaml @@ -0,0 +1,8 @@ +base_config: egs/egs_bases/tts/vocoder/pwg.yaml +raw_data_dir: 'data/raw/LibriTTS' +processed_data_dir: 'data/processed/libritts' +binary_data_dir: 'data/binary/libritts_wav' +generator_params: + kernel_size: 5 +num_spk: 400 +max_samples: 20480 diff --git a/NeuralSeq/egs/datasets/audio/lj/base_mel2wav.yaml b/NeuralSeq/egs/datasets/audio/lj/base_mel2wav.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df4355bc38d0568c0b3acfa4f7fc040cef5995d6 --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/lj/base_mel2wav.yaml @@ -0,0 +1,5 @@ +raw_data_dir: 'data/raw/LJSpeech-1.1' +processed_data_dir: 'data/processed/ljspeech' +binary_data_dir: 'data/binary/ljspeech_wav' +binarization_args: + with_spk_embed: false \ No newline at end of file diff --git a/NeuralSeq/egs/datasets/audio/lj/preprocess.py b/NeuralSeq/egs/datasets/audio/lj/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d45c9aa855bb7ce40b5e8374547014350fa92b --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/lj/preprocess.py @@ -0,0 +1,9 @@ +from data_gen.tts.base_preprocess import BasePreprocessor + + +class LJPreprocess(BasePreprocessor): + def meta_data(self): + for l in open(f'{self.raw_data_dir}/metadata.csv').readlines(): + item_name, _, txt = l.strip().split("|") + wav_fn = f"{self.raw_data_dir}/wavs/{item_name}.wav" + yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt} diff --git a/NeuralSeq/egs/datasets/audio/lj/pwg.yaml b/NeuralSeq/egs/datasets/audio/lj/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e0c6dc6da4367bad9bafd1a7ba492a5cfb18a347 --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/lj/pwg.yaml @@ -0,0 +1,3 @@ +base_config: + - egs/egs_bases/tts/vocoder/pwg.yaml + - ./base_mel2wav.yaml \ No newline at end of file diff --git a/NeuralSeq/egs/datasets/audio/vctk/base_mel2wav.yaml b/NeuralSeq/egs/datasets/audio/vctk/base_mel2wav.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b5210a1361259d30449430f70849061d17a8e59f --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/vctk/base_mel2wav.yaml @@ -0,0 +1,3 @@ +raw_data_dir: 'data/raw/VCTK-Corpus' +processed_data_dir: 'data/processed/vctk' +binary_data_dir: 'data/binary/vctk_wav' diff --git a/NeuralSeq/egs/datasets/audio/vctk/fs2.yaml b/NeuralSeq/egs/datasets/audio/vctk/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..49bb983bd98caca74af0ea70414e71aa34334b38 --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/vctk/fs2.yaml @@ -0,0 +1,12 @@ +base_config: + - egs/egs_bases/tts/fs2.yaml +raw_data_dir: 'data/raw/VCTK-Corpus' +processed_data_dir: 'data/processed/vctk' +binary_data_dir: 'data/binary/vctk' +pre_align_cls: egs.datasets.audio.vctk.pre_align.VCTKPreAlign +use_spk_id: true +test_num: 200 +num_spk: 400 +binarization_args: + shuffle: true + trim_eos_bos: true \ No newline at end of file diff --git a/NeuralSeq/egs/datasets/audio/vctk/pre_align.py b/NeuralSeq/egs/datasets/audio/vctk/pre_align.py new file mode 100644 index 0000000000000000000000000000000000000000..a03b3e12af245fa603403432f4487c53e8b13eab --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/vctk/pre_align.py @@ -0,0 +1,22 @@ +import os + +from data_gen.tts.base_pre_align import BasePreAlign +import glob + + +class VCTKPreAlign(BasePreAlign): + def meta_data(self): + wav_fns = glob.glob(f'{self.raw_data_dir}/wav48/*/*.wav') + for wav_fn in wav_fns: + item_name = os.path.basename(wav_fn)[:-4] + spk = item_name.split("_")[0] + txt_fn = wav_fn.split("/") + txt_fn[-1] = f'{item_name}.txt' + txt_fn[-3] = f'txt' + txt_fn = "/".join(txt_fn) + if os.path.exists(txt_fn) and os.path.exists(wav_fn): + yield item_name, wav_fn, (self.load_txt, txt_fn), spk + + +if __name__ == "__main__": + VCTKPreAlign().process() diff --git a/NeuralSeq/egs/datasets/audio/vctk/pwg.yaml b/NeuralSeq/egs/datasets/audio/vctk/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8c7c557fee02b0a62881f36a2abe010ede0d5f39 --- /dev/null +++ b/NeuralSeq/egs/datasets/audio/vctk/pwg.yaml @@ -0,0 +1,6 @@ +base_config: + - egs/egs_bases/tts/vocoder/pwg.yaml + - ./base_mel2wav.yaml + +num_spk: 400 +max_samples: 20480 diff --git a/NeuralSeq/egs/egs_bases/config_base.yaml b/NeuralSeq/egs/egs_bases/config_base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..39240ceb8cd52e4c0a146aa579f1ea046c727c84 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/config_base.yaml @@ -0,0 +1,46 @@ +# task +binary_data_dir: '' +work_dir: '' # experiment directory. +infer: false # inference +amp: false +seed: 1234 +debug: false +save_codes: [] +# - configs +# - modules +# - tasks +# - utils +# - usr + +############# +# dataset +############# +ds_workers: 1 +test_num: 100 +endless_ds: false +sort_by_len: true + +######### +# train and eval +######### +print_nan_grads: false +load_ckpt: '' +save_best: true +num_ckpt_keep: 3 +clip_grad_norm: 0 +accumulate_grad_batches: 1 +tb_log_interval: 100 +num_sanity_val_steps: 5 # steps of validation at the beginning +check_val_every_n_epoch: 10 +val_check_interval: 2000 +valid_monitor_key: 'val_loss' +valid_monitor_mode: 'min' +max_epochs: 1000 +max_updates: 1000000 +max_tokens: 31250 +max_sentences: 100000 +max_valid_tokens: -1 +max_valid_sentences: -1 +test_input_dir: '' +resume_from_checkpoint: 0 +rename_tmux: true \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/base.yaml b/NeuralSeq/egs/egs_bases/svs/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ec306951586ae23506de9edfdc24b05279ff2f12 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/base.yaml @@ -0,0 +1,24 @@ +task_cls: tasks.svs.task.DiffFsTask +pitch_type: frame +timesteps: 100 +dilation_cycle_length: 1 +residual_layers: 20 +residual_channels: 256 +lr: 0.001 +decay_steps: 50000 +keep_bins: 80 +spec_min: [ ] +spec_max: [ ] + +content_cond_steps: [ ] # [ 0, 10000 ] +spk_cond_steps: [ ] # [ 0, 10000 ] +# train and eval +fs2_ckpt: '' +max_updates: 400000 +# max_updates: 200000 +use_gt_dur: true +use_gt_f0: true +gen_tgt_spk_id: -1 +max_sentences: 48 +num_sanity_val_steps: 1 +num_valid_plots: 1 diff --git a/NeuralSeq/egs/egs_bases/svs/lj_ds_beta6.yaml b/NeuralSeq/egs/egs_bases/svs/lj_ds_beta6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7fd443c9843fb582283e75d44935e0152e2b4cb7 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/lj_ds_beta6.yaml @@ -0,0 +1,43 @@ +base_config: + - configs/tts/lj/fs2.yaml + - ./base.yaml +# spec_min and spec_max are calculated on the training set. +spec_min: [ -4.7574, -4.6783, -4.6431, -4.5832, -4.5390, -4.6771, -4.8089, -4.7672, + -4.5784, -4.7755, -4.7150, -4.8919, -4.8271, -4.7389, -4.6047, -4.7759, + -4.6799, -4.8201, -4.7823, -4.8262, -4.7857, -4.7545, -4.9358, -4.9733, + -5.1134, -5.1395, -4.9016, -4.8434, -5.0189, -4.8460, -5.0529, -4.9510, + -5.0217, -5.0049, -5.1831, -5.1445, -5.1015, -5.0281, -4.9887, -4.9916, + -4.9785, -4.9071, -4.9488, -5.0342, -4.9332, -5.0650, -4.8924, -5.0875, + -5.0483, -5.0848, -5.1809, -5.0677, -5.0015, -5.0792, -5.0636, -5.2413, + -5.1421, -5.1710, -5.3256, -5.0511, -5.1186, -5.0057, -5.0446, -5.1173, + -5.0325, -5.1085, -5.0053, -5.0755, -5.1176, -5.1004, -5.2153, -5.2757, + -5.3025, -5.2867, -5.2918, -5.3328, -5.2731, -5.2985, -5.2400, -5.2211 ] +spec_max: [ -0.5982, -0.0778, 0.1205, 0.2747, 0.4657, 0.5123, 0.5684, 0.7093, + 0.6461, 0.6420, 0.7316, 0.7715, 0.7681, 0.8349, 0.7815, 0.7591, + 0.7910, 0.7433, 0.7352, 0.6869, 0.6854, 0.6623, 0.5353, 0.6492, + 0.6909, 0.6106, 0.5761, 0.5936, 0.5638, 0.4054, 0.4545, 0.3589, + 0.3037, 0.3380, 0.1599, 0.2433, 0.2741, 0.2130, 0.1569, 0.1911, + 0.2324, 0.1586, 0.1221, 0.0341, -0.0558, 0.0553, -0.1153, -0.0933, + -0.1171, -0.0050, -0.1519, -0.1629, -0.0522, -0.0739, -0.2069, -0.2405, + -0.1244, -0.2116, -0.1361, -0.1575, -0.1442, 0.0513, -0.1567, -0.2000, + 0.0086, -0.0698, 0.1385, 0.0941, 0.1864, 0.1225, 0.2176, 0.2566, + 0.1670, 0.1007, 0.1444, 0.0888, 0.1998, 0.2414, 0.2932, 0.3047 ] + +task_cls: tasks.svs.diffspeech_task.DiffSpeechTask +vocoder: vocoders.hifigan.HifiGAN +vocoder_ckpt: checkpoints/0414_hifi_lj_1 +num_valid_plots: 10 +use_gt_dur: false +use_gt_f0: false +pitch_type: cwt +pitch_extractor: 'parselmouth' +max_updates: 160000 +lr: 0.001 +timesteps: 100 +K_step: 71 +diff_loss_type: l1 +diff_decoder_type: 'wavenet' +schedule_type: 'linear' +max_beta: 0.06 +fs2_ckpt: checkpoints/fs2_lj_1/model_ckpt_steps_150000.ckpt +save_gt: true \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/aux_rel.yaml b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/aux_rel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5c204cba8e70679c48301af9fd2302427a2be0dd --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/aux_rel.yaml @@ -0,0 +1,63 @@ +base_config: + - configs/singing/fs2.yaml + - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml + +audio_sample_rate: 24000 +hop_size: 128 # Hop size. +fft_size: 512 # FFT size. +win_size: 512 # FFT size. +fmin: 30 +fmax: 12000 +min_level_db: -120 + +binarization_args: + with_wav: true + with_spk_embed: false + with_align: true +raw_data_dir: 'data/raw/opencpop/segments' +processed_data_dir: 'xxx' +binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer + + +binary_data_dir: 'data/binary/opencpop-midi-dp' +use_midi: true # for midi exp +use_gt_f0: false # for midi exp +use_gt_dur: false # for further midi exp +lambda_f0: 1.0 +lambda_uv: 1.0 +#lambda_energy: 0.1 +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +pe_enable: false +pe_ckpt: '' + +num_spk: 1 +test_prefixes: [ + '2044', + '2086', + '2092', + '2093', + '2100', +] + +task_cls: tasks.svs.diffsinger_task.AuxDecoderMIDITask +#vocoder: tasks.svs.singingvocoder.highgan.HighGAN +#vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl +vocoder: vocoders.hifigan.HifiGAN +vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 + +use_nsf: true + +# config for experiments +max_frames: 5000 +max_tokens: 40000 +predictor_layers: 5 +rel_pos: true +dur_predictor_layers: 5 # * + +use_spk_embed: false +num_valid_plots: 10 +max_updates: 160000 +save_gt: true \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..820a608456d199b77d021d5a771f0a076a5a4cfa --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml @@ -0,0 +1,34 @@ +base_config: + - egs/egs_bases/svs/popcs_ds_beta6.yaml + - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml + +binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer +binary_data_dir: 'data/binary/opencpop-midi-dp' + +#switch_midi2f0_step: 174000 +use_midi: true # for midi exp +use_gt_f0: false # for midi exp +use_gt_dur: false # for further midi exp +lambda_f0: 1.0 +lambda_uv: 1.0 +#lambda_energy: 0.1 +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +pe_enable: false +pe_ckpt: '' + +fs2_ckpt: 'checkpoints/0302_opencpop_fs_midi/model_ckpt_steps_160000.ckpt' # +#num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask + +K_step: 60 +max_tokens: 36000 +predictor_layers: 5 +dilation_cycle_length: 4 # * +rel_pos: true +dur_predictor_layers: 5 # * +max_updates: 160000 +gaussian_start: false +mask_uv_prob: 0.15 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bfe5cc939a46372bf4086d680d38c6737e1b7d81 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml @@ -0,0 +1,41 @@ +spec_min: [-6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., -6., + -6., -6., -6., -6., -6., -6., -6., -6.] +spec_max: [-7.9453e-01, -8.1116e-01, -6.1631e-01, -3.0679e-01, -1.3863e-01, + -5.0652e-02, -1.1563e-01, -1.0679e-01, -9.1068e-02, -6.2174e-02, + -7.5302e-02, -7.2217e-02, -6.3815e-02, -7.3299e-02, 7.3610e-03, + -7.2508e-02, -5.0234e-02, -1.6534e-01, -2.6928e-01, -2.0782e-01, + -2.0823e-01, -1.1702e-01, -7.0128e-02, -6.5868e-02, -1.2675e-02, + 1.5121e-03, -8.9902e-02, -2.1392e-01, -2.3789e-01, -2.8922e-01, + -3.0405e-01, -2.3029e-01, -2.2088e-01, -2.1542e-01, -2.9367e-01, + -3.0137e-01, -3.8281e-01, -4.3590e-01, -2.8681e-01, -4.6855e-01, + -5.7485e-01, -4.7022e-01, -5.4266e-01, -4.4848e-01, -6.4120e-01, + -6.8700e-01, -6.4860e-01, -7.6436e-01, -4.9971e-01, -7.1068e-01, + -6.9724e-01, -6.1487e-01, -5.5843e-01, -6.9773e-01, -5.7502e-01, + -7.0919e-01, -8.2431e-01, -8.4213e-01, -9.0431e-01, -8.2840e-01, + -7.7945e-01, -8.2758e-01, -8.7699e-01, -1.0532e+00, -1.0766e+00, + -1.1198e+00, -1.0185e+00, -9.8983e-01, -1.0001e+00, -1.0756e+00, + -1.0024e+00, -1.0304e+00, -1.0579e+00, -1.0188e+00, -1.0500e+00, + -1.0842e+00, -1.0923e+00, -1.1223e+00, -1.2381e+00, -1.6467e+00] + +mel_vmin: -6. #-6. +mel_vmax: 1.5 +wav2spec_eps: 1e-6 + +raw_data_dir: 'data/raw/opencpop/segments' +processed_data_dir: 'xxx' +binary_data_dir: 'data/binary/opencpop-midi-dp' +datasets: [ + 'opencpop', +] +test_prefixes: [ + '2044', + '2086', + '2092', + '2093', + '2100', +] diff --git a/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000-10dil.yaml b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000-10dil.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8825430a386b8748847939a1366e54f344b50e92 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000-10dil.yaml @@ -0,0 +1,38 @@ +base_config: + - egs/egs_bases/svs/popcs_ds_beta6.yaml + - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml + +binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer +binary_data_dir: 'data/binary/opencpop-midi-dp' + +#switch_midi2f0_step: 174000 +use_midi: true # for midi exp +use_gt_dur: false # for further midi exp +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +dur_predictor_layers: 5 # * + + +fs2_ckpt: '' # +#num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask + +timesteps: 1000 +K_step: 1000 +max_beta: 0.02 +max_tokens: 36000 +max_updates: 320000 +gaussian_start: True + +use_pitch_embed: false +use_gt_f0: false # for midi exp + +lambda_f0: 0. +lambda_uv: 0. +dilation_cycle_length: 10 # * +rel_pos: true +predictor_layers: 5 +pe_enable: true +pe_ckpt: 'checkpoints/0102_xiaoma_pe' diff --git a/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c942cda62b2da399b105b10f259dabe417c3ac7 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml @@ -0,0 +1,42 @@ +base_config: + - egs/egs_bases/svs/popcs_ds_beta6.yaml + - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml + +binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer +binary_data_dir: 'data/binary/opencpop-midi-dp' + +#switch_midi2f0_step: 174000 +use_midi: true # for midi exp +use_gt_dur: false # for further midi exp +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +dur_predictor_layers: 5 # * + + +fs2_ckpt: '' # +#num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask + +# for diffusion schedule +timesteps: 1000 +K_step: 1000 +max_beta: 0.02 +max_tokens: 36000 +max_updates: 320000 +gaussian_start: True +pndm_speedup: 10 + +use_pitch_embed: false +use_gt_f0: false # for midi exp + +lambda_f0: 0. +lambda_uv: 0. +dilation_cycle_length: 4 # * +rel_pos: true +predictor_layers: 5 +pe_enable: true +pe_ckpt: 'checkpoints/0102_xiaoma_pe' + + diff --git a/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3cdda09d091396b9513fd1f4d19099ece73d618f --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml @@ -0,0 +1,36 @@ +base_config: + - egs/egs_bases/svs/popcs_ds_beta6.yaml + - egs/egs_bases/svs/midi/cascade/opencs/opencpop_statis.yaml + +binarizer_cls: data_gen.singing.binarize.OpencpopBinarizer +binary_data_dir: 'data/binary/opencpop-midi-dp' + +#switch_midi2f0_step: 174000 +use_midi: true # for midi exp +use_gt_dur: false # for further midi exp +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +dur_predictor_layers: 5 # * + + +fs2_ckpt: '' # +#num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask + +K_step: 100 +max_tokens: 36000 +max_updates: 160000 +gaussian_start: True + +use_pitch_embed: false +use_gt_f0: false # for midi exp + +lambda_f0: 0. +lambda_uv: 0. +dilation_cycle_length: 4 # * +rel_pos: true +predictor_layers: 5 +pe_enable: true +pe_ckpt: 'checkpoints/0102_xiaoma_pe' diff --git a/NeuralSeq/egs/egs_bases/svs/midi/e2e/popcs/ds100_adj_rel.yaml b/NeuralSeq/egs/egs_bases/svs/midi/e2e/popcs/ds100_adj_rel.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b51b4f223e6833430457456c210807704aab03b2 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/e2e/popcs/ds100_adj_rel.yaml @@ -0,0 +1,36 @@ +base_config: + - egs/egs_bases/svs/popcs_ds_beta6.yaml + - egs/egs_bases/svs/midi/cascade/popcs/popcs_statis.yaml + +binarizer_cls: data_gen.singing.binarize.MidiSingingBinarizer +binary_data_dir: 'data/binary/popcs-midi-dp' + +#switch_midi2f0_step: 174000 +use_midi: true # for midi exp +use_gt_dur: false # for further midi exp +lambda_ph_dur: 1.0 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 +dur_predictor_layers: 5 # * + + +fs2_ckpt: '' # +#num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerMIDITask + +K_step: 100 +max_tokens: 40000 +max_updates: 160000 +gaussian_start: True + +use_pitch_embed: false +use_gt_f0: false # for midi exp + +lambda_f0: 0. +lambda_uv: 0. +dilation_cycle_length: 4 # * +rel_pos: true +predictor_layers: 5 +pe_enable: true +pe_ckpt: 'checkpoints/0102_xiaoma_pe' diff --git a/NeuralSeq/egs/egs_bases/svs/midi/pe.yaml b/NeuralSeq/egs/egs_bases/svs/midi/pe.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8321efe33939986744351b9144416a7c87e22965 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/midi/pe.yaml @@ -0,0 +1,24 @@ +base_config: + - configs/tts/lj/fs2.yaml + +max_frames: 8000 +audio_sample_rate: 24000 +hop_size: 128 # Hop size. +fft_size: 512 # FFT size. +win_size: 512 # FFT size. +fmin: 30 +fmax: 12000 +min_level_db: -120 + +binary_data_dir: 'xxx' + +pitch_type: frame +task_cls: tasks.tts.pe.PitchExtractionTask +pitch_extractor_conv_layers: 2 + + +# config for experiments +max_tokens: 20000 +use_spk_embed: false +num_valid_plots: 10 +max_updates: 60000 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6.yaml b/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..000c9415ebf61e922be184ae89ffec1eae65a639 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6.yaml @@ -0,0 +1,70 @@ +base_config: + - configs/tts/fs2.yaml + - configs/singing/base.yaml + - ./base.yaml + +audio_sample_rate: 24000 +hop_size: 128 # Hop size. +fft_size: 512 # FFT size. +win_size: 512 # FFT size. +fmin: 30 +fmax: 12000 +min_level_db: -120 + +binarization_args: + with_wav: true + with_spk_embed: false + with_align: true +raw_data_dir: 'data/raw/popcs' +processed_data_dir: 'data/processed/popcs' +binary_data_dir: 'data/binary/popcs-pmf0' +num_spk: 1 +datasets: [ + 'popcs', +] +test_prefixes: [ + 'popcs-说散就散', + 'popcs-隐形的翅膀', +] + +spec_min: [-6.8276, -7.0270, -6.8142, -7.1429, -7.6669, -7.6000, -7.1148, -6.9640, + -6.8414, -6.6596, -6.6880, -6.7439, -6.7986, -7.4940, -7.7845, -7.6586, + -6.9288, -6.7639, -6.9118, -6.8246, -6.7183, -7.1769, -6.9794, -7.4513, + -7.3422, -7.5623, -6.9610, -6.8158, -6.9595, -6.8403, -6.5688, -6.6356, + -7.0209, -6.5002, -6.7819, -6.5232, -6.6927, -6.5701, -6.5531, -6.7069, + -6.6462, -6.4523, -6.5954, -6.4264, -6.4487, -6.7070, -6.4025, -6.3042, + -6.4008, -6.3857, -6.3903, -6.3094, -6.2491, -6.3518, -6.3566, -6.4168, + -6.2481, -6.3624, -6.2858, -6.2575, -6.3638, -6.4520, -6.1835, -6.2754, + -6.1253, -6.1645, -6.0638, -6.1262, -6.0710, -6.1039, -6.4428, -6.1363, + -6.1054, -6.1252, -6.1797, -6.0235, -6.0758, -5.9453, -6.0213, -6.0446] +spec_max: [ 0.2645, 0.0583, -0.2344, -0.0184, 0.1227, 0.1533, 0.1103, 0.1212, + 0.2421, 0.1809, 0.2134, 0.3161, 0.3301, 0.3289, 0.2667, 0.2421, + 0.2581, 0.2600, 0.1394, 0.1907, 0.1082, 0.1474, 0.1680, 0.2550, + 0.1057, 0.0826, 0.0423, 0.1203, -0.0701, -0.0056, 0.0477, -0.0639, + -0.0272, -0.0728, -0.1648, -0.0855, -0.2652, -0.1998, -0.1547, -0.2167, + -0.4181, -0.5463, -0.4161, -0.4733, -0.6518, -0.5387, -0.4290, -0.4191, + -0.4151, -0.3042, -0.3810, -0.4160, -0.4496, -0.2847, -0.4676, -0.4658, + -0.4931, -0.4885, -0.5547, -0.5481, -0.6948, -0.7968, -0.8455, -0.8392, + -0.8770, -0.9520, -0.8749, -0.7297, -0.8374, -0.8667, -0.7157, -0.9035, + -0.9219, -0.8801, -0.9298, -0.9009, -0.9604, -1.0537, -1.0781, -1.3766] + +task_cls: tasks.svs.diffsinger_task.DiffSingerTask +#vocoder: tasks.svs.singingvocoder.highgan.HighGAN +#vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl +vocoder: vocoders.hifigan.HifiGAN +vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 + +pitch_extractor: 'parselmouth' +# config for experiments +use_spk_embed: false +num_valid_plots: 10 +max_updates: 160000 +lr: 0.001 +timesteps: 100 +K_step: 51 +diff_loss_type: l1 +diff_decoder_type: 'wavenet' +schedule_type: 'linear' +max_beta: 0.06 +fs2_ckpt: '' +use_nsf: true \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6_offline.yaml b/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6_offline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8628623e606b6ada6f7c5e8025ced36982fcb926 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/popcs_ds_beta6_offline.yaml @@ -0,0 +1,12 @@ +base_config: + - ./popcs_ds_beta6.yaml + +fs2_ckpt: checkpoints/popcs_fs2_pmf0_1230/model_ckpt_steps_160000.ckpt # to be infer +num_valid_plots: 0 +task_cls: tasks.svs.diffsinger_task.DiffSingerOfflineTask + +# tmp: +#pe_enable: true +#pe_ckpt: '' +vocoder: vocoders.hifigan.HifiGAN +vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/svs/popcs_fs2.yaml b/NeuralSeq/egs/egs_bases/svs/popcs_fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9f1a7a97f7eedd018c8d2ac93422c1ea899fb60 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/svs/popcs_fs2.yaml @@ -0,0 +1,44 @@ +base_config: + - configs/singing/fs2.yaml + +audio_sample_rate: 24000 +hop_size: 128 # Hop size. +fft_size: 512 # FFT size. +win_size: 512 # FFT size. +fmin: 30 +fmax: 12000 +min_level_db: -120 + +binarization_args: + with_wav: true + with_spk_embed: false + with_align: true +raw_data_dir: 'data/raw/popcs' +processed_data_dir: 'data/processed/popcs' +binary_data_dir: 'data/binary/popcs-pmf0' +num_spk: 1 +datasets: [ + 'popcs', +] +test_prefixes: [ + 'popcs-说散就散', + 'popcs-隐形的翅膀', +] + +task_cls: tasks.tts.fs2.FastSpeech2Task +#vocoder: tasks.svs.singingvocoder.highgan.HighGAN +#vocoder_ckpt: checkpoints/h_2_model/checkpoint-530000steps.pkl +vocoder: vocoders.hifigan.HifiGAN +vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128 +use_nsf: true + +# config for experiments +max_tokens: 18000 +use_spk_embed: false +num_valid_plots: 10 +max_updates: 160000 +save_gt: true + +# tmp: +#pe_enable: true +#pe_ckpt: '' \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/base.yaml b/NeuralSeq/egs/egs_bases/tts/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..255e4cce1bb52d58d24443bc277621d70732583f --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/base.yaml @@ -0,0 +1,112 @@ +# task +base_config: ../config_base.yaml +task_cls: '' +############# +# dataset +############# +raw_data_dir: '' +processed_data_dir: '' +binary_data_dir: '' +dict_dir: '' +pre_align_cls: '' +binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer +pre_align_args: + txt_processor: en + use_tone: true # for ZH + sox_resample: false + sox_to_wav: false + allow_no_txt: false + trim_sil: false + denoise: false +binarization_args: + shuffle: false + with_txt: true + with_wav: false + with_align: true + with_spk_embed: false + with_spk_id: true + with_f0: true + with_f0cwt: false + with_linear: false + with_word: true + trim_sil: false + trim_eos_bos: false + reset_phone_dict: true + reset_word_dict: true +word_size: 30000 +pitch_extractor: parselmouth + +loud_norm: false +endless_ds: true + +test_num: 100 +min_frames: 0 +max_frames: 1548 +frames_multiple: 1 +max_input_tokens: 1550 +audio_num_mel_bins: 80 +audio_sample_rate: 22050 +hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) +win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) +fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) +fmax: 7600 # To be increased/reduced depending on data. +fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter +min_level_db: -100 +ref_level_db: 20 +griffin_lim_iters: 60 +num_spk: 1 +mel_vmin: -6 +mel_vmax: 1.5 +ds_workers: 1 + +######### +# model +######### +dropout: 0.1 +enc_layers: 4 +dec_layers: 4 +hidden_size: 256 +num_heads: 2 +enc_ffn_kernel_size: 9 +dec_ffn_kernel_size: 9 +ffn_act: gelu +ffn_padding: 'SAME' +use_spk_id: false +use_split_spk_id: false +use_spk_embed: false + + +########### +# optimization +########### +lr: 2.0 +scheduler: rsqrt # rsqrt|none +warmup_updates: 8000 +optimizer_adam_beta1: 0.9 +optimizer_adam_beta2: 0.98 +weight_decay: 0 +clip_grad_norm: 1 +clip_grad_value: 0 + + +########### +# train and eval +########### +max_tokens: 30000 +max_sentences: 100000 +max_valid_sentences: 1 +max_valid_tokens: 60000 +valid_infer_interval: 10000 +train_set_name: 'train' +train_sets: '' +valid_set_name: 'valid' +test_set_name: 'test' +num_test_samples: 0 +num_valid_plots: 10 +test_ids: [ ] +vocoder_denoise_c: 0.0 +profile_infer: false +out_wav_norm: false +save_gt: true +save_f0: false +gen_dir_name: '' \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/base_zh.yaml b/NeuralSeq/egs/egs_bases/tts/base_zh.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e6400eebc67900e3bf7e71e4575762a6b5fdc3e --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/base_zh.yaml @@ -0,0 +1,6 @@ +base_config: ./base.yaml +preprocess_args: + txt_processor: zh + use_tone: true + +word_size: 3000 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/fs2.yaml b/NeuralSeq/egs/egs_bases/tts/fs2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c200a50ad2e04ff685bd30c7d5c3be69ff7cdf3f --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/fs2.yaml @@ -0,0 +1,102 @@ +base_config: ./base.yaml +task_cls: tasks.tts.fs2.FastSpeech2Task + +# model +hidden_size: 256 +dropout: 0.1 +encoder_type: fft # rel_fft|fft|tacotron|tacotron2|conformer +decoder_type: fft # fft|rnn|conv|conformer|wn + +# rnn enc/dec +encoder_K: 8 +decoder_rnn_dim: 0 # for rnn decoder, 0 -> hidden_size * 2 + +# fft enc/dec +use_pos_embed: true +dec_num_heads: 2 +dec_layers: 4 +ffn_hidden_size: 1024 +enc_ffn_kernel_size: 9 +dec_ffn_kernel_size: 9 + +# conv enc/dec +enc_dec_norm: ln +conv_use_pos: false +layers_in_block: 2 +enc_dilations: [ 1, 1, 1, 1 ] +enc_kernel_size: 5 +dec_dilations: [ 1, 1, 1, 1 ] # for conv decoder +dec_kernel_size: 5 +dur_loss: mse # huber|mol + +# duration +predictor_hidden: -1 +predictor_kernel: 5 +predictor_layers: 2 +dur_predictor_kernel: 3 +dur_predictor_layers: 2 +predictor_dropout: 0.5 + +# pitch and energy +pitch_norm: standard # standard|log +use_pitch_embed: true +pitch_type: frame # frame|ph|cwt +use_uv: true +cwt_hidden_size: 128 +cwt_layers: 2 +cwt_loss: l1 +cwt_add_f0_loss: false +cwt_std_scale: 0.8 + +pitch_ar: false +pitch_embed_type: 0 +pitch_loss: 'l1' # l1|l2|ssim +pitch_ssim_win: 11 +use_energy_embed: false + +# reference encoder and speaker embedding +use_ref_enc: false +use_var_enc: false +lambda_commit: 0.25 +var_enc_vq_codes: 64 +ref_norm_layer: bn +dec_inp_add_noise: false +sil_add_noise: false +ref_hidden_stride_kernel: + - 0,3,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size + - 0,3,5 + - 0,2,5 + - 0,2,5 + - 0,2,5 +pitch_enc_hidden_stride_kernel: + - 0,2,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size + - 0,2,5 + - 0,2,5 +dur_enc_hidden_stride_kernel: + - 0,2,3 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size + - 0,2,3 + - 0,1,3 + +# mel +mel_loss: l1:0.5|ssim:0.5 # l1|l2|gdl|ssim or l1:0.5|ssim:0.5 + +# loss lambda +lambda_f0: 1.0 +lambda_uv: 1.0 +lambda_energy: 0.1 +lambda_ph_dur: 0.1 +lambda_sent_dur: 1.0 +lambda_word_dur: 1.0 +predictor_grad: 0.1 + +# train and eval +pretrain_fs_ckpt: '' +warmup_updates: 2000 +max_tokens: 32000 +max_sentences: 100000 +max_valid_sentences: 1 +max_updates: 120000 +use_gt_dur: false +use_gt_f0: false +ds_workers: 2 +lr: 1.0 diff --git a/NeuralSeq/egs/egs_bases/tts/fs2_adv.yaml b/NeuralSeq/egs/egs_bases/tts/fs2_adv.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c316b85e370301172286fe938427950269c08dd7 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/fs2_adv.yaml @@ -0,0 +1,47 @@ +base_config: ./fs2.yaml +task_cls: tasks.tts.fs2_adv.FastSpeech2AdvTask + +disc_win_num: 3 +disc_interval: 1 +disc_reduction: stack # stack|sum|none +disc_start_steps: 0 +rerun_gen: false + +disc_norm: in +mel_disc_hidden_size: 128 + +# mel decoder +mel_gan: true +lambda_mel_adv: 0.1 +mel_hidden_size: 256 + +# others +dropout: 0.05 +pitch_embed_type: 0 +enc_ffn_kernel_size: 9 +dec_ffn_kernel_size: 9 +use_cond_disc: false + +optimizer_adam_beta1: 0.5 +optimizer_adam_beta2: 0.999 +generator_grad_norm: 5.0 # Generator's gradient norm. +disc_hidden_size: 128 +disc_lr: 0.0001 # Discriminator's learning rate. +discriminator_optimizer_params: + eps: 1.0e-6 # Discriminator's epsilon. + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + step_size: 60000 # Discriminator's scheduler step size. + gamma: 0.5 # D5iscriminator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +discriminator_grad_norm: 1 # Discriminator's gradient norm. + +max_updates: 400000 +max_tokens: 30000 +max_sentences: 80 +val_check_interval: 2000 + +gen_dir_name: '' +num_ckpt_keep: 2 +save_best: false + diff --git a/NeuralSeq/egs/egs_bases/tts/ps.yaml b/NeuralSeq/egs/egs_bases/tts/ps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..23e93bf94679b0a436f2d98d27d0e25893f32300 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/ps.yaml @@ -0,0 +1,63 @@ +base_config: ./fs2.yaml + +########################### +# models +########################### +# encoders +hidden_size: 192 +ffn_hidden_size: 768 +enc_ffn_kernel_size: 5 +enc_layers: 4 +dur_level: word +encoder_type: rel_fft +use_word_encoder: true + +# mix ling encoder +word_enc_layers: 4 +word_encoder_type: rel_fft +use_pitch_embed: false +enc_prenet: true +enc_pre_ln: true +text_encoder_postnet: true +dropout: 0.0 +add_word_pos: true + +# dur predictor +dur_predictor_layers: 3 +dur_predictor_kernel: 5 +predictor_dropout: 0.2 + +## fvae +use_fvae: true +latent_size: 16 +fvae_encoder_type: conv +fvae_decoder_type: conv +fvae_enc_dec_hidden: 192 +fvae_kernel_size: 5 +fvae_enc_n_layers: 8 +fvae_dec_n_layers: 4 +fvae_strides: 4 +fvae_noise_scale: 1.0 + +# prior flow +use_prior_flow: true +prior_flow_hidden: 64 +prior_flow_kernel_size: 3 +prior_flow_n_blocks: 4 + +########################### +# training and inference +########################### +lambda_kl: 1.0 +kl_min: 0.0 +lambda_sent_dur: 0.0 +kl_start_steps: 10000 +posterior_start_steps: 0 +frames_multiple: 4 +num_valid_plots: 10 +lr: 0.0002 +warmup_updates: 8000 +max_tokens: 40000 +valid_infer_interval: 10000 +max_sentences: 80 +max_updates: 480000 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/ps_flow.yaml b/NeuralSeq/egs/egs_bases/tts/ps_flow.yaml new file mode 100644 index 0000000000000000000000000000000000000000..36747ea5dcc065bdda68e79976d3f1827b5853f4 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/ps_flow.yaml @@ -0,0 +1,20 @@ +base_config: ./ps2.yaml +task_cls: tasks.tts.ps_flow.PortaSpeechFlowTask + +use_post_flow: true +detach_postflow_input: true +post_flow_lr: 0.001 +post_glow_hidden: 192 +post_glow_kernel_size: 3 +post_glow_n_blocks: 12 +post_glow_n_block_layers: 3 +post_share_cond_layers: false +share_wn_layers: 4 +use_cond_proj: false +use_latent_cond: false +use_txt_cond: true +sigmoid_scale: false +post_glow_training_start: 160000 +noise_scale: 0.8 +infer_post_glow: true +two_stage: true \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/ps_flow_small.yaml b/NeuralSeq/egs/egs_bases/tts/ps_flow_small.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e95a86492ee9c6516a05201bf4d0a2de6e55e947 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/ps_flow_small.yaml @@ -0,0 +1,42 @@ +base_config: ./ps_flow.yaml + +########################### +# models +########################### +# encoders +hidden_size: 128 +ffn_hidden_size: 512 +enc_ffn_kernel_size: 3 +enc_layers: 3 +word_enc_layers: 3 + +# dur predictor +dur_predictor_layers: 3 +dur_predictor_kernel: 5 +predictor_dropout: 0.2 + +## fvae +use_fvae: true +latent_size: 16 +fvae_encoder_type: wn +fvae_decoder_type: wn +fvae_enc_dec_hidden: 128 +fvae_kernel_size: 3 +fvae_enc_n_layers: 8 +fvae_dec_n_layers: 3 +fvae_strides: 4 +fvae_noise_scale: 1.0 + + +# prior flow +use_prior_flow: true +prior_flow_hidden: 32 +prior_flow_kernel_size: 3 +prior_flow_n_blocks: 3 +# post flow +post_glow_hidden: 128 +post_glow_kernel_size: 3 +post_glow_n_blocks: 8 +post_glow_n_block_layers: 3 +share_wn_layers: 4 +noise_scale: 0.6 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/vocoder/base.yaml b/NeuralSeq/egs/egs_bases/tts/vocoder/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92a1a74e269f5245af09a1bc00a5998f069b6801 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/vocoder/base.yaml @@ -0,0 +1,34 @@ +base_config: ../base.yaml +binarization_args: + with_wav: true + with_spk_embed: false + with_align: false + with_word: false + with_txt: false + +########### +# train and eval +########### +max_samples: 25600 +max_sentences: 5 +max_valid_sentences: 1 +max_updates: 1000000 +val_check_interval: 2000 + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +fft_size: 1024 # FFT size. +hop_size: 256 # Hop size. +win_length: null # Window length. +# If set to null, it will be the same as fft_size. +window: "hann" # Window function. +num_mels: 80 # Number of mel basis. +fmin: 80 # Minimum freq in mel basis calculation. +fmax: 7600 # Maximum frequency in mel basis calculation. +aux_context_window: 0 # Context window size for auxiliary feature. +use_pitch_embed: false + +generator_grad_norm: 10 # Generator's gradient norm. +discriminator_grad_norm: 1 # Discriminator's gradient norm. +disc_start_steps: 40000 # Number of steps to start to train discriminator. diff --git a/NeuralSeq/egs/egs_bases/tts/vocoder/hifigan.yaml b/NeuralSeq/egs/egs_bases/tts/vocoder/hifigan.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fb77ba5104bf9d5b7ce9282f626bf56e957fbfe8 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/vocoder/hifigan.yaml @@ -0,0 +1,28 @@ +base_config: ./base.yaml +task_cls: tasks.vocoder.hifigan.HifiGanTask +resblock: "1" +adam_b1: 0.8 +adam_b2: 0.99 +upsample_rates: [ 8,8,2,2 ] +upsample_kernel_sizes: [ 16,16,4,4 ] +upsample_initial_channel: 512 +resblock_kernel_sizes: [ 3,7,11 ] +resblock_dilation_sizes: [ [ 1,3,5 ], [ 1,3,5 ], [ 1,3,5 ] ] + +use_pitch_embed: false +use_fm_loss: false +use_ms_stft: false + +lambda_mel: 5.0 +lambda_mel_adv: 1.0 +lambda_cdisc: 4.0 +lambda_adv: 1.0 + +lr: 0.0002 # Generator's learning rate. +generator_scheduler_params: + step_size: 600 + gamma: 0.999 +discriminator_scheduler_params: + step_size: 600 + gamma: 0.999 +max_updates: 3000000 \ No newline at end of file diff --git a/NeuralSeq/egs/egs_bases/tts/vocoder/pwg.yaml b/NeuralSeq/egs/egs_bases/tts/vocoder/pwg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2d95bbd92abcdf70fb7a38b15877509390696fd2 --- /dev/null +++ b/NeuralSeq/egs/egs_bases/tts/vocoder/pwg.yaml @@ -0,0 +1,82 @@ +base_config: ./base.yaml +task_cls: tasks.vocoder.pwg.PwgTask + +aux_context_window: 2 # Context window size for auxiliary feature. +use_pitch_embed: false +########################################################### +# GENERATOR NETWORK ARCHITECTURE SETTING # +########################################################### +generator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Kernel size of dilated convolution. + layers: 30 # Number of residual block layers. + stacks: 3 # Number of stacks i.e., dilation cycles. + residual_channels: 64 # Number of channels in residual conv. + gate_channels: 128 # Number of channels in gated conv. + skip_channels: 64 # Number of channels in skip conv. + aux_channels: 80 # Number of channels for auxiliary feature conv. + # Must be the same as num_mels. + # If set to 2, previous 2 and future 2 frames will be considered. + dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. + upsample_params: # Upsampling network parameters. + upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. + use_pitch_embed: false + use_nsf: false +########################################################### +# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # +########################################################### +discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Number of output channels. + layers: 10 # Number of conv layers. + conv_channels: 64 # Number of chnn layers. + bias: true # Whether to use bias parameter in conv. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. + nonlinear_activation_params: # Nonlinear function parameters + negative_slope: 0.2 # Alpha in LeakyReLU. +rerun_gen: true + +########################################################### +# STFT LOSS SETTING # +########################################################### +stft_loss_params: + fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. + hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss + win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. + window: "hann_window" # Window function for STFT-based loss +use_mel_loss: false + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_adv: 4.0 # Loss balancing coefficient. + +########################################################### +# OPTIMIZER & SCHEDULER SETTING # +########################################################### +generator_optimizer_params: + lr: 0.0001 # Generator's learning rate. + eps: 1.0e-6 # Generator's epsilon. + weight_decay: 0.0 # Generator's weight decay coefficient. +generator_scheduler_params: + step_size: 200000 # Generator's scheduler step size. + gamma: 0.5 # Generator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +generator_grad_norm: 10 # Generator's gradient norm. +discriminator_optimizer_params: + lr: 0.00005 # Discriminator's learning rate. + eps: 1.0e-6 # Discriminator's epsilon. + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + step_size: 200000 # Discriminator's scheduler step size. + gamma: 0.5 # Discriminator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +discriminator_grad_norm: 1 # Discriminator's gradient norm. +disc_start_steps: 40000 # Number of steps to start to train discriminator. diff --git a/NeuralSeq/gitattributes b/NeuralSeq/gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..7fd1b7b544c9ec416883d1cae83fc8b0c4ea20b0 --- /dev/null +++ b/NeuralSeq/gitattributes @@ -0,0 +1,33 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text 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0000000000000000000000000000000000000000..39ed74f29f7526d5149e4f0079a3681a3bac2582 --- /dev/null +++ b/NeuralSeq/inference/svs/base_svs_infer.py @@ -0,0 +1,265 @@ +import os + +import torch +import numpy as np +from modules.hifigan.hifigan import HifiGanGenerator +from vocoders.hifigan import HifiGAN +from inference.svs.opencpop.map import cpop_pinyin2ph_func + +from utils import load_ckpt +from utils.hparams import set_hparams, hparams +from utils.text_encoder import TokenTextEncoder +from pypinyin import pinyin, lazy_pinyin, Style +import librosa +import glob +import re + + +class BaseSVSInfer: + def __init__(self, hparams, device=None): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.hparams = hparams + self.device = device + + phone_list = ["AP", "SP", "a", "ai", "an", "ang", "ao", "b", "c", "ch", "d", "e", "ei", "en", "eng", "er", "f", "g", + "h", "i", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong", "iu", "j", "k", "l", "m", "n", "o", + "ong", "ou", "p", "q", "r", "s", "sh", "t", "u", "ua", "uai", "uan", "uang", "ui", "un", "uo", "v", + "van", "ve", "vn", "w", "x", "y", "z", "zh"] + self.ph_encoder = TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') + self.pinyin2phs = cpop_pinyin2ph_func() + self.spk_map = {'opencpop': 0} + + self.model = self.build_model() + self.model.eval() + self.model.to(self.device) + self.vocoder = self.build_vocoder() + self.vocoder.eval() + self.vocoder.to(self.device) + + def build_model(self): + raise NotImplementedError + + def forward_model(self, inp): + raise NotImplementedError + + def build_vocoder(self): + base_dir = hparams['vocoder_ckpt'] + config_path = f'{base_dir}/config.yaml' + ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= + lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] + print('| load HifiGAN: ', ckpt) + ckpt_dict = torch.load(ckpt, map_location="cpu") + config = set_hparams(config_path, global_hparams=False) + state = ckpt_dict["state_dict"]["model_gen"] + vocoder = HifiGanGenerator(config) + vocoder.load_state_dict(state, strict=True) + vocoder.remove_weight_norm() + vocoder = vocoder.eval().to(self.device) + return vocoder + + def run_vocoder(self, c, **kwargs): + c = c.transpose(2, 1) # [B, 80, T] + f0 = kwargs.get('f0') # [B, T] + if f0 is not None and hparams.get('use_nsf'): + # f0 = torch.FloatTensor(f0).to(self.device) + y = self.vocoder(c, f0).view(-1) + else: + y = self.vocoder(c).view(-1) + # [T] + return y[None] + + def preprocess_word_level_input(self, inp): + # Pypinyin can't solve polyphonic words + text_raw = inp['text'].replace('最长', '最常').replace('长睫毛', '常睫毛') \ + .replace('那么长', '那么常').replace('多长', '多常') \ + .replace('很长', '很常') # We hope someone could provide a better g2p module for us by opening pull requests. + + # lyric + pinyins = lazy_pinyin(text_raw, strict=False) + ph_per_word_lst = [self.pinyin2phs[pinyin.strip()] for pinyin in pinyins if pinyin.strip() in self.pinyin2phs] + + # Note + note_per_word_lst = [x.strip() for x in inp['notes'].split('|') if x.strip() != ''] + mididur_per_word_lst = [x.strip() for x in inp['notes_duration'].split('|') if x.strip() != ''] + + if len(note_per_word_lst) == len(ph_per_word_lst) == len(mididur_per_word_lst): + print('Pass word-notes check.') + else: + print('The number of words does\'t match the number of notes\' windows. ', + 'You should split the note(s) for each word by | mark.') + print(ph_per_word_lst, note_per_word_lst, mididur_per_word_lst) + print(len(ph_per_word_lst), len(note_per_word_lst), len(mididur_per_word_lst)) + return None + + note_lst = [] + ph_lst = [] + midi_dur_lst = [] + is_slur = [] + for idx, ph_per_word in enumerate(ph_per_word_lst): + # for phs in one word: + # single ph like ['ai'] or multiple phs like ['n', 'i'] + ph_in_this_word = ph_per_word.split() + + # for notes in one word: + # single note like ['D4'] or multiple notes like ['D4', 'E4'] which means a 'slur' here. + note_in_this_word = note_per_word_lst[idx].split() + midi_dur_in_this_word = mididur_per_word_lst[idx].split() + # process for the model input + # Step 1. + # Deal with note of 'not slur' case or the first note of 'slur' case + # j ie + # F#4/Gb4 F#4/Gb4 + # 0 0 + for ph in ph_in_this_word: + ph_lst.append(ph) + note_lst.append(note_in_this_word[0]) + midi_dur_lst.append(midi_dur_in_this_word[0]) + is_slur.append(0) + # step 2. + # Deal with the 2nd, 3rd... notes of 'slur' case + # j ie ie + # F#4/Gb4 F#4/Gb4 C#4/Db4 + # 0 0 1 + if len(note_in_this_word) > 1: # is_slur = True, we should repeat the YUNMU to match the 2nd, 3rd... notes. + for idx in range(1, len(note_in_this_word)): + ph_lst.append(ph_in_this_word[-1]) + note_lst.append(note_in_this_word[idx]) + midi_dur_lst.append(midi_dur_in_this_word[idx]) + is_slur.append(1) + ph_seq = ' '.join(ph_lst) + + if len(ph_lst) == len(note_lst) == len(midi_dur_lst): + print(len(ph_lst), len(note_lst), len(midi_dur_lst)) + print('Pass word-notes check.') + else: + print('The number of words does\'t match the number of notes\' windows. ', + 'You should split the note(s) for each word by | mark.') + return None + return ph_seq, note_lst, midi_dur_lst, is_slur + + def preprocess_phoneme_level_input(self, inp): + ph_seq = inp['ph_seq'] + note_lst = inp['note_seq'].split() + midi_dur_lst = inp['note_dur_seq'].split() + is_slur = [float(x) for x in inp['is_slur_seq'].split()] + print(len(note_lst), len(ph_seq.split()), len(midi_dur_lst)) + if len(note_lst) == len(ph_seq.split()) == len(midi_dur_lst): + print('Pass word-notes check.') + else: + print('The number of words does\'t match the number of notes\' windows. ', + 'You should split the note(s) for each word by | mark.') + return None + return ph_seq, note_lst, midi_dur_lst, is_slur + + def preprocess_input(self, inp, input_type='word'): + """ + + :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} + :return: + """ + + item_name = inp.get('item_name', '') + spk_name = inp.get('spk_name', 'opencpop') + + # single spk + spk_id = self.spk_map[spk_name] + + # get ph seq, note lst, midi dur lst, is slur lst. + if input_type == 'word': + ret = self.preprocess_word_level_input(inp) + elif input_type == 'phoneme': # like transcriptions.txt in Opencpop dataset. + ret = self.preprocess_phoneme_level_input(inp) + else: + print('Invalid input type.') + return None + + if ret: + ph_seq, note_lst, midi_dur_lst, is_slur = ret + else: + print('==========> Preprocess_word_level or phone_level input wrong.') + return None + + # convert note lst to midi id; convert note dur lst to midi duration + try: + midis = [librosa.note_to_midi(x.split("/")[0]) if x != 'rest' else 0 + for x in note_lst] + midi_dur_lst = [float(x) for x in midi_dur_lst] + except Exception as e: + print(e) + print('Invalid Input Type.') + return None + + ph_token = self.ph_encoder.encode(ph_seq) + item = {'item_name': item_name, 'text': inp['text'], 'ph': ph_seq, 'spk_id': spk_id, + 'ph_token': ph_token, 'pitch_midi': np.asarray(midis), 'midi_dur': np.asarray(midi_dur_lst), + 'is_slur': np.asarray(is_slur), } + item['ph_len'] = len(item['ph_token']) + return item + + def input_to_batch(self, item): + item_names = [item['item_name']] + text = [item['text']] + ph = [item['ph']] + txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) + txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) + spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device) + + pitch_midi = torch.LongTensor(item['pitch_midi'])[None, :hparams['max_frames']].to(self.device) + midi_dur = torch.FloatTensor(item['midi_dur'])[None, :hparams['max_frames']].to(self.device) + is_slur = torch.LongTensor(item['is_slur'])[None, :hparams['max_frames']].to(self.device) + + batch = { + 'item_name': item_names, + 'text': text, + 'ph': ph, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'spk_ids': spk_ids, + 'pitch_midi': pitch_midi, + 'midi_dur': midi_dur, + 'is_slur': is_slur + } + return batch + + def postprocess_output(self, output): + return output + + def infer_once(self, inp): + inp = self.preprocess_input(inp, input_type=inp['input_type'] if inp.get('input_type') else 'word') + output = self.forward_model(inp) + output = self.postprocess_output(output) + return output + + @classmethod + def example_run(cls, inp): + from utils.audio import save_wav + set_hparams(print_hparams=False) + infer_ins = cls(hparams) + out = infer_ins.infer_once(inp) + os.makedirs('infer_out', exist_ok=True) + save_wav(out, f'infer_out/example_out.wav', hparams['audio_sample_rate']) + + +# if __name__ == '__main__': + # debug + # a = BaseSVSInfer(hparams) + # a.preprocess_input({'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP', + # 'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest', + # 'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590' + # }) + + # b = { + # 'text': '小酒窝长睫毛AP是你最美的记号', + # 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', + # 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340' + # } + # c = { + # 'text': '小酒窝长睫毛AP是你最美的记号', + # 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', + # 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', + # 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', + # 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0' + # } # input like Opencpop dataset. + # a.preprocess_input(b) + # a.preprocess_input(c, input_type='phoneme') \ No newline at end of file diff --git a/NeuralSeq/inference/svs/ds_cascade.py b/NeuralSeq/inference/svs/ds_cascade.py new file mode 100644 index 0000000000000000000000000000000000000000..f0ec5ede64d93ef1d182ec42ca36f1f1bb920360 --- /dev/null +++ b/NeuralSeq/inference/svs/ds_cascade.py @@ -0,0 +1,54 @@ +import torch +from inference.svs.base_svs_infer import BaseSVSInfer +from utils import load_ckpt +from utils.hparams import hparams +from modulesmodules.diff.shallow_diffusion_tts import GaussianDiffusion +from tasks.svs.diffsinger_task import DIFF_DECODERS + +class DiffSingerCascadeInfer(BaseSVSInfer): + def build_model(self): + model = GaussianDiffusion( + phone_encoder=self.ph_encoder, + out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + K_step=hparams['K_step'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + model.eval() + load_ckpt(model, hparams['work_dir'], 'model') + return model + + def forward_model(self, inp): + sample = self.input_to_batch(inp) + txt_tokens = sample['txt_tokens'] # [B, T_t] + spk_id = sample.get('spk_ids') + with torch.no_grad(): + output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True, + pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'], + is_slur=sample['is_slur']) + mel_out = output['mel_out'] # [B, T,80] + f0_pred = output['f0_denorm'] + wav_out = self.run_vocoder(mel_out, f0=f0_pred) + wav_out = wav_out.cpu().numpy() + return wav_out[0] + + +if __name__ == '__main__': + inp = { + 'text': '小酒窝长睫毛AP是你最美的记号', + 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', + 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', + 'input_type': 'word' + } # user input: Chinese characters + c = { + 'text': '小酒窝长睫毛AP是你最美的记号', + 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', + 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', + 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', + 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', + 'input_type': 'phoneme' + } # input like Opencpop dataset. + DiffSingerCascadeInfer.example_run(inp) + +# # CUDA_VISIBLE_DEVICES=1 python inference/svs/ds_cascade.py --config egs/egs_bases/svs/midi/cascade/opencs/ds60_rel.yaml --exp_name 0303_opencpop_ds58_midi \ No newline at end of file diff --git a/NeuralSeq/inference/svs/ds_e2e.py b/NeuralSeq/inference/svs/ds_e2e.py new file mode 100644 index 0000000000000000000000000000000000000000..7d69d49eff8076f97933538a1d1a59d80b553dd8 --- /dev/null +++ b/NeuralSeq/inference/svs/ds_e2e.py @@ -0,0 +1,67 @@ +import torch +# from inference.tts.fs import FastSpeechInfer +# from modules.tts.fs2_orig import FastSpeech2Orig +from inference.svs.base_svs_infer import BaseSVSInfer +from utils import load_ckpt +from utils.hparams import hparams +from modules.diff.shallow_diffusion_tts import GaussianDiffusion +from tasks.svs.diffsinger_task import DIFF_DECODERS +from modules.fastspeech.pe import PitchExtractor +import utils + + +class DiffSingerE2EInfer(BaseSVSInfer): + def build_model(self): + model = GaussianDiffusion( + phone_encoder=self.ph_encoder, + out_dims=hparams['audio_num_mel_bins'], denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + K_step=hparams['K_step'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + model.eval() + load_ckpt(model, hparams['work_dir'], 'model') + + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + self.pe = PitchExtractor().to(self.device) + utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) + self.pe.eval() + return model + + def forward_model(self, inp): + sample = self.input_to_batch(inp) + txt_tokens = sample['txt_tokens'] # [B, T_t] + spk_id = sample.get('spk_ids') + with torch.no_grad(): + output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True, + pitch_midi=sample['pitch_midi'], midi_dur=sample['midi_dur'], + is_slur=sample['is_slur']) + mel_out = output['mel_out'] # [B, T,80] + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + f0_pred = self.pe(mel_out)['f0_denorm_pred'] # pe predict from Pred mel + else: + f0_pred = output['f0_denorm'] + wav_out = self.run_vocoder(mel_out, f0=f0_pred) + wav_out = wav_out.cpu().numpy() + return wav_out[0] + +if __name__ == '__main__': + inp = { + 'text': '小酒窝长睫毛AP是你最美的记号', + 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', + 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', + 'input_type': 'word' + } # user input: Chinese characters + inp = { + 'text': '小酒窝长睫毛AP是你最美的记号', + 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', + 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', + 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', + 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', + 'input_type': 'phoneme' + } # input like Opencpop dataset. + DiffSingerE2EInfer.example_run(inp) + + +# CUDA_VISIBLE_DEVICES=3 python inference/svs/ds_e2e.py --config egs/egs_bases/svs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name 0228_opencpop_ds100_rel \ No newline at end of file diff --git a/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-37.pyc b/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..14823648fa27268f7ff4c3cbcd86d20f24793f64 Binary files /dev/null and b/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-37.pyc differ diff --git a/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-38.pyc b/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72ba74565a97f2ecddfa17ffb6449e574bddac57 Binary files /dev/null and b/NeuralSeq/inference/svs/opencpop/__pycache__/map.cpython-38.pyc differ diff --git a/NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt b/NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt new file mode 100644 index 0000000000000000000000000000000000000000..fed726000048c318056098a663eff83afd323bc9 --- /dev/null +++ b/NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt @@ -0,0 +1,418 @@ +| a | a | +| ai | ai | +| an | an | +| ang | ang | +| ao | ao | +| ba | b a | +| bai | b ai | +| ban | b an | +| bang | b ang | +| bao | b ao | +| bei | b ei | +| ben | b en | +| beng | b eng | +| bi | b i | +| bian | b ian | +| biao | b iao | +| bie | b ie | +| bin | b in | +| bing | b ing | +| bo | b o | +| bu | b u | +| ca | c a | +| cai | c ai | +| can | c an | +| cang | c ang | +| cao | c ao | +| ce | c e | +| cei | c ei | +| cen | c en | +| ceng | c eng | +| cha | ch a | +| chai | ch ai | +| chan | ch an | +| chang | ch ang | +| chao | ch ao | +| che | ch e | +| chen | ch en | +| cheng | ch eng | +| chi | ch i | +| chong | ch ong | +| chou | ch ou | +| chu | ch u | +| chua | ch ua | +| chuai | ch uai | +| chuan | ch uan | +| chuang | ch uang | +| chui | ch ui | +| chun | ch un | +| chuo | ch uo | +| ci | c i | +| cong | c ong | +| cou | c ou | +| cu | c u | +| cuan | c uan | +| cui | c ui | +| cun | c un | +| cuo | c uo | +| da | d a | +| dai | d ai | +| dan | d an | +| dang | d ang | +| dao | d ao | +| de | d e | +| dei | d ei | +| den | d en | +| deng | d eng | +| di | d i | +| dia | d ia | +| dian | d ian | +| diao | d iao | +| die | d ie | +| ding | d ing | +| diu | d iu | +| dong | d ong | +| dou | d ou | +| du | d u | +| duan | d uan | +| dui | d ui | +| dun | d un | +| duo | d uo | +| e | e | +| ei | ei | +| en | en | +| eng | eng | +| er | er | +| fa | f a | +| fan | f an | +| fang | f ang | +| fei | f ei | +| fen | f en | +| feng | f eng | +| fo | f o | +| fou | f ou | +| fu | f u | +| ga | g a | +| gai | g ai | +| gan | g an | +| gang | g ang | +| gao | g ao | +| ge | g e | +| gei | g ei | +| gen | g en | +| geng | g eng | +| gong | g ong | +| gou | g ou | +| gu | g u | +| gua | g ua | +| guai | g uai | +| guan | g uan | +| guang | g uang | +| gui | g ui | +| gun | g un | +| guo | g uo | +| ha | h a | +| hai | h ai | +| han | h an | +| hang | h ang | +| hao | h ao | +| he | h e | +| hei | h ei | +| hen | h en | +| heng | h eng | +| hm | h m | +| hng | h ng | +| hong | h ong | +| hou | h ou | +| hu | h u | +| hua | h ua | +| huai | h uai | +| huan | h uan | +| huang | h uang | +| hui | h ui | +| hun | h un | +| huo | h uo | +| ji | j i | +| jia | j ia | +| jian | j ian | +| jiang | j iang | +| jiao | j iao | +| jie | j ie | +| jin | j in | +| jing | j ing | +| jiong | j iong | +| jiu | j iu | +| ju | j v | +| juan | j van | +| jue | j ve | +| jun | j vn | +| ka | k a | +| kai | k ai | +| kan | k an | +| kang | k ang | +| kao | k ao | +| ke | k e | +| kei | k ei | +| ken | k en | +| keng | k eng | +| kong | k ong | +| kou | k ou | +| ku | k u | +| kua | k ua | +| kuai | k uai | +| kuan | k uan | +| kuang | k uang | +| kui | k ui | +| kun | k un | +| kuo | k uo | +| la | l a | +| lai | l ai | +| lan | l an | +| lang | l ang | +| lao | l ao | +| le | l e | +| lei | l ei | +| leng | l eng | +| li | l i | +| lia | l ia | +| lian | l ian | +| liang | l iang | +| liao | l iao | +| lie | l ie | +| lin | l in | +| ling | l ing | +| liu | l iu | +| lo | l o | +| long | l ong | +| lou | l ou | +| lu | l u | +| luan | l uan | +| lun | l un | +| luo | l uo | +| lv | l v | +| lve | l ve | +| m | m | +| ma | m a | +| mai | m ai | +| man | m an | +| mang | m ang | +| mao | m ao | +| me | m e | +| mei | m ei | +| men | m en | +| meng | m eng | +| mi | m i | +| mian | m ian | +| miao | m iao | +| mie | m ie | +| min | m in | +| ming | m ing | +| miu | m iu | +| mo | m o | +| mou | m ou | +| mu | m u | +| n | n | +| na | n a | +| nai | n ai | +| nan | n an | +| nang | n ang | +| nao | n ao | +| ne | n e | +| nei | n ei | +| nen | n en | +| neng | n eng | +| ng | n g | +| ni | n i | +| nian | n ian | +| niang | n iang | +| niao | n iao | +| nie | n ie | +| nin | n in | +| ning | n ing | +| niu | n iu | +| nong | n ong | +| nou | n ou | +| nu | n u | +| nuan | n uan | +| nun | n un | +| nuo | n uo | +| nv | n v | +| nve | n ve | +| o | o | +| ou | ou | +| pa | p a | +| pai | p ai | +| pan | p an | +| pang | p ang | +| pao | p ao | +| pei | p ei | +| pen | p en | +| peng | p eng | +| pi | p i | +| pian | p ian | +| piao | p iao | +| pie | p ie | +| pin | p in | +| ping | p ing | +| po | p o | +| pou | p ou | +| pu | p u | +| qi | q i | +| qia | q ia | +| qian | q ian | +| qiang | q iang | +| qiao | q iao | +| qie | q ie | +| qin | q in | +| qing | q ing | +| qiong | q iong | +| qiu | q iu | +| qu | q v | +| quan | q van | +| que | q ve | +| qun | q vn | +| ran | r an | +| rang | r ang | +| rao | r ao | +| re | r e | +| ren | r en | +| reng | r eng | +| ri | r i | +| rong | r ong | +| rou | r ou | +| ru | r u | +| rua | r ua | +| ruan | r uan | +| rui | r ui | +| run | r un | +| ruo | r uo | +| sa | s a | +| sai | s ai | +| san | s an | +| sang | s ang | +| sao | s ao | +| se | s e | +| sen | s en | +| seng | s eng | +| sha | sh a | +| shai | sh ai | +| shan | sh an | +| shang | sh ang | +| shao | sh ao | +| she | sh e | +| shei | sh ei | +| shen | sh en | +| sheng | sh eng | +| shi | sh i | +| shou | sh ou | +| shu | sh u | +| shua | sh ua | +| shuai | sh uai | +| shuan | sh uan | +| shuang | sh uang | +| shui | sh ui | +| shun | sh un | +| shuo | sh uo | +| si | s i | +| song | s ong | +| sou | s ou | +| su | s u | +| suan | s uan | +| sui | s ui | +| sun | s un | +| suo | s uo | +| ta | t a | +| tai | t ai | +| tan | t an | +| tang | t ang | +| tao | t ao | +| te | t e | +| tei | t ei | +| teng | t eng | +| ti | t i | +| tian | t ian | +| tiao | t iao | +| tie | t ie | +| ting | t ing | +| tong | t ong | +| tou | t ou | +| tu | t u | +| tuan | t uan | +| tui | t ui | +| tun | t un | +| tuo | t uo | +| wa | w a | +| wai | w ai | +| wan | w an | +| wang | w ang | +| wei | w ei | +| wen | w en | +| weng | w eng | +| wo | w o | +| wu | w u | +| xi | x i | +| xia | x ia | +| xian | x ian | +| xiang | x iang | +| xiao | x iao | +| xie | x ie | +| xin | x in | +| xing | x ing | +| xiong | x iong | +| xiu | x iu | +| xu | x v | +| xuan | x van | +| xue | x ve | +| xun | x vn | +| ya | y a | +| yan | y an | +| yang | y ang | +| yao | y ao | +| ye | y e | +| yi | y i | +| yin | y in | +| ying | y ing | +| yo | y o | +| yong | y ong | +| you | y ou | +| yu | y v | +| yuan | y van | +| yue | y ve | +| yun | y vn | +| za | z a | +| zai | z ai | +| zan | z an | +| zang | z ang | +| zao | z ao | +| ze | z e | +| zei | z ei | +| zen | z en | +| zeng | z eng | +| zha | zh a | +| zhai | zh ai | +| zhan | zh an | +| zhang | zh ang | +| zhao | zh ao | +| zhe | zh e | +| zhei | zh ei | +| zhen | zh en | +| zheng | zh eng | +| zhi | zh i | +| zhong | zh ong | +| zhou | zh ou | +| zhu | zh u | +| zhua | zh ua | +| zhuai | zh uai | +| zhuan | zh uan | +| zhuang | zh uang | +| zhui | zh ui | +| zhun | zh un | +| zhuo | zh uo | +| zi | z i | +| zong | z ong | +| zou | z ou | +| zu | z u | +| zuan | z uan | +| zui | z ui | +| zun | z un | +| zuo | z uo | \ No newline at end of file diff --git a/NeuralSeq/inference/svs/opencpop/map.py b/NeuralSeq/inference/svs/opencpop/map.py new file mode 100644 index 0000000000000000000000000000000000000000..cf09dbfa25378e8cb098e57c3143f9d66ae00391 --- /dev/null +++ b/NeuralSeq/inference/svs/opencpop/map.py @@ -0,0 +1,8 @@ +def cpop_pinyin2ph_func(): + # In the README file of opencpop dataset, they defined a "pinyin to phoneme mapping table" + pinyin2phs = {'AP': 'AP', 'SP': 'SP'} + with open('NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt') as rf: + for line in rf.readlines(): + elements = [x.strip() for x in line.split('|') if x.strip() != ''] + pinyin2phs[elements[0]] = elements[1] + return pinyin2phs \ No newline at end of file diff --git a/NeuralSeq/inference/tts/GenerSpeech.py b/NeuralSeq/inference/tts/GenerSpeech.py new file mode 100644 index 0000000000000000000000000000000000000000..50527337e218248c763cf7504bc13814cca0696c --- /dev/null +++ b/NeuralSeq/inference/tts/GenerSpeech.py @@ -0,0 +1,123 @@ +import torch +import os +import importlib +from inference.tts.base_tts_infer import BaseTTSInfer +from utils.ckpt_utils import load_ckpt, get_last_checkpoint +from modules.GenerSpeech.model.generspeech import GenerSpeech +from data_gen.tts.emotion import inference as EmotionEncoder +from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance +from data_gen.tts.emotion.inference import preprocess_wav +from data_gen.tts.data_gen_utils import is_sil_phoneme +from resemblyzer import VoiceEncoder +from utils import audio +class GenerSpeechInfer(BaseTTSInfer): + def build_model(self): + model = GenerSpeech(self.ph_encoder) + model.eval() + load_ckpt(model, self.hparams['work_dir'], 'model') + return model + + def preprocess_input(self, inp): + """ + :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} + :return: + """ + # processed text + preprocessor, preprocess_args = self.preprocessor, self.preprocess_args + text_raw = inp['text'] + item_name = inp.get('item_name', '') + ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw, preprocess_args) + ph_token = self.ph_encoder.encode(ph) + + # processed ref audio + ref_audio = inp['ref_audio'] + processed_ref_audio = 'example/temp.wav' + voice_encoder = VoiceEncoder().cuda() + encoder = [self.ph_encoder, self.word_encoder] + EmotionEncoder.load_model(self.hparams['emotion_encoder_path']) + binarizer_cls = self.hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + + ref_audio_raw, ref_text_raw = self.asr(ref_audio) # prepare text + ph_ref, txt_ref, word_ref, ph2word_ref, ph_gb_word_ref = preprocessor.txt_to_ph(preprocessor.txt_processor, ref_text_raw, preprocess_args) + ph_gb_word_nosil = ["_".join([p for p in w.split("_") if not is_sil_phoneme(p)]) for w in ph_gb_word_ref.split(" ") if not is_sil_phoneme(w)] + phs_for_align = ['SIL'] + ph_gb_word_nosil + ['SIL'] + phs_for_align = " ".join(phs_for_align) + + # prepare files for alignment + os.system('rm -r example/; mkdir example/') + audio.save_wav(ref_audio_raw, processed_ref_audio, self.hparams['audio_sample_rate']) + with open(f'example/temp.lab', 'w') as f_txt: + f_txt.write(phs_for_align) + os.system(f'mfa align example/ {self.hparams["binary_data_dir"]}/mfa_dict.txt {self.hparams["binary_data_dir"]}/mfa_model.zip example/textgrid/ --clean') + item2tgfn = 'example/textgrid/temp.TextGrid' # prepare textgrid alignment + + item = binarizer_cls.process_item(item_name, ph_ref, txt_ref, item2tgfn, processed_ref_audio, 0, 0, encoder, self.hparams['binarization_args']) + item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn'])) + item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) + + item.update({ + 'ref_ph': item['ph'], + 'ph': ph, + 'ph_token': ph_token, + 'text': txt + }) + return item + + def input_to_batch(self, item): + item_names = [item['item_name']] + text = [item['text']] + ph = [item['ph']] + + txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) + txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) + mels = torch.FloatTensor(item['mel'])[None, :].to(self.device) + f0 = torch.FloatTensor(item['f0'])[None, :].to(self.device) + # uv = torch.FloatTensor(item['uv']).to(self.device) + mel2ph = torch.LongTensor(item['mel2ph'])[None, :].to(self.device) + spk_embed = torch.FloatTensor(item['spk_embed'])[None, :].to(self.device) + emo_embed = torch.FloatTensor(item['emo_embed'])[None, :].to(self.device) + + ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device) + mel2word = torch.LongTensor(item['mel2word'])[None, :].to(self.device) + word_tokens = torch.LongTensor(item['word_tokens'])[None, :].to(self.device) + + batch = { + 'item_name': item_names, + 'text': text, + 'ph': ph, + 'mels': mels, + 'f0': f0, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'spk_embed': spk_embed, + 'emo_embed': emo_embed, + 'mel2ph': mel2ph, + 'ph2word': ph2word, + 'mel2word': mel2word, + 'word_tokens': word_tokens, + } + return batch + + def forward_model(self, inp): + sample = self.input_to_batch(inp) + txt_tokens = sample['txt_tokens'] # [B, T_t] + with torch.no_grad(): + output = self.model(txt_tokens, ref_mel2ph=sample['mel2ph'], ref_mel2word=sample['mel2word'], ref_mels=sample['mels'], + spk_embed=sample['spk_embed'], emo_embed=sample['emo_embed'], global_steps=300000, infer=True) + mel_out = output['mel_out'] + wav_out = self.run_vocoder(mel_out) + wav_out = wav_out.squeeze().cpu().numpy() + return wav_out + + + + +if __name__ == '__main__': + inp = { + 'text': 'here we go', + 'ref_audio': 'assets/0011_001570.wav' + } + GenerSpeechInfer.example_run(inp) diff --git a/NeuralSeq/inference/tts/PortaSpeech.py b/NeuralSeq/inference/tts/PortaSpeech.py new file mode 100644 index 0000000000000000000000000000000000000000..1e0222284a60f7de0142851ff7ad2bf26fb5ea76 --- /dev/null +++ b/NeuralSeq/inference/tts/PortaSpeech.py @@ -0,0 +1,85 @@ +import torch +from inference.tts.base_tts_infer import BaseTTSInfer +from utils.ckpt_utils import load_ckpt +from modules.portaspeech.portaspeech import PortaSpeech + +class TTSInference(BaseTTSInfer): + def __init__(self, hparams, device=None): + super().__init__(hparams, device) + print("Initializing TTS model to %s" % device) + self.spk_map = self.preprocessor.load_spk_map(self.data_dir) + print("TTS loaded!") + + def build_model(self): + model = PortaSpeech(self.ph_encoder, self.word_encoder) + load_ckpt(model, self.hparams['work_dir'], 'model') + with torch.no_grad(): + model.store_inverse_all() + return model + + def forward_model(self, inp): + sample = self.input_to_batch(inp) + with torch.no_grad(): + output = self.model( + sample['txt_tokens'], + sample['word_tokens'], + ph2word=sample['ph2word'], + word_len=sample['word_lengths'].max(), + infer=True, + forward_post_glow=True, + spk_id=sample.get('spk_ids') + ) + mel_out = output['mel_out'] + wav_out = self.run_vocoder(mel_out) + wav_out = wav_out.cpu().numpy() + return wav_out[0] + + def preprocess_input(self, inp): + """ + + :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)} + :return: + """ + preprocessor, preprocess_args = self.preprocessor, self.preprocess_args + text_raw = inp['text'] + item_name = inp.get('item_name', '') + spk_name = inp.get('spk_name', '') + ph, txt, word, ph2word, ph_gb_word = preprocessor.txt_to_ph( + preprocessor.txt_processor, text_raw, preprocess_args) + word_token = self.word_encoder.encode(word) + ph_token = self.ph_encoder.encode(ph) + spk_id = self.spk_map[spk_name] + item = {'item_name': item_name, 'text': txt, 'ph': ph, 'spk_id': spk_id, + 'ph_token': ph_token, 'word_token': word_token, 'ph2word': ph2word, + 'ph_words':ph_gb_word, 'words': word} + item['ph_len'] = len(item['ph_token']) + return item + + def input_to_batch(self, item): + item_names = [item['item_name']] + text = [item['text']] + ph = [item['ph']] + txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device) + txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) + word_tokens = torch.LongTensor(item['word_token'])[None, :].to(self.device) + word_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device) + ph2word = torch.LongTensor(item['ph2word'])[None, :].to(self.device) + spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device) + batch = { + 'item_name': item_names, + 'text': text, + 'ph': ph, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'word_tokens': word_tokens, + 'word_lengths': word_lengths, + 'ph2word': ph2word, + 'spk_ids': spk_ids, + } + return batch + + def postprocess_output(self, output): + return output + + + diff --git a/NeuralSeq/inference/tts/__pycache__/GenerSpeech.cpython-38.pyc b/NeuralSeq/inference/tts/__pycache__/GenerSpeech.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b4d9ccd43d69f738def8bffbb7970775e9dc4612 Binary files /dev/null and b/NeuralSeq/inference/tts/__pycache__/GenerSpeech.cpython-38.pyc differ diff --git a/NeuralSeq/inference/tts/__pycache__/base_tts_infer.cpython-38.pyc b/NeuralSeq/inference/tts/__pycache__/base_tts_infer.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c301cff551d4e284cd908dcf4e10beb725c7cf7b Binary files /dev/null and b/NeuralSeq/inference/tts/__pycache__/base_tts_infer.cpython-38.pyc differ diff --git a/NeuralSeq/inference/tts/base_tts_infer.py b/NeuralSeq/inference/tts/base_tts_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..6478f910b0dea7bd2bfac85722c775c181a0f826 --- /dev/null +++ b/NeuralSeq/inference/tts/base_tts_infer.py @@ -0,0 +1,101 @@ +from tasks.tts.dataset_utils import FastSpeechWordDataset +from tasks.tts.tts_utils import load_data_preprocessor +from vocoders.hifigan import HifiGanGenerator +import os +import librosa +import soundfile as sf +from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor +from string import punctuation +import torch +from utils.ckpt_utils import load_ckpt +from utils.hparams import set_hparams +from utils.hparams import hparams as hp + +class BaseTTSInfer: + def __init__(self, hparams, device=None): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.hparams = hparams + self.device = device + self.data_dir = hparams['binary_data_dir'] + self.preprocessor, self.preprocess_args = load_data_preprocessor() + self.ph_encoder, self.word_encoder = self.preprocessor.load_dict(self.data_dir) + self.ds_cls = FastSpeechWordDataset + self.model = self.build_model() + self.model.eval() + self.model.to(self.device) + self.vocoder = self.build_vocoder() + self.vocoder.eval() + self.vocoder.to(self.device) + self.asr_processor, self.asr_model = self.build_asr() + + def build_model(self): + raise NotImplementedError + + def forward_model(self, inp): + raise NotImplementedError + + def build_asr(self): + # load pretrained model + processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") # facebook/wav2vec2-base-960h wav2vec2-large-960h-lv60-self + model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to(self.device) + return processor, model + + def build_vocoder(self): + base_dir = self.hparams['vocoder_ckpt'] + config_path = f'{base_dir}/config.yaml' + config = set_hparams(config_path, global_hparams=False) + vocoder = HifiGanGenerator(config) + load_ckpt(vocoder, base_dir, 'model_gen') + return vocoder + + def run_vocoder(self, c): + c = c.transpose(2, 1) + y = self.vocoder(c)[:, 0] + return y + + def preprocess_input(self, inp): + raise NotImplementedError + + def input_to_batch(self, item): + raise NotImplementedError + + def postprocess_output(self, output): + return output + + def infer_once(self, inp): + inp = self.preprocess_input(inp) + output = self.forward_model(inp) + output = self.postprocess_output(output) + return output + + @classmethod + def example_run(cls, inp): + from utils.audio import save_wav + + #set_hparams(print_hparams=False) + infer_ins = cls(hp) + out = infer_ins.infer_once(inp) + os.makedirs('infer_out', exist_ok=True) + save_wav(out, f'infer_out/{hp["text"]}.wav', hp['audio_sample_rate']) + print(f'Save at infer_out/{hp["text"]}.wav.') + + def asr(self, file): + sample_rate = self.hparams['audio_sample_rate'] + audio_input, source_sample_rate = sf.read(file) + + # Resample the wav if needed + if sample_rate is not None and source_sample_rate != sample_rate: + audio_input = librosa.resample(audio_input, source_sample_rate, sample_rate) + + # pad input values and return pt tensor + input_values = self.asr_processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values + + # retrieve logits & take argmax + logits = self.asr_model(input_values.cuda()).logits + predicted_ids = torch.argmax(logits, dim=-1) + + # transcribe + transcription = self.asr_processor.decode(predicted_ids[0]) + transcription = transcription.rstrip(punctuation) + return audio_input, transcription \ No newline at end of file diff --git a/NeuralSeq/modules/GenerSpeech/config/generspeech.yaml b/NeuralSeq/modules/GenerSpeech/config/generspeech.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b81c43a906e979475ff64039974b6a162fba4a5f --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/config/generspeech.yaml @@ -0,0 +1,101 @@ +base_config: + - egs/egs_bases/tts/fs2.yaml + - egs/datasets/audio/emotion/base_text2mel.yaml + +task_cls: modules.GenerSpeech.task.generspeech.GenerSpeechTask + +# emotion encoder +emotion_encoder_path: checkpoints/Emotion_encoder.pt # set the emotion encoder path + +# vocoder +vocoder: hifigan +vocoder_ckpt: checkpoints/trainset_hifigan + +# dataset +raw_data_dir: 'data/raw/training_set' +processed_data_dir: 'data/processed/training_set' +binary_data_dir: 'data/binary/training_set' +test_input_dir: '' + +# process +binarizer_cls: data_gen.tts.base_binarizer_emotion.EmotionBinarizer +audio_sample_rate: 16000 +hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) +win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) +fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) +fmax: 7600 # To be increased/reduced depending on data. +fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter +min_level_db: -100 +ref_level_db: 20 + +binarization_args: + reset_phone_dict: true + reset_word_dict: true + shuffle: true + trim_eos_bos: false + trim_sil: false + with_align: true + with_f0: true + with_f0cwt: false + with_linear: false + with_spk_embed: true + with_spk_id: true + with_txt: true + with_wav: true + with_word: true + +preprocess_cls: egs.datasets.audio.libritts.pre_align.LibrittsPreAlign +preprocess_args: + nsample_per_mfa_group: 1000 + # text process + txt_processor: en + use_mfa: true + with_phsep: true + reset_phone_dict: true + reset_word_dict: true + add_eos_bos: true + # mfa + mfa_group_shuffle: false + mfa_offset: 0.02 + # wav processors + wav_processors: [] + save_sil_mask: true + vad_max_silence_length: 12 + +# data +word_dict_size: 10000 +num_spk: 500 +use_spk_embed: true +use_spk_id: false +use_word: true +use_emotion: true +use_gt_dur: false +ref_audio: '' +text: '' + +# training +num_sanity_val_steps: -1 +max_updates: 300000 +max_sentences: 100000 +num_test_samples: 72 + +## glow +post_glow_hidden: 128 +post_glow_kernel_size: 3 +post_glow_n_blocks: 8 +post_glow_n_block_layers: 3 +share_wn_layers: 4 +sigmoid_scale: false +post_share_cond_layers: false +use_txt_cond: true +use_latent_cond: true +noise_scale: 0.8 + +# prosody extractor +lambda_commit: 0.25 +vq_start: 20500 +vae_dropout: 0.0 +nVQ: 128 +forcing: 20000 +crop: false +predictor_grad: 1.0 \ No newline at end of file diff --git a/NeuralSeq/modules/GenerSpeech/model/__pycache__/generspeech.cpython-38.pyc b/NeuralSeq/modules/GenerSpeech/model/__pycache__/generspeech.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..155e678c96216b8f392c12c2c1f28cea523c8ff0 Binary files /dev/null and b/NeuralSeq/modules/GenerSpeech/model/__pycache__/generspeech.cpython-38.pyc differ diff --git a/NeuralSeq/modules/GenerSpeech/model/__pycache__/glow_modules.cpython-38.pyc b/NeuralSeq/modules/GenerSpeech/model/__pycache__/glow_modules.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..644cd47735290ac278c0c4e884d212ca6c1d4841 Binary files /dev/null and b/NeuralSeq/modules/GenerSpeech/model/__pycache__/glow_modules.cpython-38.pyc differ diff --git a/NeuralSeq/modules/GenerSpeech/model/__pycache__/mixstyle.cpython-38.pyc b/NeuralSeq/modules/GenerSpeech/model/__pycache__/mixstyle.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29bd4484cd6cbd7acca6cd82c910f20e17853718 Binary files /dev/null and b/NeuralSeq/modules/GenerSpeech/model/__pycache__/mixstyle.cpython-38.pyc differ diff --git a/NeuralSeq/modules/GenerSpeech/model/__pycache__/prosody_util.cpython-38.pyc b/NeuralSeq/modules/GenerSpeech/model/__pycache__/prosody_util.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5bd5e5b11464c818dc8f6b3824449ee86baf732f Binary files /dev/null and b/NeuralSeq/modules/GenerSpeech/model/__pycache__/prosody_util.cpython-38.pyc differ diff --git a/NeuralSeq/modules/GenerSpeech/model/__pycache__/wavenet.cpython-38.pyc b/NeuralSeq/modules/GenerSpeech/model/__pycache__/wavenet.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7c74e240179e920db0c626ac6e6246839536f09e Binary files /dev/null and b/NeuralSeq/modules/GenerSpeech/model/__pycache__/wavenet.cpython-38.pyc differ diff --git a/NeuralSeq/modules/GenerSpeech/model/generspeech.py b/NeuralSeq/modules/GenerSpeech/model/generspeech.py new file mode 100644 index 0000000000000000000000000000000000000000..d6ee09c417ae8ac8ce6e3f02a60eea36f4f4ba05 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/model/generspeech.py @@ -0,0 +1,260 @@ +import torch +from modules.GenerSpeech.model.glow_modules import Glow +from modules.fastspeech.tts_modules import PitchPredictor +import random +from modules.GenerSpeech.model.prosody_util import ProsodyAligner, LocalStyleAdaptor +from utils.pitch_utils import f0_to_coarse, denorm_f0 +from modules.commons.common_layers import * +import torch.distributions as dist +from utils.hparams import hparams +from modules.GenerSpeech.model.mixstyle import MixStyle +from modules.fastspeech.fs2 import FastSpeech2 +import json +from modules.fastspeech.tts_modules import DEFAULT_MAX_SOURCE_POSITIONS, DEFAULT_MAX_TARGET_POSITIONS + +class GenerSpeech(FastSpeech2): + ''' + GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech + https://arxiv.org/abs/2205.07211 + ''' + def __init__(self, dictionary, out_dims=None): + super().__init__(dictionary, out_dims) + + # Mixstyle + self.norm = MixStyle(p=0.5, alpha=0.1, eps=1e-6, hidden_size=self.hidden_size) + + # emotion embedding + self.emo_embed_proj = Linear(256, self.hidden_size, bias=True) + + # build prosody extractor + ## frame level + self.prosody_extractor_utter = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) + self.l1_utter = nn.Linear(self.hidden_size * 2, self.hidden_size) + self.align_utter = ProsodyAligner(num_layers=2) + + ## phoneme level + self.prosody_extractor_ph = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) + self.l1_ph = nn.Linear(self.hidden_size * 2, self.hidden_size) + self.align_ph = ProsodyAligner(num_layers=2) + + ## word level + self.prosody_extractor_word = LocalStyleAdaptor(self.hidden_size, hparams['nVQ'], self.padding_idx) + self.l1_word = nn.Linear(self.hidden_size * 2, self.hidden_size) + self.align_word = ProsodyAligner(num_layers=2) + + self.pitch_inpainter_predictor = PitchPredictor( + self.hidden_size, n_chans=self.hidden_size, + n_layers=3, dropout_rate=0.1, odim=2, + padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']) + + # build attention layer + self.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + self.embed_positions = SinusoidalPositionalEmbedding( + self.hidden_size, self.padding_idx, + init_size=self.max_source_positions + self.padding_idx + 1, + ) + + # build post flow + cond_hs = 80 + if hparams.get('use_txt_cond', True): + cond_hs = cond_hs + hparams['hidden_size'] + + cond_hs = cond_hs + hparams['hidden_size'] * 3 # for emo, spk embedding and prosody embedding + self.post_flow = Glow( + 80, hparams['post_glow_hidden'], hparams['post_glow_kernel_size'], 1, + hparams['post_glow_n_blocks'], hparams['post_glow_n_block_layers'], + n_split=4, n_sqz=2, + gin_channels=cond_hs, + share_cond_layers=hparams['post_share_cond_layers'], + share_wn_layers=hparams['share_wn_layers'], + sigmoid_scale=hparams['sigmoid_scale'] + ) + self.prior_dist = dist.Normal(0, 1) + + + def forward(self, txt_tokens, mel2ph=None, ref_mel2ph=None, ref_mel2word=None, spk_embed=None, emo_embed=None, ref_mels=None, + f0=None, uv=None, skip_decoder=False, global_steps=0, infer=False, **kwargs): + ret = {} + encoder_out = self.encoder(txt_tokens) # [B, T, C] + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + + # add spk/emo embed + spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] + emo_embed = self.emo_embed_proj(emo_embed)[:, None, :] + + + # add dur + dur_inp = (encoder_out + spk_embed + emo_embed) * src_nonpadding + mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + decoder_inp = self.expand_states(encoder_out, mel2ph) + decoder_inp = self.norm(decoder_inp, spk_embed + emo_embed) + + # add prosody VQ + ret['ref_mel2ph'] = ref_mel2ph + ret['ref_mel2word'] = ref_mel2word + prosody_utter_mel = self.get_prosody_utter(decoder_inp, ref_mels, ret, infer, global_steps) + prosody_ph_mel = self.get_prosody_ph(decoder_inp, ref_mels, ret, infer, global_steps) + prosody_word_mel = self.get_prosody_word(decoder_inp, ref_mels, ret, infer, global_steps) + + # add pitch embed + pitch_inp_domain_agnostic = decoder_inp * tgt_nonpadding + pitch_inp_domain_specific = (decoder_inp + spk_embed + emo_embed + prosody_utter_mel + prosody_ph_mel + prosody_word_mel) * tgt_nonpadding + predicted_pitch = self.inpaint_pitch(pitch_inp_domain_agnostic, pitch_inp_domain_specific, f0, uv, mel2ph, ret) + + # decode + decoder_inp = decoder_inp + spk_embed + emo_embed + predicted_pitch + prosody_utter_mel + prosody_ph_mel + prosody_word_mel + ret['decoder_inp'] = decoder_inp = decoder_inp * tgt_nonpadding + if skip_decoder: + return ret + ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) + + # postflow + is_training = self.training + ret['x_mask'] = tgt_nonpadding + ret['spk_embed'] = spk_embed + ret['emo_embed'] = emo_embed + ret['ref_prosody'] = prosody_utter_mel + prosody_ph_mel + prosody_word_mel + self.run_post_glow(ref_mels, infer, is_training, ret) + return ret + + def get_prosody_ph(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): + # get VQ prosody + if global_steps > hparams['vq_start'] or infer: + prosody_embedding, loss, ppl = self.prosody_extractor_ph(ref_mels, ret['ref_mel2ph'], no_vq=False) + ret['vq_loss_ph'] = loss + ret['ppl_ph'] = ppl + else: + prosody_embedding = self.prosody_extractor_ph(ref_mels, ret['ref_mel2ph'], no_vq=True) + + # add positional embedding + positions = self.embed_positions(prosody_embedding[:, :, 0]) + prosody_embedding = self.l1_ph(torch.cat([prosody_embedding, positions], dim=-1)) + + + # style-to-content attention + src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data + prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data + if global_steps < hparams['forcing']: + output, guided_loss, attn_emo = self.align_ph(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=True) + else: + output, guided_loss, attn_emo = self.align_ph(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=False) + + ret['gloss_ph'] = guided_loss + ret['attn_ph'] = attn_emo + return output.transpose(0, 1) + + def get_prosody_word(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): + # get VQ prosody + if global_steps > hparams['vq_start'] or infer: + prosody_embedding, loss, ppl = self.prosody_extractor_word(ref_mels, ret['ref_mel2word'], no_vq=False) + ret['vq_loss_word'] = loss + ret['ppl_word'] = ppl + else: + prosody_embedding = self.prosody_extractor_word(ref_mels, ret['ref_mel2word'], no_vq=True) + + # add positional embedding + positions = self.embed_positions(prosody_embedding[:, :, 0]) + prosody_embedding = self.l1_word(torch.cat([prosody_embedding, positions], dim=-1)) + + + # style-to-content attention + src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data + prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data + if global_steps < hparams['forcing']: + output, guided_loss, attn_emo = self.align_word(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=True) + else: + output, guided_loss, attn_emo = self.align_word(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=False) + ret['gloss_word'] = guided_loss + ret['attn_word'] = attn_emo + return output.transpose(0, 1) + + def get_prosody_utter(self, encoder_out, ref_mels, ret, infer=False, global_steps=0): + # get VQ prosody + if global_steps > hparams['vq_start'] or infer: + prosody_embedding, loss, ppl = self.prosody_extractor_utter(ref_mels, no_vq=False) + ret['vq_loss_utter'] = loss + ret['ppl_utter'] = ppl + else: + prosody_embedding = self.prosody_extractor_utter(ref_mels, no_vq=True) + + # add positional embedding + positions = self.embed_positions(prosody_embedding[:, :, 0]) + prosody_embedding = self.l1_utter(torch.cat([prosody_embedding, positions], dim=-1)) + + + # style-to-content attention + src_key_padding_mask = encoder_out[:, :, 0].eq(self.padding_idx).data + prosody_key_padding_mask = prosody_embedding[:, :, 0].eq(self.padding_idx).data + if global_steps < hparams['forcing']: + output, guided_loss, attn_emo = self.align_utter(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=True) + else: + output, guided_loss, attn_emo = self.align_utter(encoder_out.transpose(0, 1), prosody_embedding.transpose(0, 1), + src_key_padding_mask, prosody_key_padding_mask, forcing=False) + ret['gloss_utter'] = guided_loss + ret['attn_utter'] = attn_emo + return output.transpose(0, 1) + + + + def inpaint_pitch(self, pitch_inp_domain_agnostic, pitch_inp_domain_specific, f0, uv, mel2ph, ret): + if hparams['pitch_type'] == 'frame': + pitch_padding = mel2ph == 0 + if hparams['predictor_grad'] != 1: + pitch_inp_domain_agnostic = pitch_inp_domain_agnostic.detach() + hparams['predictor_grad'] * (pitch_inp_domain_agnostic - pitch_inp_domain_agnostic.detach()) + pitch_inp_domain_specific = pitch_inp_domain_specific.detach() + hparams['predictor_grad'] * (pitch_inp_domain_specific - pitch_inp_domain_specific.detach()) + + pitch_domain_agnostic = self.pitch_predictor(pitch_inp_domain_agnostic) + pitch_domain_specific = self.pitch_inpainter_predictor(pitch_inp_domain_specific) + pitch_pred = pitch_domain_agnostic + pitch_domain_specific + ret['pitch_pred'] = pitch_pred + + use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv'] + if f0 is None: + f0 = pitch_pred[:, :, 0] # [B, T] + if use_uv: + uv = pitch_pred[:, :, 1] > 0 # [B, T] + f0_denorm = denorm_f0(f0, uv if use_uv else None, hparams, pitch_padding=pitch_padding) + pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt] + ret['f0_denorm'] = f0_denorm + ret['f0_denorm_pred'] = denorm_f0(pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None, hparams, pitch_padding=pitch_padding) + if hparams['pitch_type'] == 'ph': + pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph) + ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph) + ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph) + pitch_embed = self.pitch_embed(pitch) + return pitch_embed + + def run_post_glow(self, tgt_mels, infer, is_training, ret): + x_recon = ret['mel_out'].transpose(1, 2) + g = x_recon + B, _, T = g.shape + if hparams.get('use_txt_cond', True): + g = torch.cat([g, ret['decoder_inp'].transpose(1, 2)], 1) + g_spk_embed = ret['spk_embed'].repeat(1, T, 1).transpose(1, 2) + g_emo_embed = ret['emo_embed'].repeat(1, T, 1).transpose(1, 2) + l_ref_prosody = ret['ref_prosody'].transpose(1, 2) + g = torch.cat([g, g_spk_embed, g_emo_embed, l_ref_prosody], dim=1) + prior_dist = self.prior_dist + if not infer: + if is_training: + self.train() + x_mask = ret['x_mask'].transpose(1, 2) + y_lengths = x_mask.sum(-1) + g = g.detach() + tgt_mels = tgt_mels.transpose(1, 2) + z_postflow, ldj = self.post_flow(tgt_mels, x_mask, g=g) + ldj = ldj / y_lengths / 80 + ret['z_pf'], ret['ldj_pf'] = z_postflow, ldj + ret['postflow'] = -prior_dist.log_prob(z_postflow).mean() - ldj.mean() + else: + x_mask = torch.ones_like(x_recon[:, :1, :]) + z_post = prior_dist.sample(x_recon.shape).to(g.device) * hparams['noise_scale'] + x_recon_, _ = self.post_flow(z_post, x_mask, g, reverse=True) + x_recon = x_recon_ + ret['mel_out'] = x_recon.transpose(1, 2) \ No newline at end of file diff --git a/NeuralSeq/modules/GenerSpeech/model/glow_modules.py b/NeuralSeq/modules/GenerSpeech/model/glow_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..62d0d5a2884178806275d17c486e62a636d1732a --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/model/glow_modules.py @@ -0,0 +1,767 @@ +import scipy +from torch.nn import functional as F +import torch +from torch import nn +import numpy as np +from modules.commons.common_layers import Permute +from modules.fastspeech.tts_modules import FFTBlocks +from modules.GenerSpeech.model.wavenet import fused_add_tanh_sigmoid_multiply, WN + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + assert n_layers > 1, "Number of layers should be larger than 0." + + self.conv_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = nn.Sequential( + nn.ReLU(), + nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + + +class ActNorm(nn.Module): # glow中的线性变换层 + def __init__(self, channels, ddi=False, **kwargs): + super().__init__() + self.channels = channels + self.initialized = not ddi + + self.logs = nn.Parameter(torch.zeros(1, channels, 1)) + self.bias = nn.Parameter(torch.zeros(1, channels, 1)) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + if x_mask is None: + x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) + x_len = torch.sum(x_mask, [1, 2]) + if not self.initialized: + self.initialize(x, x_mask) + self.initialized = True + + if reverse: + z = (x - self.bias) * torch.exp(-self.logs) * x_mask + logdet = torch.sum(-self.logs) * x_len + else: + z = (self.bias + torch.exp(self.logs) * x) * x_mask + logdet = torch.sum(self.logs) * x_len # [b] + return z, logdet + + def store_inverse(self): + pass + + def set_ddi(self, ddi): + self.initialized = not ddi + + def initialize(self, x, x_mask): + with torch.no_grad(): + denom = torch.sum(x_mask, [0, 2]) + m = torch.sum(x * x_mask, [0, 2]) / denom + m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom + v = m_sq - (m ** 2) + logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) + + bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) + logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) + + self.bias.data.copy_(bias_init) + self.logs.data.copy_(logs_init) + + +class InvConvNear(nn.Module): # 可逆卷积 + def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs): + super().__init__() + assert (n_split % 2 == 0) + self.channels = channels + self.n_split = n_split + self.n_sqz = n_sqz + self.no_jacobian = no_jacobian + + w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] + if torch.det(w_init) < 0: + w_init[:, 0] = -1 * w_init[:, 0] + self.lu = lu + if lu: + # LU decomposition can slightly speed up the inverse + np_p, np_l, np_u = scipy.linalg.lu(w_init) + np_s = np.diag(np_u) + np_sign_s = np.sign(np_s) + np_log_s = np.log(np.abs(np_s)) + np_u = np.triu(np_u, k=1) + l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1) + eye = np.eye(*w_init.shape, dtype=float) + + self.register_buffer('p', torch.Tensor(np_p.astype(float))) + self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) + self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True) + self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True) + self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True) + self.register_buffer('l_mask', torch.Tensor(l_mask)) + self.register_buffer('eye', torch.Tensor(eye)) + else: + self.weight = nn.Parameter(w_init) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + b, c, t = x.size() + assert (c % self.n_split == 0) + if x_mask is None: + x_mask = 1 + x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t + else: + x_len = torch.sum(x_mask, [1, 2]) + + x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t) + x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) + + if self.lu: + self.weight, log_s = self._get_weight() + logdet = log_s.sum() + logdet = logdet * (c / self.n_split) * x_len + else: + logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] + + if reverse: + if hasattr(self, "weight_inv"): + weight = self.weight_inv + else: + weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) + logdet = -logdet + else: + weight = self.weight + if self.no_jacobian: + logdet = 0 + + weight = weight.view(self.n_split, self.n_split, 1, 1) + z = F.conv2d(x, weight) + + z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t) + z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask + return z, logdet + + def _get_weight(self): + l, log_s, u = self.l, self.log_s, self.u + l = l * self.l_mask + self.eye + u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s)) + weight = torch.matmul(self.p, torch.matmul(l, u)) + return weight, log_s + + def store_inverse(self): + weight, _ = self._get_weight() + self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device) + + +class InvConv(nn.Module): + def __init__(self, channels, no_jacobian=False, lu=True, **kwargs): + super().__init__() + w_shape = [channels, channels] + w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float) + LU_decomposed = lu + if not LU_decomposed: + # Sample a random orthogonal matrix: + self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) + else: + np_p, np_l, np_u = scipy.linalg.lu(w_init) + np_s = np.diag(np_u) + np_sign_s = np.sign(np_s) + np_log_s = np.log(np.abs(np_s)) + np_u = np.triu(np_u, k=1) + l_mask = np.tril(np.ones(w_shape, dtype=float), -1) + eye = np.eye(*w_shape, dtype=float) + + self.register_buffer('p', torch.Tensor(np_p.astype(float))) + self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) + self.l = nn.Parameter(torch.Tensor(np_l.astype(float))) + self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float))) + self.u = nn.Parameter(torch.Tensor(np_u.astype(float))) + self.l_mask = torch.Tensor(l_mask) + self.eye = torch.Tensor(eye) + self.w_shape = w_shape + self.LU = LU_decomposed + self.weight = None + + def get_weight(self, device, reverse): + w_shape = self.w_shape + self.p = self.p.to(device) + self.sign_s = self.sign_s.to(device) + self.l_mask = self.l_mask.to(device) + self.eye = self.eye.to(device) + l = self.l * self.l_mask + self.eye + u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) + dlogdet = self.log_s.sum() + if not reverse: + w = torch.matmul(self.p, torch.matmul(l, u)) + else: + l = torch.inverse(l.double()).float() + u = torch.inverse(u.double()).float() + w = torch.matmul(u, torch.matmul(l, self.p.inverse())) + return w.view(w_shape[0], w_shape[1], 1), dlogdet + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + """ + log-det = log|abs(|W|)| * pixels + """ + b, c, t = x.size() + if x_mask is None: + x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t + else: + x_len = torch.sum(x_mask, [1, 2]) + logdet = 0 + if not reverse: + weight, dlogdet = self.get_weight(x.device, reverse) + z = F.conv1d(x, weight) + if logdet is not None: + logdet = logdet + dlogdet * x_len + return z, logdet + else: + if self.weight is None: + weight, dlogdet = self.get_weight(x.device, reverse) + else: + weight, dlogdet = self.weight, self.dlogdet + z = F.conv1d(x, weight) + if logdet is not None: + logdet = logdet - dlogdet * x_len + return z, logdet + + def store_inverse(self): + self.weight, self.dlogdet = self.get_weight('cuda', reverse=True) + + +class Flip(nn.Module): + def forward(self, x, *args, reverse=False, **kwargs): + x = torch.flip(x, [1]) + logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) + return x, logdet + + def store_inverse(self): + pass + + +class CouplingBlock(nn.Module): # 仿射耦合层 + def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=0, p_dropout=0, sigmoid_scale=False, + share_cond_layers=False, wn=None): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + self.sigmoid_scale = sigmoid_scale + + start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) + start = torch.nn.utils.weight_norm(start) + self.start = start + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(hidden_channels, in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, + p_dropout, share_cond_layers) + if wn is not None: + self.wn.in_layers = wn.in_layers + self.wn.res_skip_layers = wn.res_skip_layers + + def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): + if x_mask is None: + x_mask = 1 + x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] + + x = self.start(x_0) * x_mask + x = self.wn(x, x_mask, g) + out = self.end(x) + + z_0 = x_0 + m = out[:, :self.in_channels // 2, :] + logs = out[:, self.in_channels // 2:, :] + if self.sigmoid_scale: + logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) + if reverse: + z_1 = (x_1 - m) * torch.exp(-logs) * x_mask + logdet = torch.sum(-logs * x_mask, [1, 2]) + else: + z_1 = (m + torch.exp(logs) * x_1) * x_mask + logdet = torch.sum(logs * x_mask, [1, 2]) + z = torch.cat([z_0, z_1], 1) + return z, logdet + + def store_inverse(self): + self.wn.remove_weight_norm() + + +class GlowFFTBlocks(FFTBlocks): + def __init__(self, hidden_size=128, gin_channels=256, num_layers=2, ffn_kernel_size=5, + dropout=None, num_heads=4, use_pos_embed=True, use_last_norm=True, + norm='ln', use_pos_embed_alpha=True): + super().__init__(hidden_size, num_layers, ffn_kernel_size, dropout, num_heads, use_pos_embed, + use_last_norm, norm, use_pos_embed_alpha) + self.inp_proj = nn.Conv1d(hidden_size + gin_channels, hidden_size, 1) + + def forward(self, x, x_mask=None, g=None): + """ + :param x: [B, C_x, T] + :param x_mask: [B, 1, T] + :param g: [B, C_g, T] + :return: [B, C_x, T] + """ + if g is not None: + x = self.inp_proj(torch.cat([x, g], 1)) + x = x.transpose(1, 2) + x = super(GlowFFTBlocks, self).forward(x, x_mask[:, 0] == 0) + x = x.transpose(1, 2) + return x + + +class TransformerCouplingBlock(nn.Module): + def __init__(self, in_channels, hidden_channels, n_layers, + gin_channels=0, p_dropout=0, sigmoid_scale=False): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + self.sigmoid_scale = sigmoid_scale + + start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) + self.start = start + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(hidden_channels, in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + self.fft_blocks = GlowFFTBlocks( + hidden_size=hidden_channels, + ffn_kernel_size=3, + gin_channels=gin_channels, + num_layers=n_layers) + + def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): + if x_mask is None: + x_mask = 1 + x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] + + x = self.start(x_0) * x_mask + x = self.fft_blocks(x, x_mask, g) + out = self.end(x) + + z_0 = x_0 + m = out[:, :self.in_channels // 2, :] + logs = out[:, self.in_channels // 2:, :] + if self.sigmoid_scale: + logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) + if reverse: + z_1 = (x_1 - m) * torch.exp(-logs) * x_mask + logdet = torch.sum(-logs * x_mask, [1, 2]) + else: + z_1 = (m + torch.exp(logs) * x_1) * x_mask + logdet = torch.sum(logs * x_mask, [1, 2]) + z = torch.cat([z_0, z_1], 1) + return z, logdet + + def store_inverse(self): + pass + + +class FreqFFTCouplingBlock(nn.Module): + def __init__(self, in_channels, hidden_channels, n_layers, + gin_channels=0, p_dropout=0, sigmoid_scale=False): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + self.sigmoid_scale = sigmoid_scale + + hs = hidden_channels + stride = 8 + self.start = torch.nn.Conv2d(3, hs, kernel_size=stride * 2, + stride=stride, padding=stride // 2) + end = nn.ConvTranspose2d(hs, 2, kernel_size=stride, stride=stride) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = nn.Sequential( + nn.Conv2d(hs * 3, hs, 3, 1, 1), + nn.ReLU(), + nn.GroupNorm(4, hs), + nn.Conv2d(hs, hs, 3, 1, 1), + end + ) + self.fft_v = FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers) + self.fft_h = nn.Sequential( + nn.Conv1d(hs, hs, 3, 1, 1), + nn.ReLU(), + nn.Conv1d(hs, hs, 3, 1, 1), + ) + self.fft_g = nn.Sequential( + nn.Conv1d( + gin_channels - 160, hs, kernel_size=stride * 2, stride=stride, padding=stride // 2), + Permute(0, 2, 1), + FFTBlocks(hidden_size=hs, ffn_kernel_size=1, num_layers=n_layers), + Permute(0, 2, 1), + ) + + def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): + g_, _ = unsqueeze(g) + g_mel = g_[:, :80] + g_txt = g_[:, 80:] + g_mel, _ = squeeze(g_mel) + g_txt, _ = squeeze(g_txt) # [B, C, T] + + if x_mask is None: + x_mask = 1 + x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] + x = torch.stack([x_0, g_mel[:, :80], g_mel[:, 80:]], 1) + x = self.start(x) # [B, C, N_bins, T] + B, C, N_bins, T = x.shape + + x_v = self.fft_v(x.permute(0, 3, 2, 1).reshape(B * T, N_bins, C)) + x_v = x_v.reshape(B, T, N_bins, -1).permute(0, 3, 2, 1) + # x_v = x + + x_h = self.fft_h(x.permute(0, 2, 1, 3).reshape(B * N_bins, C, T)) + x_h = x_h.reshape(B, N_bins, -1, T).permute(0, 2, 1, 3) + # x_h = x + + x_g = self.fft_g(g_txt)[:, :, None, :].repeat(1, 1, 10, 1) + x = torch.cat([x_v, x_h, x_g], 1) + out = self.end(x) + + z_0 = x_0 + m = out[:, 0] + logs = out[:, 1] + if self.sigmoid_scale: + logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) + if reverse: + z_1 = (x_1 - m) * torch.exp(-logs) * x_mask + logdet = torch.sum(-logs * x_mask, [1, 2]) + else: + z_1 = (m + torch.exp(logs) * x_1) * x_mask + logdet = torch.sum(logs * x_mask, [1, 2]) + z = torch.cat([z_0, z_1], 1) + return z, logdet + + def store_inverse(self): + pass + + +class Glow(nn.Module): + def __init__(self, + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_blocks, + n_layers, + p_dropout=0., + n_split=4, + n_sqz=2, + sigmoid_scale=False, + gin_channels=0, + inv_conv_type='near', + share_cond_layers=False, + share_wn_layers=0, + ): + super().__init__() + + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_blocks = n_blocks + self.n_layers = n_layers + self.p_dropout = p_dropout + self.n_split = n_split + self.n_sqz = n_sqz + self.sigmoid_scale = sigmoid_scale + self.gin_channels = gin_channels + self.share_cond_layers = share_cond_layers + if gin_channels != 0 and share_cond_layers: + cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + wn = None + self.flows = nn.ModuleList() + for b in range(n_blocks): + self.flows.append(ActNorm(channels=in_channels * n_sqz)) + if inv_conv_type == 'near': + self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz)) + if inv_conv_type == 'invconv': + self.flows.append(InvConv(channels=in_channels * n_sqz)) + if share_wn_layers > 0: + if b % share_wn_layers == 0: + wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz, + p_dropout, share_cond_layers) + self.flows.append( + CouplingBlock( + in_channels * n_sqz, + hidden_channels, + kernel_size=kernel_size, + dilation_rate=dilation_rate, + n_layers=n_layers, + gin_channels=gin_channels * n_sqz, + p_dropout=p_dropout, + sigmoid_scale=sigmoid_scale, + share_cond_layers=share_cond_layers, + wn=wn + )) + + def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): + logdet_tot = 0 + if not reverse: + flows = self.flows + else: + flows = reversed(self.flows) + if return_hiddens: + hs = [] + if self.n_sqz > 1: + x, x_mask_ = squeeze(x, x_mask, self.n_sqz) + if g is not None: + g, _ = squeeze(g, x_mask, self.n_sqz) + x_mask = x_mask_ + if self.share_cond_layers and g is not None: + g = self.cond_layer(g) + for f in flows: + x, logdet = f(x, x_mask, g=g, reverse=reverse) + if return_hiddens: + hs.append(x) + logdet_tot += logdet + if self.n_sqz > 1: + x, x_mask = unsqueeze(x, x_mask, self.n_sqz) + if return_hiddens: + return x, logdet_tot, hs + return x, logdet_tot + + def store_inverse(self): + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) + for f in self.flows: + f.store_inverse() + + +class GlowV2(nn.Module): + def __init__(self, + in_channels=256, + hidden_channels=256, + kernel_size=3, + dilation_rate=1, + n_blocks=8, + n_layers=4, + p_dropout=0., + n_split=4, + n_split_blocks=3, + sigmoid_scale=False, + gin_channels=0, + share_cond_layers=True): + super().__init__() + + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_blocks = n_blocks + self.n_layers = n_layers + self.p_dropout = p_dropout + self.n_split = n_split + self.n_split_blocks = n_split_blocks + self.sigmoid_scale = sigmoid_scale + self.gin_channels = gin_channels + + self.cond_layers = nn.ModuleList() + self.share_cond_layers = share_cond_layers + + self.flows = nn.ModuleList() + in_channels = in_channels * 2 + for l in range(n_split_blocks): + blocks = nn.ModuleList() + self.flows.append(blocks) + gin_channels = gin_channels * 2 + if gin_channels != 0 and share_cond_layers: + cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1) + self.cond_layers.append(torch.nn.utils.weight_norm(cond_layer, name='weight')) + for b in range(n_blocks): + blocks.append(ActNorm(channels=in_channels)) + blocks.append(InvConvNear(channels=in_channels, n_split=n_split)) + blocks.append(CouplingBlock( + in_channels, + hidden_channels, + kernel_size=kernel_size, + dilation_rate=dilation_rate, + n_layers=n_layers, + gin_channels=gin_channels, + p_dropout=p_dropout, + sigmoid_scale=sigmoid_scale, + share_cond_layers=share_cond_layers)) + + def forward(self, x=None, x_mask=None, g=None, reverse=False, concat_zs=True, + noise_scale=0.66, return_hiddens=False): + logdet_tot = 0 + if not reverse: + flows = self.flows + assert x_mask is not None + zs = [] + if return_hiddens: + hs = [] + for i, blocks in enumerate(flows): + x, x_mask = squeeze(x, x_mask) + g_ = None + if g is not None: + g, _ = squeeze(g) + if self.share_cond_layers: + g_ = self.cond_layers[i](g) + else: + g_ = g + for layer in blocks: + x, logdet = layer(x, x_mask=x_mask, g=g_, reverse=reverse) + if return_hiddens: + hs.append(x) + logdet_tot += logdet + if i == self.n_split_blocks - 1: + zs.append(x) + else: + x, z = torch.chunk(x, 2, 1) + zs.append(z) + if concat_zs: + zs = [z.reshape(x.shape[0], -1) for z in zs] + zs = torch.cat(zs, 1) # [B, C*T] + if return_hiddens: + return zs, logdet_tot, hs + return zs, logdet_tot + else: + flows = reversed(self.flows) + if x is not None: + assert isinstance(x, list) + zs = x + else: + B, _, T = g.shape + zs = self.get_prior(B, T, g.device, noise_scale) + zs_ori = zs + if g is not None: + g_, g = g, [] + for i in range(len(self.flows)): + g_, _ = squeeze(g_) + g.append(self.cond_layers[i](g_) if self.share_cond_layers else g_) + else: + g = [None for _ in range(len(self.flows))] + if x_mask is not None: + x_masks = [] + for i in range(len(self.flows)): + x_mask, _ = squeeze(x_mask) + x_masks.append(x_mask) + else: + x_masks = [None for _ in range(len(self.flows))] + x_masks = x_masks[::-1] + g = g[::-1] + zs = zs[::-1] + x = None + for i, blocks in enumerate(flows): + x = zs[i] if x is None else torch.cat([x, zs[i]], 1) + for layer in reversed(blocks): + x, logdet = layer(x, x_masks=x_masks[i], g=g[i], reverse=reverse) + logdet_tot += logdet + x, _ = unsqueeze(x) + return x, logdet_tot, zs_ori + + def store_inverse(self): + for f in self.modules(): + if hasattr(f, 'store_inverse') and f != self: + f.store_inverse() + + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) + + def get_prior(self, B, T, device, noise_scale=0.66): + C = 80 + zs = [] + for i in range(len(self.flows)): + C, T = C, T // 2 + if i == self.n_split_blocks - 1: + zs.append(torch.randn(B, C * 2, T).to(device) * noise_scale) + else: + zs.append(torch.randn(B, C, T).to(device) * noise_scale) + return zs + + +def squeeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + t = (t // n_sqz) * n_sqz + x = x[:, :, :t] + x_sqz = x.view(b, c, t // n_sqz, n_sqz) + x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) + + if x_mask is not None: + x_mask = x_mask[:, :, n_sqz - 1::n_sqz] + else: + x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) + return x_sqz * x_mask, x_mask + + +def unsqueeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + x_unsqz = x.view(b, n_sqz, c // n_sqz, t) + x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) + + if x_mask is not None: + x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) + else: + x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) + return x_unsqz * x_mask, x_mask diff --git a/NeuralSeq/modules/GenerSpeech/model/mixstyle.py b/NeuralSeq/modules/GenerSpeech/model/mixstyle.py new file mode 100644 index 0000000000000000000000000000000000000000..8f592118f0bb224e8d004ce8884d0ff6f5fd9fc1 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/model/mixstyle.py @@ -0,0 +1,63 @@ +from modules.commons.common_layers import * +import random + + +class MixStyle(nn.Module): + """MixStyle. + Reference: + Zhou et al. Domain Generalization with MixStyle. ICLR 2021. + """ + + def __init__(self, p=0.5, alpha=0.1, eps=1e-6, hidden_size=256): + """ + Args: + p (float): probability of using MixStyle. + alpha (float): parameter of the Beta distribution. + eps (float): scaling parameter to avoid numerical issues. + mix (str): how to mix. + """ + super().__init__() + self.p = p + self.beta = torch.distributions.Beta(alpha, alpha) + self.eps = eps + self.alpha = alpha + self._activated = True + self.hidden_size = hidden_size + self.affine_layer = LinearNorm( + hidden_size, + 2 * hidden_size, # For both b (bias) g (gain) + ) + + def __repr__(self): + return f'MixStyle(p={self.p}, alpha={self.alpha}, eps={self.eps})' + + def set_activation_status(self, status=True): + self._activated = status + + def forward(self, x, spk_embed): + if not self.training or not self._activated: + return x + + if random.random() > self.p: + return x + + B = x.size(0) + + mu, sig = torch.mean(x, dim=-1, keepdim=True), torch.std(x, dim=-1, keepdim=True) + x_normed = (x - mu) / (sig + 1e-6) # [B, T, H_m] + + lmda = self.beta.sample((B, 1, 1)) + lmda = lmda.to(x.device) + + # Get Bias and Gain + mu1, sig1 = torch.split(self.affine_layer(spk_embed), self.hidden_size, dim=-1) # [B, 1, 2 * H_m] --> 2 * [B, 1, H_m] + + # MixStyle + perm = torch.randperm(B) + mu2, sig2 = mu1[perm], sig1[perm] + + mu_mix = mu1*lmda + mu2 * (1-lmda) + sig_mix = sig1*lmda + sig2 * (1-lmda) + + # Perform Scailing and Shifting + return sig_mix * x_normed + mu_mix # [B, T, H_m] diff --git a/NeuralSeq/modules/GenerSpeech/model/prosody_util.py b/NeuralSeq/modules/GenerSpeech/model/prosody_util.py new file mode 100644 index 0000000000000000000000000000000000000000..113c39df9d1b0144aa5a5f00505c7e08bfc6ea11 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/model/prosody_util.py @@ -0,0 +1,385 @@ +from torch import nn +import copy +import torch +from utils.hparams import hparams +from modules.GenerSpeech.model.wavenet import WN +import math + +from modules.fastspeech.tts_modules import LayerNorm +import torch.nn.functional as F +from utils.tts_utils import group_hidden_by_segs, sequence_mask + +from scipy.cluster.vq import kmeans2 +from torch.nn import functional as F + + +class VQEmbeddingEMA(nn.Module): + def __init__(self, n_embeddings, embedding_dim, commitment_cost=0.25, decay=0.999, epsilon=1e-5, + print_vq_prob=False): + super(VQEmbeddingEMA, self).__init__() + self.commitment_cost = commitment_cost + self.n_embeddings = n_embeddings + self.decay = decay + self.epsilon = epsilon + self.print_vq_prob = print_vq_prob + self.register_buffer('data_initialized', torch.zeros(1)) + init_bound = 1 / 512 + embedding = torch.Tensor(n_embeddings, embedding_dim) + embedding.uniform_(-init_bound, init_bound) + self.register_buffer("embedding", embedding) + self.register_buffer("ema_count", torch.zeros(n_embeddings)) + self.register_buffer("ema_weight", self.embedding.clone()) + + def encode(self, x): + B, T, _ = x.shape + M, D = self.embedding.size() + x_flat = x.detach().reshape(-1, D) + + distances = torch.addmm(torch.sum(self.embedding ** 2, dim=1) + + torch.sum(x_flat ** 2, dim=1, keepdim=True), + x_flat, self.embedding.t(), + alpha=-2.0, beta=1.0) # [B*T_mel, N_vq] + indices = torch.argmin(distances.float(), dim=-1) # [B*T_mel] + quantized = F.embedding(indices, self.embedding) + quantized = quantized.view_as(x) + return x_flat, quantized, indices + + def forward(self, x): + """ + + :param x: [B, T, D] + :return: [B, T, D] + """ + B, T, _ = x.shape + M, D = self.embedding.size() + if self.training and self.data_initialized.item() == 0: + print('| running kmeans in VQVAE') # data driven initialization for the embeddings + x_flat = x.detach().reshape(-1, D) + rp = torch.randperm(x_flat.size(0)) + kd = kmeans2(x_flat[rp].data.cpu().numpy(), self.n_embeddings, minit='points') + self.embedding.copy_(torch.from_numpy(kd[0])) + x_flat, quantized, indices = self.encode(x) + encodings = F.one_hot(indices, M).float() + self.ema_weight.copy_(torch.matmul(encodings.t(), x_flat)) + self.ema_count.copy_(torch.sum(encodings, dim=0)) + + x_flat, quantized, indices = self.encode(x) + encodings = F.one_hot(indices, M).float() + indices = indices.reshape(B, T) + + if self.training and self.data_initialized.item() != 0: + self.ema_count = self.decay * self.ema_count + (1 - self.decay) * torch.sum(encodings, dim=0) + + n = torch.sum(self.ema_count) + self.ema_count = (self.ema_count + self.epsilon) / (n + M * self.epsilon) * n + + dw = torch.matmul(encodings.t(), x_flat) + self.ema_weight = self.decay * self.ema_weight + (1 - self.decay) * dw + + self.embedding = self.ema_weight / self.ema_count.unsqueeze(-1) + self.data_initialized.fill_(1) + + e_latent_loss = F.mse_loss(x, quantized.detach(), reduction='none') + nonpadding = (x.abs().sum(-1) > 0).float() + e_latent_loss = (e_latent_loss.mean(-1) * nonpadding).sum() / nonpadding.sum() + loss = self.commitment_cost * e_latent_loss + + quantized = x + (quantized - x).detach() + + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + if self.print_vq_prob: + print("| VQ code avg_probs: ", avg_probs) + return quantized, loss, indices, perplexity + +class CrossAttenLayer(nn.Module): + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1): + super(CrossAttenLayer, self).__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + self.linear2 = nn.Linear(dim_feedforward, d_model) + self.dropout2 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + self.activation = nn.ReLU() + + def forward(self, src, local_emotion, emotion_key_padding_mask=None, forcing=False): + # src: (Tph, B, 256) local_emotion: (Temo, B, 256) emotion_key_padding_mask: (B, Temo) + if forcing: + maxlength = src.shape[0] + k = local_emotion.shape[0] / src.shape[0] + lengths1 = torch.ceil(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) + 1 + lengths2 = torch.floor(torch.tensor([i for i in range(maxlength)]).to(src.device) * k) - 1 + mask1 = sequence_mask(lengths1, local_emotion.shape[0]) + mask2 = sequence_mask(lengths2, local_emotion.shape[0]) + mask = mask1.float() - mask2.float() + attn_emo = mask.repeat(src.shape[1], 1, 1) # (B, Tph, Temo) + src2 = torch.matmul(local_emotion.permute(1, 2, 0), attn_emo.float().transpose(1, 2)).permute(2, 0, 1) + else: + src2, attn_emo = self.multihead_attn(src, local_emotion, local_emotion, key_padding_mask=emotion_key_padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.activation(self.linear1(src))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src, attn_emo + + +class ProsodyAligner(nn.Module): + def __init__(self, num_layers, guided_sigma=0.3, guided_layers=None, norm=None): + super(ProsodyAligner, self).__init__() + self.layers = nn.ModuleList([CrossAttenLayer(d_model=hparams['hidden_size'], nhead=2) for _ in range(num_layers)]) + self.num_layers = num_layers + self.norm = norm + self.guided_sigma = guided_sigma + self.guided_layers = guided_layers if guided_layers is not None else num_layers + + def forward(self, src, local_emotion, src_key_padding_mask=None, emotion_key_padding_mask=None, forcing=False): + output = src + guided_loss = 0 + attn_emo_list = [] + for i, mod in enumerate(self.layers): + # output: (Tph, B, 256), global_emotion: (1, B, 256), local_emotion: (Temo, B, 256) mask: None, src_key_padding_mask: (B, Tph), + # emotion_key_padding_mask: (B, Temo) + output, attn_emo = mod(output, local_emotion, emotion_key_padding_mask=emotion_key_padding_mask, forcing=forcing) + attn_emo_list.append(attn_emo.unsqueeze(1)) + # attn_emo: (B, Tph, Temo) attn: (B, Tph, Tph) + if i < self.guided_layers and src_key_padding_mask is not None: + s_length = (~src_key_padding_mask).float().sum(-1) # B + emo_length = (~emotion_key_padding_mask).float().sum(-1) + attn_w_emo = _make_guided_attention_mask(src_key_padding_mask.size(-1), s_length, emotion_key_padding_mask.size(-1), emo_length, self.guided_sigma) + + g_loss_emo = attn_emo * attn_w_emo # N, L, S + non_padding_mask = (~src_key_padding_mask).unsqueeze(-1) & (~emotion_key_padding_mask).unsqueeze(1) + guided_loss = g_loss_emo[non_padding_mask].mean() + guided_loss + + if self.norm is not None: + output = self.norm(output) + + return output, guided_loss, attn_emo_list + +def _make_guided_attention_mask(ilen, rilen, olen, rolen, sigma): + grid_x, grid_y = torch.meshgrid(torch.arange(ilen, device=rilen.device), torch.arange(olen, device=rolen.device)) + grid_x = grid_x.unsqueeze(0).expand(rilen.size(0), -1, -1) + grid_y = grid_y.unsqueeze(0).expand(rolen.size(0), -1, -1) + rilen = rilen.unsqueeze(1).unsqueeze(1) + rolen = rolen.unsqueeze(1).unsqueeze(1) + return 1.0 - torch.exp( + -((grid_y.float() / rolen - grid_x.float() / rilen) ** 2) / (2 * (sigma ** 2)) + ) + +class LocalStyleAdaptor(nn.Module): + def __init__(self, hidden_size, num_vq_codes=64, padding_idx=0): + super(LocalStyleAdaptor, self).__init__() + self.encoder = ConvBlocks(80, hidden_size, [1] * 5, 5, dropout=hparams['vae_dropout']) + self.n_embed = num_vq_codes + self.vqvae = VQEmbeddingEMA(self.n_embed, hidden_size, commitment_cost=hparams['lambda_commit']) + self.wavenet = WN(hidden_channels=80, gin_channels=80, kernel_size=3, dilation_rate=1, n_layers=4) + self.padding_idx = padding_idx + self.hidden_size = hidden_size + + def forward(self, ref_mels, mel2ph=None, no_vq=False): + """ + + :param ref_mels: [B, T, 80] + :return: [B, 1, H] + """ + padding_mask = ref_mels[:, :, 0].eq(self.padding_idx).data + ref_mels = self.wavenet(ref_mels.transpose(1, 2), x_mask=(~padding_mask).unsqueeze(1).repeat([1, 80, 1])).transpose(1, 2) + if mel2ph is not None: + ref_ph, _ = group_hidden_by_segs(ref_mels, mel2ph, torch.max(mel2ph)) + else: + ref_ph = ref_mels + prosody = self.encoder(ref_ph) + if no_vq: + return prosody + z, vq_loss, vq_tokens, ppl = self.vqvae(prosody) + vq_loss = vq_loss.mean() + return z, vq_loss, ppl + + + + +class LambdaLayer(nn.Module): + def __init__(self, lambd): + super(LambdaLayer, self).__init__() + self.lambd = lambd + + def forward(self, x): + return self.lambd(x) + + +class Conv1d(nn.Conv1d): + """A wrapper around nn.Conv1d, that works on (batch, time, channels)""" + + def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, dilation=1, groups=1, bias=True, padding=0): + super(Conv1d, self).__init__(in_channels=in_channels, out_channels=out_channels, + kernel_size=kernel_size, stride=stride, dilation=dilation, + groups=groups, bias=bias, padding=padding) + + def forward(self, x): + return super().forward(x.transpose(2, 1)).transpose(2, 1) + + +def init_weights_func(m): + classname = m.__class__.__name__ + if classname.find("Conv1d") != -1: + torch.nn.init.xavier_uniform_(m.weight) + + +class ResidualBlock(nn.Module): + """Implements conv->PReLU->norm n-times""" + + def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0, + c_multiple=2, ln_eps=1e-12): + super(ResidualBlock, self).__init__() + + if norm_type == 'bn': + norm_builder = lambda: nn.BatchNorm1d(channels) + elif norm_type == 'in': + norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True) + elif norm_type == 'gn': + norm_builder = lambda: nn.GroupNorm(8, channels) + elif norm_type == 'ln': + norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps) + else: + norm_builder = lambda: nn.Identity() + + self.blocks = [ + nn.Sequential( + norm_builder(), + nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation, + padding=(dilation * (kernel_size - 1)) // 2), + LambdaLayer(lambda x: x * kernel_size ** -0.5), + nn.GELU(), + nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation), + ) + for i in range(n) + ] + + self.blocks = nn.ModuleList(self.blocks) + self.dropout = dropout + + def forward(self, x): + nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] + for b in self.blocks: + x_ = b(x) + if self.dropout > 0 and self.training: + x_ = F.dropout(x_, self.dropout, training=self.training) + x = x + x_ + x = x * nonpadding + return x + + +class Pad(nn.ZeroPad2d): + def __init__(self, kernel_size, dilation): + pad_total = dilation * (kernel_size - 1) + begin = pad_total // 2 + end = pad_total - begin + + super(Pad, self).__init__((begin, end, begin, end)) + + +class ZeroTemporalPad(nn.ZeroPad2d): + """Pad sequences to equal lentgh in the temporal dimension""" + + def __init__(self, kernel_size, dilation, causal=False): + total_pad = (dilation * (kernel_size - 1)) + + if causal: + super(ZeroTemporalPad, self).__init__((total_pad, 0)) + else: + begin = total_pad // 2 + end = total_pad - begin + super(ZeroTemporalPad, self).__init__((begin, end)) + + +class ConvBlocks(nn.Module): + """Decodes the expanded phoneme encoding into spectrograms""" + + def __init__(self, channels, out_dims, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, init_weights=True): + super(ConvBlocks, self).__init__() + self.res_blocks = nn.Sequential( + *[ResidualBlock(channels, kernel_size, d, + n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple, + dropout=dropout, ln_eps=ln_eps) + for d in dilations], + ) + if norm_type == 'bn': + norm = nn.BatchNorm1d(channels) + elif norm_type == 'in': + norm = nn.InstanceNorm1d(channels, affine=True) + elif norm_type == 'gn': + norm = nn.GroupNorm(8, channels) + elif norm_type == 'ln': + norm = LayerNorm(channels, dim=1, eps=ln_eps) + self.last_norm = norm + self.post_net1 = nn.Conv1d(channels, out_dims, kernel_size=3, padding=1) + if init_weights: + self.apply(init_weights_func) + + def forward(self, x): + """ + + :param x: [B, T, H] + :return: [B, T, H] + """ + x = x.transpose(1, 2) + nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] + x = self.res_blocks(x) * nonpadding + x = self.last_norm(x) * nonpadding + x = self.post_net1(x) * nonpadding + return x.transpose(1, 2) + + +class TextConvEncoder(ConvBlocks): + def __init__(self, embed_tokens, channels, out_dims, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, init_weights=True): + super().__init__(channels, out_dims, dilations, kernel_size, + norm_type, layers_in_block, c_multiple, + dropout, ln_eps, init_weights) + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(channels) + + def forward(self, txt_tokens): + """ + + :param txt_tokens: [B, T] + :return: { + 'encoder_out': [B x T x C] + } + """ + x = self.embed_scale * self.embed_tokens(txt_tokens) + return super().forward(x) + + +class ConditionalConvBlocks(ConvBlocks): + def __init__(self, channels, g_channels, out_dims, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True): + super().__init__(channels, out_dims, dilations, kernel_size, + norm_type, layers_in_block, c_multiple, + dropout, ln_eps, init_weights) + self.g_prenet = nn.Conv1d(g_channels, channels, 3, padding=1) + self.is_BTC = is_BTC + if init_weights: + self.g_prenet.apply(init_weights_func) + + def forward(self, x, g, x_mask): + if self.is_BTC: + x = x.transpose(1, 2) + g = g.transpose(1, 2) + x_mask = x_mask.transpose(1, 2) + x = x + self.g_prenet(g) + x = x * x_mask + + if not self.is_BTC: + x = x.transpose(1, 2) + x = super(ConditionalConvBlocks, self).forward(x) # input needs to be BTC + if not self.is_BTC: + x = x.transpose(1, 2) + return x diff --git a/NeuralSeq/modules/GenerSpeech/model/wavenet.py b/NeuralSeq/modules/GenerSpeech/model/wavenet.py new file mode 100644 index 0000000000000000000000000000000000000000..481c02d9cc776eba40e578e1b2549bf352357be8 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/model/wavenet.py @@ -0,0 +1,87 @@ +from modules.commons.common_layers import * + + +# @torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +class WN(torch.nn.Module): + def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, + p_dropout=0, share_cond_layers=False): + super(WN, self).__init__() + assert (kernel_size % 2 == 1) + assert (hidden_channels % 2 == 0) + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + self.share_cond_layers = share_cond_layers + + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + self.drop = nn.Dropout(p_dropout) + + if gin_channels != 0 and not share_cond_layers: + cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = dilation_rate ** i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * hidden_channels + else: + res_skip_channels = hidden_channels + + res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, x, x_mask=None, g=None, **kwargs): + output = torch.zeros_like(x) + n_channels_tensor = torch.IntTensor([self.hidden_channels]) + + if g is not None and not self.share_cond_layers: + g = self.cond_layer(g) + + for i in range(self.n_layers): + x_in = self.in_layers[i](x) + x_in = self.drop(x_in) + if g is not None: + cond_offset = i * 2 * self.hidden_channels + g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :] + else: + g_l = torch.zeros_like(x_in) + + acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + x = (x + res_skip_acts[:, :self.hidden_channels, :]) * x_mask + output = output + res_skip_acts[:, self.hidden_channels:, :] + else: + output = output + res_skip_acts + return output * x_mask + + def remove_weight_norm(self): + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) diff --git a/NeuralSeq/modules/GenerSpeech/task/dataset.py b/NeuralSeq/modules/GenerSpeech/task/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0b1f6fa2c596a23b9bc49ad470c1723d3597d738 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/task/dataset.py @@ -0,0 +1,193 @@ +import matplotlib +matplotlib.use('Agg') +from tasks.base_task import data_loader +from tasks.tts.fs2 import FastSpeech2Task +from tasks.tts.dataset_utils import FastSpeechDataset, BaseTTSDataset +import glob +import importlib +from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse +from inference.base_tts_infer import load_data_preprocessor +from data_gen.tts.emotion import inference as EmotionEncoder +from data_gen.tts.emotion.inference import embed_utterance as Embed_utterance +from data_gen.tts.emotion.inference import preprocess_wav +from tqdm import tqdm +from utils.hparams import hparams +from data_gen.tts.data_gen_utils import build_phone_encoder, build_word_encoder +import random +import torch +import torch.optim +import torch.nn.functional as F +import torch.utils.data +from utils.indexed_datasets import IndexedDataset +from resemblyzer import VoiceEncoder +import torch.distributions +import numpy as np +import utils +import os + + + +class GenerSpeech_dataset(BaseTTSDataset): + def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): + super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) + self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) + if prefix == 'valid': + indexed_ds = IndexedDataset(f'{self.data_dir}/train') + sizes = np.load(f'{self.data_dir}/train_lengths.npy') + index = [i for i in range(len(indexed_ds))] + random.shuffle(index) + index = index[:300] + self.sizes = sizes[index] + self.indexed_ds = [] + for i in index: + self.indexed_ds.append(indexed_ds[i]) + self.avail_idxs = list(range(len(self.sizes))) + if hparams['min_frames'] > 0: + self.avail_idxs = [x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] + self.sizes = [self.sizes[i] for i in self.avail_idxs] + + if prefix == 'test' and hparams['test_input_dir'] != '': + self.preprocessor, self.preprocess_args = load_data_preprocessor() + self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) + self.avail_idxs = [i for i, _ in enumerate(self.sizes)] + + + def load_test_inputs(self, test_input_dir): + inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')) + binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + + phone_encoder = build_phone_encoder(hparams['binary_data_dir']) + word_encoder = build_word_encoder(hparams['binary_data_dir']) + voice_encoder = VoiceEncoder().cuda() + + encoder = [phone_encoder, word_encoder] + sizes = [] + items = [] + EmotionEncoder.load_model(hparams['emotion_encoder_path']) + preprocessor, preprocess_args = self.preprocessor, self.preprocess_args + + for wav_fn in tqdm(inp_wav_paths): + item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_") + spk_id = emotion = 0 + item2tgfn = wav_fn.replace('.wav', '.TextGrid') # prepare textgrid alignment + txtpath = wav_fn.replace('.wav', '.txt') # prepare text + with open(txtpath, 'r') as f: + text_raw = f.readlines() + f.close() + ph, txt = preprocessor.txt_to_ph(preprocessor.txt_processor, text_raw[0], preprocess_args) + + item = binarizer_cls.process_item(item_name, ph, txt, item2tgfn, wav_fn, spk_id, emotion, encoder, hparams['binarization_args']) + item['emo_embed'] = Embed_utterance(preprocess_wav(item['wav_fn'])) + item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) + items.append(item) + sizes.append(item['len']) + return items, sizes + + def _get_item(self, index): + if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: + index = self.avail_idxs[index] + if self.indexed_ds is None: + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + return self.indexed_ds[index] + + def __getitem__(self, index): + hparams = self.hparams + item = self._get_item(index) + assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) + max_frames = hparams['max_frames'] + spec = torch.Tensor(item['mel'])[:max_frames] + max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] + spec = spec[:max_frames] + phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) + sample = { + "id": index, + "item_name": item['item_name'], + "text": item['txt'], + "txt_token": phone, + "mel": spec, + "mel_nonpadding": spec.abs().sum(-1) > 0, + } + spec = sample['mel'] + T = spec.shape[0] + sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None + if hparams['use_pitch_embed']: + assert 'f0' in item + if hparams.get('normalize_pitch', False): + f0 = item["f0"] + if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: + f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ + hparams['f0_mean'] + f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) + pitch = f0_to_coarse(f0) + pitch = torch.LongTensor(pitch[:max_frames]) + else: + pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None + f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) + uv = torch.FloatTensor(uv) + f0 = torch.FloatTensor(f0) + else: + f0 = uv = torch.zeros_like(mel2ph) + pitch = None + sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch + sample["spk_embed"] = torch.Tensor(item['spk_embed']) + sample["emotion"] = item['emotion'] + sample["emo_embed"] = torch.Tensor(item['emo_embed']) + + if hparams.get('use_word', False): + sample["ph_words"] = item["ph_words"] + sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) + sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] + sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) + return sample + + def collater(self, samples): + if len(samples) == 0: + return {} + hparams = self.hparams + id = torch.LongTensor([s['id'] for s in samples]) + item_names = [s['item_name'] for s in samples] + text = [s['text'] for s in samples] + txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) + mels = utils.collate_2d([s['mel'] for s in samples], 0.0) + txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) + mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) + + batch = { + 'id': id, + 'item_name': item_names, + 'nsamples': len(samples), + 'text': text, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'mels': mels, + 'mel_lengths': mel_lengths, + } + + f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) + pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None + uv = utils.collate_1d([s['uv'] for s in samples]) + mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) if samples[0]['mel2ph'] is not None else None + batch.update({ + 'mel2ph': mel2ph, + 'pitch': pitch, + 'f0': f0, + 'uv': uv, + }) + spk_embed = torch.stack([s['spk_embed'] for s in samples]) + batch['spk_embed'] = spk_embed + emo_embed = torch.stack([s['emo_embed'] for s in samples]) + batch['emo_embed'] = emo_embed + + if hparams.get('use_word', False): + ph_words = [s['ph_words'] for s in samples] + batch['ph_words'] = ph_words + word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) + batch['word_tokens'] = word_tokens + mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) + batch['mel2word'] = mel2word + ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) + batch['ph2word'] = ph2word + return batch \ No newline at end of file diff --git a/NeuralSeq/modules/GenerSpeech/task/generspeech.py b/NeuralSeq/modules/GenerSpeech/task/generspeech.py new file mode 100644 index 0000000000000000000000000000000000000000..7536bf651ebcfeef3df97c752b4801c487671e95 --- /dev/null +++ b/NeuralSeq/modules/GenerSpeech/task/generspeech.py @@ -0,0 +1,271 @@ +import matplotlib +matplotlib.use('Agg') +from data_gen.tts.data_gen_utils import get_pitch +from modules.fastspeech.tts_modules import mel2ph_to_dur +import matplotlib.pyplot as plt +from utils import audio +from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse +from vocoders.base_vocoder import get_vocoder_cls +import json +from utils.plot import spec_to_figure +from utils.hparams import hparams +import torch +import torch.optim +import torch.nn.functional as F +import torch.utils.data +from modules.GenerSpeech.task.dataset import GenerSpeech_dataset +from modules.GenerSpeech.model.generspeech import GenerSpeech +import torch.distributions +import numpy as np +from utils.tts_utils import select_attn +import utils +import os +from tasks.tts.fs2 import FastSpeech2Task + +class GenerSpeechTask(FastSpeech2Task): + def __init__(self): + super(GenerSpeechTask, self).__init__() + self.dataset_cls = GenerSpeech_dataset + + def build_tts_model(self): + self.model = GenerSpeech(self.phone_encoder) + + def build_model(self): + self.build_tts_model() + if hparams['load_ckpt'] != '': + self.load_ckpt(hparams['load_ckpt'], strict=False) + utils.num_params(self.model) + return self.model + + def run_model(self, model, sample, return_output=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + mel2ph = sample['mel2ph'] # [B, T_s] + mel2word = sample['mel2word'] + f0 = sample['f0'] # [B, T_s] + uv = sample['uv'] # [B, T_s] 0/1 + + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + emo_embed = sample.get('emo_embed') + output = model(txt_tokens, mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, + ref_mels=target, f0=f0, uv=uv, tgt_mels=target, global_steps=self.global_step, infer=False) + losses = {} + losses['postflow'] = output['postflow'] + if self.global_step > hparams['forcing']: + losses['gloss'] = (output['gloss_utter'] + output['gloss_ph'] + output['gloss_word']) / 3 + if self.global_step > hparams['vq_start']: + losses['vq_loss'] = (output['vq_loss_utter'] + output['vq_loss_ph'] + output['vq_loss_word']) / 3 + losses['ppl_utter'] = output['ppl_utter'] + losses['ppl_ph'] = output['ppl_ph'] + losses['ppl_word'] = output['ppl_word'] + self.add_mel_loss(output['mel_out'], target, losses) + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + output['select_attn'] = select_attn(output['attn_ph']) + + if not return_output: + return losses + else: + return losses, output + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + encdec_attn = model_out['select_attn'] + mel_out = self.model.out2mel(model_out['mel_out']) + outputs = utils.tensors_to_scalars(outputs) + if self.global_step % hparams['valid_infer_interval'] == 0 \ + and batch_idx < hparams['num_valid_plots']: + vmin = hparams['mel_vmin'] + vmax = hparams['mel_vmax'] + self.plot_mel(batch_idx, sample['mels'], mel_out) + self.plot_dur(batch_idx, sample, model_out) + if hparams['use_pitch_embed']: + self.plot_pitch(batch_idx, sample, model_out) + if self.vocoder is None: + self.vocoder = get_vocoder_cls(hparams)() + if self.global_step > 0: + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + emo_embed = sample.get('emo_embed') + ref_mels = sample['mels'] + mel2ph = sample['mel2ph'] # [B, T_s] + mel2word = sample['mel2word'] + # with gt duration + model_out = self.model(sample['txt_tokens'], mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, + emo_embed=emo_embed, ref_mels=ref_mels, global_steps=self.global_step, infer=True) + wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu()) + self.logger.add_audio(f'wav_gtdur_{batch_idx}', wav_pred, self.global_step, + hparams['audio_sample_rate']) + self.logger.add_figure(f'ali_{batch_idx}', spec_to_figure(encdec_attn[0]), self.global_step) + self.logger.add_figure( + f'mel_gtdur_{batch_idx}', + spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step) + # with pred duration + model_out = self.model(sample['txt_tokens'], ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, ref_mels=ref_mels, + global_steps=self.global_step, infer=True) + self.logger.add_figure( + f'mel_{batch_idx}', + spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step) + wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu()) + self.logger.add_audio(f'wav_{batch_idx}', wav_pred, self.global_step, hparams['audio_sample_rate']) + # gt wav + if self.global_step <= hparams['valid_infer_interval']: + mel_gt = sample['mels'][0].cpu() + wav_gt = self.vocoder.spec2wav(mel_gt) + self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, 22050) + return outputs + + ############ + # infer + ############ + def test_step(self, sample, batch_idx): + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + emo_embed = sample.get('emo_embed') + txt_tokens = sample['txt_tokens'] + mel2ph, uv, f0 = None, None, None + ref_mel2word = sample['mel2word'] + ref_mel2ph = sample['mel2ph'] + ref_mels = sample['mels'] + if hparams['use_gt_dur']: + mel2ph = sample['mel2ph'] + if hparams['use_gt_f0']: + f0 = sample['f0'] + uv = sample['uv'] + global_steps = 200000 + run_model = lambda: self.model( + txt_tokens, spk_embed=spk_embed, emo_embed=emo_embed, mel2ph=mel2ph, ref_mel2ph=ref_mel2ph, ref_mel2word=ref_mel2word, + f0=f0, uv=uv, ref_mels=ref_mels, global_steps=global_steps, infer=True) + outputs = run_model() + sample['outputs'] = self.model.out2mel(outputs['mel_out']) + sample['mel2ph_pred'] = outputs['mel2ph'] + if hparams['use_pitch_embed']: + sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) + if hparams['pitch_type'] == 'ph': + sample['f0'] = torch.gather(F.pad(sample['f0'], [1, 0]), 1, sample['mel2ph']) + sample['f0_pred'] = outputs.get('f0_denorm') + + return self.after_infer(sample) + + + + def after_infer(self, predictions, sil_start_frame=0): + predictions = utils.unpack_dict_to_list(predictions) + assert len(predictions) == 1, 'Only support batch_size=1 in inference.' + prediction = predictions[0] + prediction = utils.tensors_to_np(prediction) + item_name = prediction.get('item_name') + text = prediction.get('text') + ph_tokens = prediction.get('txt_tokens') + mel_gt = prediction["mels"] + mel2ph_gt = prediction.get("mel2ph") + mel2ph_gt = mel2ph_gt if mel2ph_gt is not None else None + mel_pred = prediction["outputs"] + mel2ph_pred = prediction.get("mel2ph_pred") + f0_gt = prediction.get("f0") + f0_pred = prediction.get("f0_pred") + + str_phs = None + if self.phone_encoder is not None and 'txt_tokens' in prediction: + str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) + + if 'encdec_attn' in prediction: + encdec_attn = prediction['encdec_attn'] # (1, Tph, Tmel) + encdec_attn = encdec_attn[encdec_attn.max(-1).sum(-1).argmax(-1)] + txt_lengths = prediction.get('txt_lengths') + encdec_attn = encdec_attn.T[:, :txt_lengths] + else: + encdec_attn = None + + wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) + wav_pred[:sil_start_frame * hparams['hop_size']] = 0 + gen_dir = self.gen_dir + base_fn = f'[{self.results_id:06d}][{item_name}][%s]' + # if text is not None: + # base_fn += text.replace(":", "%3A")[:80] + base_fn = base_fn.replace(' ', '_') + if not hparams['profile_infer']: + os.makedirs(gen_dir, exist_ok=True) + os.makedirs(f'{gen_dir}/wavs', exist_ok=True) + os.makedirs(f'{gen_dir}/plot', exist_ok=True) + if hparams.get('save_mel_npy', False): + os.makedirs(f'{gen_dir}/npy', exist_ok=True) + if 'encdec_attn' in prediction: + os.makedirs(f'{gen_dir}/attn_plot', exist_ok=True) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_pred, mel_pred, base_fn % 'TTS', gen_dir, str_phs, mel2ph_pred, encdec_attn])) + + if mel_gt is not None and hparams['save_gt']: + wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_gt, mel_gt, base_fn % 'Ref', gen_dir, str_phs, mel2ph_gt])) + if hparams['save_f0']: + import matplotlib.pyplot as plt + f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) + f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) + fig = plt.figure() + plt.plot(f0_pred_, label=r'$\hat{f_0}$') + plt.plot(f0_gt_, label=r'$f_0$') + plt.legend() + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') + plt.close(fig) + + print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") + self.results_id += 1 + return { + 'item_name': item_name, + 'text': text, + 'ph_tokens': self.phone_encoder.decode(ph_tokens.tolist()), + 'wav_fn_pred': base_fn % 'TTS', + 'wav_fn_gt': base_fn % 'Ref', + } + + + + @staticmethod + def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None): + audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], + norm=hparams['out_wav_norm']) + fig = plt.figure(figsize=(14, 10)) + spec_vmin = hparams['mel_vmin'] + spec_vmax = hparams['mel_vmax'] + heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) + fig.colorbar(heatmap) + f0, _ = get_pitch(wav_out, mel, hparams) + f0 = f0 / 10 * (f0 > 0) + plt.plot(f0, c='white', linewidth=1, alpha=0.6) + if mel2ph is not None and str_phs is not None: + decoded_txt = str_phs.split(" ") + dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() + dur = [0] + list(np.cumsum(dur)) + for i in range(len(dur) - 1): + shift = (i % 20) + 1 + plt.text(dur[i], shift, decoded_txt[i]) + plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') + plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', + alpha=1, linewidth=1) + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png') + plt.close(fig) + if hparams.get('save_mel_npy', False): + np.save(f'{gen_dir}/npy/{base_fn}', mel) + if alignment is not None: + fig, ax = plt.subplots(figsize=(12, 16)) + im = ax.imshow(alignment, aspect='auto', origin='lower', + interpolation='none') + ax.set_xticks(np.arange(0, alignment.shape[1], 5)) + ax.set_yticks(np.arange(0, alignment.shape[0], 10)) + ax.set_ylabel("$S_p$ index") + ax.set_xlabel("$H_c$ index") + fig.colorbar(im, ax=ax) + fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png') + plt.close(fig) + + + diff --git a/NeuralSeq/modules/__init__.py b/NeuralSeq/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/NeuralSeq/modules/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/modules/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f34d33c24157b9d3332a2728c36005b6e92b8dac Binary files /dev/null and b/NeuralSeq/modules/__pycache__/__init__.cpython-37.pyc differ diff --git a/NeuralSeq/modules/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/modules/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e8a11d844e87fc91b95dff9bfc5c730261213582 Binary files /dev/null and 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a/NeuralSeq/modules/commons/__pycache__/ssim.cpython-38.pyc b/NeuralSeq/modules/commons/__pycache__/ssim.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a3903d17d9eac07d557acc94182ef93e0b7c22e Binary files /dev/null and b/NeuralSeq/modules/commons/__pycache__/ssim.cpython-38.pyc differ diff --git a/NeuralSeq/modules/commons/align_ops.py b/NeuralSeq/modules/commons/align_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..a190d63a3f3ba31f41754975569336a87c63089d --- /dev/null +++ b/NeuralSeq/modules/commons/align_ops.py @@ -0,0 +1,25 @@ +import torch +import torch.nn.functional as F + + +def build_word_mask(x2word, y2word): + return (x2word[:, :, None] == y2word[:, None, :]).long() + + +def mel2ph_to_mel2word(mel2ph, ph2word): + mel2word = (ph2word - 1).gather(1, (mel2ph - 1).clamp(min=0)) + 1 + mel2word = mel2word * (mel2ph > 0).long() + return mel2word + + +def clip_mel2token_to_multiple(mel2token, frames_multiple): + max_frames = mel2token.shape[1] // frames_multiple * frames_multiple + mel2token = mel2token[:, :max_frames] + return mel2token + + +def expand_states(h, mel2token): + h = F.pad(h, [0, 0, 1, 0]) + mel2token_ = mel2token[..., None].repeat([1, 1, h.shape[-1]]) + h = torch.gather(h, 1, mel2token_) # [B, T, H] + return h diff --git a/NeuralSeq/modules/commons/common_layers.py b/NeuralSeq/modules/commons/common_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..0657b0dcb3cb00340e8b354bd4a196c527aa668e --- /dev/null +++ b/NeuralSeq/modules/commons/common_layers.py @@ -0,0 +1,667 @@ +import math +import torch +from torch import nn +from torch.nn import Parameter +import torch.onnx.operators +import torch.nn.functional as F +import utils + + +class Reshape(nn.Module): + def __init__(self, *args): + super(Reshape, self).__init__() + self.shape = args + + def forward(self, x): + return x.view(self.shape) + + +class Permute(nn.Module): + def __init__(self, *args): + super(Permute, self).__init__() + self.args = args + + def forward(self, x): + return x.permute(self.args) + + +class LinearNorm(torch.nn.Module): + def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): + super(LinearNorm, self).__init__() + self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) + + torch.nn.init.xavier_uniform_( + self.linear_layer.weight, + gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, x): + return self.linear_layer(x) + + +class ConvNorm(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, + padding=None, dilation=1, bias=True, w_init_gain='linear'): + super(ConvNorm, self).__init__() + if padding is None: + assert (kernel_size % 2 == 1) + padding = int(dilation * (kernel_size - 1) / 2) + + self.conv = torch.nn.Conv1d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, + bias=bias) + + torch.nn.init.xavier_uniform_( + self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, signal): + conv_signal = self.conv(signal) + return conv_signal + + +def Embedding(num_embeddings, embedding_dim, padding_idx=None): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + if padding_idx is not None: + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): + if not export and torch.cuda.is_available(): + try: + from apex.normalization import FusedLayerNorm + return FusedLayerNorm(normalized_shape, eps, elementwise_affine) + except ImportError: + pass + return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.) + return m + + +class SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = SinusoidalPositionalEmbedding.get_embedding( + init_size, + embedding_dim, + padding_idx, + ) + self.register_buffer('_float_tensor', torch.FloatTensor(1)) + + @staticmethod + def get_embedding(num_embeddings, embedding_dim, padding_idx=None): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): + """Input is expected to be of size [bsz x seqlen].""" + bsz, seq_len = input.shape[:2] + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + # recompute/expand embeddings if needed + self.weights = SinusoidalPositionalEmbedding.get_embedding( + max_pos, + self.embedding_dim, + self.padding_idx, + ) + self.weights = self.weights.to(self._float_tensor) + + if incremental_state is not None: + # positions is the same for every token when decoding a single step + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) + + positions = utils.make_positions(input, self.padding_idx) if positions is None else positions + return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() + + def max_positions(self): + """Maximum number of supported positions.""" + return int(1e5) # an arbitrary large number + + +class ConvTBC(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, padding=0): + super(ConvTBC, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.padding = padding + + self.weight = torch.nn.Parameter(torch.Tensor( + self.kernel_size, in_channels, out_channels)) + self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) + + def forward(self, input): + return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding) + + +class MultiheadAttention(nn.Module): + def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, + add_bias_kv=False, add_zero_attn=False, self_attention=False, + encoder_decoder_attention=False): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim ** -0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ + 'value to be of the same size' + + if self.qkv_same_dim: + self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) + else: + self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) + self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) + self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) + + if bias: + self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) + else: + self.register_parameter('in_proj_bias', None) + + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + self.enable_torch_version = False + if hasattr(F, "multi_head_attention_forward"): + self.enable_torch_version = True + else: + self.enable_torch_version = False + self.last_attn_probs = None + + def reset_parameters(self): + if self.qkv_same_dim: + nn.init.xavier_uniform_(self.in_proj_weight) + else: + nn.init.xavier_uniform_(self.k_proj_weight) + nn.init.xavier_uniform_(self.v_proj_weight) + nn.init.xavier_uniform_(self.q_proj_weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.in_proj_bias is not None: + nn.init.constant_(self.in_proj_bias, 0.) + nn.init.constant_(self.out_proj.bias, 0.) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def forward( + self, + query, key, value, + key_padding_mask=None, + incremental_state=None, + need_weights=True, + static_kv=False, + attn_mask=None, + before_softmax=False, + need_head_weights=False, + enc_dec_attn_constraint_mask=None, + reset_attn_weight=None + ): + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + + if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: + if self.qkv_same_dim: + return F.multi_head_attention_forward(query, key, value, + self.embed_dim, self.num_heads, + self.in_proj_weight, + self.in_proj_bias, self.bias_k, self.bias_v, + self.add_zero_attn, self.dropout, + self.out_proj.weight, self.out_proj.bias, + self.training, key_padding_mask, need_weights, + attn_mask) + else: + return F.multi_head_attention_forward(query, key, value, + self.embed_dim, self.num_heads, + torch.empty([0]), + self.in_proj_bias, self.bias_k, self.bias_v, + self.add_zero_attn, self.dropout, + self.out_proj.weight, self.out_proj.bias, + self.training, key_padding_mask, need_weights, + attn_mask, use_separate_proj_weight=True, + q_proj_weight=self.q_proj_weight, + k_proj_weight=self.k_proj_weight, + v_proj_weight=self.v_proj_weight) + + if incremental_state is not None: + print('Not implemented error.') + exit() + else: + saved_state = None + + if self.self_attention: + # self-attention + q, k, v = self.in_proj_qkv(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.in_proj_q(query) + if key is None: + assert value is None + k = v = None + else: + k = self.in_proj_k(key) + v = self.in_proj_v(key) + + else: + q = self.in_proj_q(query) + k = self.in_proj_k(key) + v = self.in_proj_v(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) + + q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if k is not None: + k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if v is not None: + v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + + if saved_state is not None: + print('Not implemented error.') + exit() + + src_len = k.size(1) + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + if len(attn_mask.shape) == 2: + attn_mask = attn_mask.unsqueeze(0) + elif len(attn_mask.shape) == 3: + attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( + bsz * self.num_heads, tgt_len, src_len) + attn_weights = attn_weights + attn_mask + + if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.masked_fill( + enc_dec_attn_constraint_mask.unsqueeze(2).bool(), + -1e9, + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + -1e9, + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils.softmax(attn_weights, dim=-1) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) + + if reset_attn_weight is not None: + if reset_attn_weight: + self.last_attn_probs = attn_probs.detach() + else: + assert self.last_attn_probs is not None + attn_probs = self.last_attn_probs + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + + if need_weights: + attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + else: + attn_weights = None + + return attn, (attn_weights, attn_logits) + + def in_proj_qkv(self, query): + return self._in_proj(query).chunk(3, dim=-1) + + def in_proj_q(self, query): + if self.qkv_same_dim: + return self._in_proj(query, end=self.embed_dim) + else: + bias = self.in_proj_bias + if bias is not None: + bias = bias[:self.embed_dim] + return F.linear(query, self.q_proj_weight, bias) + + def in_proj_k(self, key): + if self.qkv_same_dim: + return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) + else: + weight = self.k_proj_weight + bias = self.in_proj_bias + if bias is not None: + bias = bias[self.embed_dim:2 * self.embed_dim] + return F.linear(key, weight, bias) + + def in_proj_v(self, value): + if self.qkv_same_dim: + return self._in_proj(value, start=2 * self.embed_dim) + else: + weight = self.v_proj_weight + bias = self.in_proj_bias + if bias is not None: + bias = bias[2 * self.embed_dim:] + return F.linear(value, weight, bias) + + def _in_proj(self, input, start=0, end=None): + weight = self.in_proj_weight + bias = self.in_proj_bias + weight = weight[start:end, :] + if bias is not None: + bias = bias[start:end] + return F.linear(input, weight, bias) + + + def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): + return attn_weights + + +class Swish(torch.autograd.Function): + @staticmethod + def forward(ctx, i): + result = i * torch.sigmoid(i) + ctx.save_for_backward(i) + return result + + @staticmethod + def backward(ctx, grad_output): + i = ctx.saved_variables[0] + sigmoid_i = torch.sigmoid(i) + return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) + + +class CustomSwish(nn.Module): + def forward(self, input_tensor): + return Swish.apply(input_tensor) + + +class TransformerFFNLayer(nn.Module): + def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'): + super().__init__() + self.kernel_size = kernel_size + self.dropout = dropout + self.act = act + if padding == 'SAME': + self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2) + elif padding == 'LEFT': + self.ffn_1 = nn.Sequential( + nn.ConstantPad1d((kernel_size - 1, 0), 0.0), + nn.Conv1d(hidden_size, filter_size, kernel_size) + ) + self.ffn_2 = Linear(filter_size, hidden_size) + if self.act == 'swish': + self.swish_fn = CustomSwish() + + def forward(self, x, incremental_state=None): + # x: T x B x C + if incremental_state is not None: + assert incremental_state is None, 'Nar-generation does not allow this.' + exit(1) + + x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) + x = x * self.kernel_size ** -0.5 + + if incremental_state is not None: + x = x[-1:] + if self.act == 'gelu': + x = F.gelu(x) + if self.act == 'relu': + x = F.relu(x) + if self.act == 'swish': + x = self.swish_fn(x) + x = F.dropout(x, self.dropout, training=self.training) + x = self.ffn_2(x) + return x + + +class BatchNorm1dTBC(nn.Module): + def __init__(self, c): + super(BatchNorm1dTBC, self).__init__() + self.bn = nn.BatchNorm1d(c) + + def forward(self, x): + """ + + :param x: [T, B, C] + :return: [T, B, C] + """ + x = x.permute(1, 2, 0) # [B, C, T] + x = self.bn(x) # [B, C, T] + x = x.permute(2, 0, 1) # [T, B, C] + return x + + +class EncSALayer(nn.Module): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, + relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'): + super().__init__() + self.c = c + self.dropout = dropout + self.num_heads = num_heads + if num_heads > 0: + if norm == 'ln': + self.layer_norm1 = LayerNorm(c) + elif norm == 'bn': + self.layer_norm1 = BatchNorm1dTBC(c) + self.self_attn = MultiheadAttention( + self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False, + ) + if norm == 'ln': + self.layer_norm2 = LayerNorm(c) + elif norm == 'bn': + self.layer_norm2 = BatchNorm1dTBC(c) + self.ffn = TransformerFFNLayer( + c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) + + def forward(self, x, encoder_padding_mask=None, **kwargs): + layer_norm_training = kwargs.get('layer_norm_training', None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + if self.num_heads > 0: + residual = x + x = self.layer_norm1(x) + x, _, = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask + ) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] + + residual = x + x = self.layer_norm2(x) + x = self.ffn(x) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] + return x + + +class DecSALayer(nn.Module): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'): + super().__init__() + self.c = c + self.dropout = dropout + self.layer_norm1 = LayerNorm(c) + self.self_attn = MultiheadAttention( + c, num_heads, self_attention=True, dropout=attention_dropout, bias=False + ) + self.layer_norm2 = LayerNorm(c) + self.encoder_attn = MultiheadAttention( + c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, + ) + self.layer_norm3 = LayerNorm(c) + self.ffn = TransformerFFNLayer( + c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) + + def forward( + self, + x, + encoder_out=None, + encoder_padding_mask=None, + incremental_state=None, + self_attn_mask=None, + self_attn_padding_mask=None, + attn_out=None, + reset_attn_weight=None, + **kwargs, + ): + layer_norm_training = kwargs.get('layer_norm_training', None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + self.layer_norm3.training = layer_norm_training + residual = x + x = self.layer_norm1(x) + x, _ = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + attn_mask=self_attn_mask + ) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + + residual = x + x = self.layer_norm2(x) + if encoder_out is not None: + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + enc_dec_attn_constraint_mask=None, #utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'), + reset_attn_weight=reset_attn_weight + ) + attn_logits = attn[1] + else: + assert attn_out is not None + x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1)) + attn_logits = None + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + + residual = x + x = self.layer_norm3(x) + x = self.ffn(x, incremental_state=incremental_state) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + # if len(attn_logits.size()) > 3: + # indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1) + # attn_logits = attn_logits.gather(1, + # indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1) + return x, attn_logits diff --git a/NeuralSeq/modules/commons/conv.py b/NeuralSeq/modules/commons/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..a86505f4216231418390610e423e42b3b3b77b15 --- /dev/null +++ b/NeuralSeq/modules/commons/conv.py @@ -0,0 +1,168 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.commons.common_layers import Embedding +from modules.fastspeech.tts_modules import LayerNorm + + +class LambdaLayer(nn.Module): + def __init__(self, lambd): + super(LambdaLayer, self).__init__() + self.lambd = lambd + + def forward(self, x): + return self.lambd(x) + + +def init_weights_func(m): + classname = m.__class__.__name__ + if classname.find("Conv1d") != -1: + torch.nn.init.xavier_uniform_(m.weight) + + +class ResidualBlock(nn.Module): + """Implements conv->PReLU->norm n-times""" + + def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0, + c_multiple=2, ln_eps=1e-12): + super(ResidualBlock, self).__init__() + + if norm_type == 'bn': + norm_builder = lambda: nn.BatchNorm1d(channels) + elif norm_type == 'in': + norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True) + elif norm_type == 'gn': + norm_builder = lambda: nn.GroupNorm(8, channels) + elif norm_type == 'ln': + norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps) + else: + norm_builder = lambda: nn.Identity() + + self.blocks = [ + nn.Sequential( + norm_builder(), + nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation, + padding=(dilation * (kernel_size - 1)) // 2), + LambdaLayer(lambda x: x * kernel_size ** -0.5), + nn.GELU(), + nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation), + ) + for i in range(n) + ] + + self.blocks = nn.ModuleList(self.blocks) + self.dropout = dropout + + def forward(self, x): + nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] + for b in self.blocks: + x_ = b(x) + if self.dropout > 0 and self.training: + x_ = F.dropout(x_, self.dropout, training=self.training) + x = x + x_ + x = x * nonpadding + return x + + +class ConvBlocks(nn.Module): + """Decodes the expanded phoneme encoding into spectrograms""" + + def __init__(self, hidden_size, out_dims, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, + init_weights=True, is_BTC=True, num_layers=None, post_net_kernel=3): + super(ConvBlocks, self).__init__() + self.is_BTC = is_BTC + if num_layers is not None: + dilations = [1] * num_layers + self.res_blocks = nn.Sequential( + *[ResidualBlock(hidden_size, kernel_size, d, + n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple, + dropout=dropout, ln_eps=ln_eps) + for d in dilations], + ) + if norm_type == 'bn': + norm = nn.BatchNorm1d(hidden_size) + elif norm_type == 'in': + norm = nn.InstanceNorm1d(hidden_size, affine=True) + elif norm_type == 'gn': + norm = nn.GroupNorm(8, hidden_size) + elif norm_type == 'ln': + norm = LayerNorm(hidden_size, dim=1, eps=ln_eps) + self.last_norm = norm + self.post_net1 = nn.Conv1d(hidden_size, out_dims, kernel_size=post_net_kernel, + padding=post_net_kernel // 2) + if init_weights: + self.apply(init_weights_func) + + def forward(self, x, nonpadding=None): + """ + + :param x: [B, T, H] + :return: [B, T, H] + """ + if self.is_BTC: + x = x.transpose(1, 2) + if nonpadding is None: + nonpadding = (x.abs().sum(1) > 0).float()[:, None, :] + elif self.is_BTC: + nonpadding = nonpadding.transpose(1, 2) + x = self.res_blocks(x) * nonpadding + x = self.last_norm(x) * nonpadding + x = self.post_net1(x) * nonpadding + if self.is_BTC: + x = x.transpose(1, 2) + return x + + +class TextConvEncoder(ConvBlocks): + def __init__(self, dict_size, hidden_size, out_dims, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, init_weights=True, num_layers=None, post_net_kernel=3): + super().__init__(hidden_size, out_dims, dilations, kernel_size, + norm_type, layers_in_block, c_multiple, + dropout, ln_eps, init_weights, num_layers=num_layers, + post_net_kernel=post_net_kernel) + self.embed_tokens = Embedding(dict_size, hidden_size, 0) + self.embed_scale = math.sqrt(hidden_size) + + def forward(self, txt_tokens): + """ + + :param txt_tokens: [B, T] + :return: { + 'encoder_out': [B x T x C] + } + """ + x = self.embed_scale * self.embed_tokens(txt_tokens) + return super().forward(x) + + +class ConditionalConvBlocks(ConvBlocks): + def __init__(self, hidden_size, c_cond, c_out, dilations, kernel_size, + norm_type='ln', layers_in_block=2, c_multiple=2, + dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True, num_layers=None): + super().__init__(hidden_size, c_out, dilations, kernel_size, + norm_type, layers_in_block, c_multiple, + dropout, ln_eps, init_weights, is_BTC=False, num_layers=num_layers) + self.g_prenet = nn.Conv1d(c_cond, hidden_size, 3, padding=1) + self.is_BTC_ = is_BTC + if init_weights: + self.g_prenet.apply(init_weights_func) + + def forward(self, x, cond, nonpadding=None): + if self.is_BTC_: + x = x.transpose(1, 2) + cond = cond.transpose(1, 2) + if nonpadding is not None: + nonpadding = nonpadding.transpose(1, 2) + if nonpadding is None: + nonpadding = x.abs().sum(1)[:, None] + x = x + self.g_prenet(cond) + x = x * nonpadding + x = super(ConditionalConvBlocks, self).forward(x) # input needs to be BTC + if self.is_BTC_: + x = x.transpose(1, 2) + return x diff --git a/NeuralSeq/modules/commons/espnet_positional_embedding.py b/NeuralSeq/modules/commons/espnet_positional_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..74decb6ab300951490ae08a4b93041a0542b5bb7 --- /dev/null +++ b/NeuralSeq/modules/commons/espnet_positional_embedding.py @@ -0,0 +1,113 @@ +import math +import torch + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + reverse (bool): Whether to reverse the input position. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class ScaledPositionalEncoding(PositionalEncoding): + """Scaled positional encoding module. + See Sec. 3.2 https://arxiv.org/abs/1809.08895 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) + self.alpha = torch.nn.Parameter(torch.tensor(1.0)) + + def reset_parameters(self): + """Reset parameters.""" + self.alpha.data = torch.tensor(1.0) + + def forward(self, x): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x + self.alpha * self.pe[:, : x.size(1)] + return self.dropout(x) + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, x): + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[:, : x.size(1)] + return self.dropout(x) + self.dropout(pos_emb) \ No newline at end of file diff --git a/NeuralSeq/modules/commons/normalizing_flow/glow_modules.py b/NeuralSeq/modules/commons/normalizing_flow/glow_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..1120e43133f7b76bf7ac52c120377ce57aae836c --- /dev/null +++ b/NeuralSeq/modules/commons/normalizing_flow/glow_modules.py @@ -0,0 +1,362 @@ +import scipy +from torch.nn import functional as F +import torch +from torch import nn +import numpy as np +from modules.commons.wavenet import WN +from modules.glow import utils + + +class ActNorm(nn.Module): + def __init__(self, channels, ddi=False, **kwargs): + super().__init__() + self.channels = channels + self.initialized = not ddi + + self.logs = nn.Parameter(torch.zeros(1, channels, 1)) + self.bias = nn.Parameter(torch.zeros(1, channels, 1)) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + if x_mask is None: + x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) + x_len = torch.sum(x_mask, [1, 2]) + if not self.initialized: + self.initialize(x, x_mask) + self.initialized = True + + if reverse: + z = (x - self.bias) * torch.exp(-self.logs) * x_mask + logdet = torch.sum(-self.logs) * x_len + else: + z = (self.bias + torch.exp(self.logs) * x) * x_mask + logdet = torch.sum(self.logs) * x_len # [b] + return z, logdet + + def store_inverse(self): + pass + + def set_ddi(self, ddi): + self.initialized = not ddi + + def initialize(self, x, x_mask): + with torch.no_grad(): + denom = torch.sum(x_mask, [0, 2]) + m = torch.sum(x * x_mask, [0, 2]) / denom + m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom + v = m_sq - (m ** 2) + logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) + + bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) + logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) + + self.bias.data.copy_(bias_init) + self.logs.data.copy_(logs_init) + + +class InvConvNear(nn.Module): + def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs): + super().__init__() + assert (n_split % 2 == 0) + self.channels = channels + self.n_split = n_split + self.n_sqz = n_sqz + self.no_jacobian = no_jacobian + + w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] + if torch.det(w_init) < 0: + w_init[:, 0] = -1 * w_init[:, 0] + self.lu = lu + if lu: + # LU decomposition can slightly speed up the inverse + np_p, np_l, np_u = scipy.linalg.lu(w_init) + np_s = np.diag(np_u) + np_sign_s = np.sign(np_s) + np_log_s = np.log(np.abs(np_s)) + np_u = np.triu(np_u, k=1) + l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1) + eye = np.eye(*w_init.shape, dtype=float) + + self.register_buffer('p', torch.Tensor(np_p.astype(float))) + self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) + self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True) + self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True) + self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True) + self.register_buffer('l_mask', torch.Tensor(l_mask)) + self.register_buffer('eye', torch.Tensor(eye)) + else: + self.weight = nn.Parameter(w_init) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + b, c, t = x.size() + assert (c % self.n_split == 0) + if x_mask is None: + x_mask = 1 + x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t + else: + x_len = torch.sum(x_mask, [1, 2]) + + x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t) + x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) + + if self.lu: + self.weight, log_s = self._get_weight() + logdet = log_s.sum() + logdet = logdet * (c / self.n_split) * x_len + else: + logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] + + if reverse: + if hasattr(self, "weight_inv"): + weight = self.weight_inv + else: + weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) + logdet = -logdet + else: + weight = self.weight + if self.no_jacobian: + logdet = 0 + + weight = weight.view(self.n_split, self.n_split, 1, 1) + z = F.conv2d(x, weight) + + z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t) + z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask + return z, logdet + + def _get_weight(self): + l, log_s, u = self.l, self.log_s, self.u + l = l * self.l_mask + self.eye + u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s)) + weight = torch.matmul(self.p, torch.matmul(l, u)) + return weight, log_s + + def store_inverse(self): + weight, _ = self._get_weight() + self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device) + + +class InvConv(nn.Module): + def __init__(self, channels, no_jacobian=False, lu=True, **kwargs): + super().__init__() + w_shape = [channels, channels] + w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float) + LU_decomposed = lu + if not LU_decomposed: + # Sample a random orthogonal matrix: + self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) + else: + np_p, np_l, np_u = scipy.linalg.lu(w_init) + np_s = np.diag(np_u) + np_sign_s = np.sign(np_s) + np_log_s = np.log(np.abs(np_s)) + np_u = np.triu(np_u, k=1) + l_mask = np.tril(np.ones(w_shape, dtype=float), -1) + eye = np.eye(*w_shape, dtype=float) + + self.register_buffer('p', torch.Tensor(np_p.astype(float))) + self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float))) + self.l = nn.Parameter(torch.Tensor(np_l.astype(float))) + self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float))) + self.u = nn.Parameter(torch.Tensor(np_u.astype(float))) + self.l_mask = torch.Tensor(l_mask) + self.eye = torch.Tensor(eye) + self.w_shape = w_shape + self.LU = LU_decomposed + self.weight = None + + def get_weight(self, device, reverse): + w_shape = self.w_shape + self.p = self.p.to(device) + self.sign_s = self.sign_s.to(device) + self.l_mask = self.l_mask.to(device) + self.eye = self.eye.to(device) + l = self.l * self.l_mask + self.eye + u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) + dlogdet = self.log_s.sum() + if not reverse: + w = torch.matmul(self.p, torch.matmul(l, u)) + else: + l = torch.inverse(l.double()).float() + u = torch.inverse(u.double()).float() + w = torch.matmul(u, torch.matmul(l, self.p.inverse())) + return w.view(w_shape[0], w_shape[1], 1), dlogdet + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + """ + log-det = log|abs(|W|)| * pixels + """ + b, c, t = x.size() + if x_mask is None: + x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t + else: + x_len = torch.sum(x_mask, [1, 2]) + logdet = 0 + if not reverse: + weight, dlogdet = self.get_weight(x.device, reverse) + z = F.conv1d(x, weight) + if logdet is not None: + logdet = logdet + dlogdet * x_len + return z, logdet + else: + if self.weight is None: + weight, dlogdet = self.get_weight(x.device, reverse) + else: + weight, dlogdet = self.weight, self.dlogdet + z = F.conv1d(x, weight) + if logdet is not None: + logdet = logdet - dlogdet * x_len + return z, logdet + + def store_inverse(self): + self.weight, self.dlogdet = self.get_weight('cuda', reverse=True) + + +class CouplingBlock(nn.Module): + def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=0, p_dropout=0, sigmoid_scale=False, wn=None): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + self.sigmoid_scale = sigmoid_scale + + start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) + start = torch.nn.utils.weight_norm(start) + self.start = start + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(hidden_channels, in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout) + if wn is not None: + self.wn.in_layers = wn.in_layers + self.wn.res_skip_layers = wn.res_skip_layers + + def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): + if x_mask is None: + x_mask = 1 + x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:] + + x = self.start(x_0) * x_mask + x = self.wn(x, x_mask, g) + out = self.end(x) + + z_0 = x_0 + m = out[:, :self.in_channels // 2, :] + logs = out[:, self.in_channels // 2:, :] + if self.sigmoid_scale: + logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) + if reverse: + z_1 = (x_1 - m) * torch.exp(-logs) * x_mask + logdet = torch.sum(-logs * x_mask, [1, 2]) + else: + z_1 = (m + torch.exp(logs) * x_1) * x_mask + logdet = torch.sum(logs * x_mask, [1, 2]) + z = torch.cat([z_0, z_1], 1) + return z, logdet + + def store_inverse(self): + self.wn.remove_weight_norm() + + +class Glow(nn.Module): + def __init__(self, + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_blocks, + n_layers, + p_dropout=0., + n_split=4, + n_sqz=2, + sigmoid_scale=False, + gin_channels=0, + inv_conv_type='near', + share_cond_layers=False, + share_wn_layers=0, + ): + super().__init__() + + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_blocks = n_blocks + self.n_layers = n_layers + self.p_dropout = p_dropout + self.n_split = n_split + self.n_sqz = n_sqz + self.sigmoid_scale = sigmoid_scale + self.gin_channels = gin_channels + self.share_cond_layers = share_cond_layers + if gin_channels != 0 and share_cond_layers: + cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + wn = None + self.flows = nn.ModuleList() + for b in range(n_blocks): + self.flows.append(ActNorm(channels=in_channels * n_sqz)) + if inv_conv_type == 'near': + self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz)) + if inv_conv_type == 'invconv': + self.flows.append(InvConv(channels=in_channels * n_sqz)) + if share_wn_layers > 0: + if b % share_wn_layers == 0: + wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz, + p_dropout, share_cond_layers) + self.flows.append( + CouplingBlock( + in_channels * n_sqz, + hidden_channels, + kernel_size=kernel_size, + dilation_rate=dilation_rate, + n_layers=n_layers, + gin_channels=gin_channels * n_sqz, + p_dropout=p_dropout, + sigmoid_scale=sigmoid_scale, + wn=wn + )) + + def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False): + logdet_tot = 0 + if not reverse: + flows = self.flows + else: + flows = reversed(self.flows) + if return_hiddens: + hs = [] + if self.n_sqz > 1: + x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz) + if g is not None: + g, _ = utils.squeeze(g, x_mask, self.n_sqz) + x_mask = x_mask_ + if self.share_cond_layers and g is not None: + g = self.cond_layer(g) + for f in flows: + x, logdet = f(x, x_mask, g=g, reverse=reverse) + if return_hiddens: + hs.append(x) + logdet_tot += logdet + if self.n_sqz > 1: + x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz) + if return_hiddens: + return x, logdet_tot, hs + return x, logdet_tot + + def store_inverse(self): + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) + for f in self.flows: + f.store_inverse() diff --git a/NeuralSeq/modules/commons/normalizing_flow/res_flow.py b/NeuralSeq/modules/commons/normalizing_flow/res_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..d0d13285704543ec28fe37d82346011240bdcaf8 --- /dev/null +++ b/NeuralSeq/modules/commons/normalizing_flow/res_flow.py @@ -0,0 +1,61 @@ +import torch +from torch import nn +from modules.commons.conv import ConditionalConvBlocks +from modules.commons.wavenet import WN + + +class FlipLayer(nn.Module): + def forward(self, x, nonpadding, cond=None, reverse=False): + x = torch.flip(x, [1]) + return x + + +class CouplingLayer(nn.Module): + def __init__(self, c_in, hidden_size, kernel_size, n_layers, p_dropout=0, c_in_g=0, nn_type='wn'): + super().__init__() + self.channels = c_in + self.hidden_size = hidden_size + self.kernel_size = kernel_size + self.n_layers = n_layers + self.c_half = c_in // 2 + + self.pre = nn.Conv1d(self.c_half, hidden_size, 1) + if nn_type == 'wn': + self.enc = WN(hidden_size, kernel_size, 1, n_layers, p_dropout=p_dropout, + c_cond=c_in_g) + elif nn_type == 'conv': + self.enc = ConditionalConvBlocks( + hidden_size, c_in_g, hidden_size, None, kernel_size, + layers_in_block=1, is_BTC=False, num_layers=n_layers) + self.post = nn.Conv1d(hidden_size, self.c_half, 1) + + def forward(self, x, nonpadding, cond=None, reverse=False): + x0, x1 = x[:, :self.c_half], x[:, self.c_half:] + x_ = self.pre(x0) * nonpadding + x_ = self.enc(x_, nonpadding=nonpadding, cond=cond) + m = self.post(x_) + x1 = m + x1 if not reverse else x1 - m + x = torch.cat([x0, x1], 1) + return x * nonpadding + + +class ResFlow(nn.Module): + def __init__(self, + c_in, + hidden_size, + kernel_size, + n_flow_layers, + n_flow_steps=4, + c_cond=0, + nn_type='wn'): + super().__init__() + self.flows = nn.ModuleList() + for i in range(n_flow_steps): + self.flows.append( + CouplingLayer(c_in, hidden_size, kernel_size, n_flow_layers, c_in_g=c_cond, nn_type=nn_type)) + self.flows.append(FlipLayer()) + + def forward(self, x, nonpadding, cond=None, reverse=False): + for flow in (self.flows if not reverse else reversed(self.flows)): + x = flow(x, nonpadding, cond=cond, reverse=reverse) + return x diff --git a/NeuralSeq/modules/commons/normalizing_flow/utils.py b/NeuralSeq/modules/commons/normalizing_flow/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7eb56ec514bff822ba1a19a6474207ed82492410 --- /dev/null +++ b/NeuralSeq/modules/commons/normalizing_flow/utils.py @@ -0,0 +1,29 @@ +import torch + + +def squeeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + t = (t // n_sqz) * n_sqz + x = x[:, :, :t] + x_sqz = x.view(b, c, t // n_sqz, n_sqz) + x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) + + if x_mask is not None: + x_mask = x_mask[:, :, n_sqz - 1::n_sqz] + else: + x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) + return x_sqz * x_mask, x_mask + + +def unsqueeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + x_unsqz = x.view(b, n_sqz, c // n_sqz, t) + x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) + + if x_mask is not None: + x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) + else: + x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) + return x_unsqz * x_mask, x_mask diff --git a/NeuralSeq/modules/commons/rel_transformer.py b/NeuralSeq/modules/commons/rel_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ed69e587f9813fc1214dc034f8cabf238e362b61 --- /dev/null +++ b/NeuralSeq/modules/commons/rel_transformer.py @@ -0,0 +1,611 @@ +import math +import torch +from torch import nn +from torch.nn import functional as F +from utils.hparams import hparams +from modules.commons.common_layers import Embedding +from utils.tts_utils import group_hidden_by_segs, expand_word2ph + +import transformers + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def shift_1d(x): + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] + return x + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +class Encoder(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., + window_size=None, block_length=None, pre_ln=False, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + self.block_length = block_length + self.pre_ln = pre_ln + + self.drop = nn.Dropout(p_dropout) + self.attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.attn_layers.append( + MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, + p_dropout=p_dropout, block_length=block_length)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + if pre_ln: + self.last_ln = LayerNorm(hidden_channels) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + x_ = x + if self.pre_ln: + x = self.norm_layers_1[i](x) + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = x_ + y + if not self.pre_ln: + x = self.norm_layers_1[i](x) + + x_ = x + if self.pre_ln: + x = self.norm_layers_2[i](x) + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = x_ + y + if not self.pre_ln: + x = self.norm_layers_2[i](x) + if self.pre_ln: + x = self.last_ln(x) + x = x * x_mask + return x + + +class MultiHeadAttention(nn.Module): + def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., + block_length=None, proximal_bias=False, proximal_init=False): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.window_size = window_size + self.heads_share = heads_share + self.block_length = block_length + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = nn.Conv1d(channels, channels, 1) + self.conv_k = nn.Conv1d(channels, channels, 1) + self.conv_v = nn.Conv1d(channels, channels, 1) + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels ** -0.5 + self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + self.conv_o = nn.Conv1d(channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + nn.init.xavier_uniform_(self.conv_q.weight) + nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + # reshape [b, d, t] -> [b, n_h, t, d_k] + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + if self.window_size is not None: + assert t_s == t_t, "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) + rel_logits = self._relative_position_to_absolute_position(rel_logits) + scores_local = rel_logits / math.sqrt(self.k_channels) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + if self.block_length is not None: + block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) + scores = scores * block_mask + -1e4 * (1 - block_mask) + p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) + output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + """ + x: [b, h, l, m] + y: [h or 1, m, d] + ret: [b, h, l, d] + """ + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + """ + x: [b, h, l, d] + y: [h or 1, m, d] + ret: [b, h, l, m] + """ + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + max_relative_position = 2 * self.window_size + 1 + # Pad first before slice to avoid using cond ops. + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = F.pad( + relative_embeddings, + convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + """ + x: [b, h, l, 2*l-1] + ret: [b, h, l, l] + """ + batch, heads, length, _ = x.size() + # Concat columns of pad to shift from relative to absolute indexing. + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) + + # Concat extra elements so to add up to shape (len+1, 2*len-1). + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) + + # Reshape and slice out the padded elements. + x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] + return x_final + + def _absolute_position_to_relative_position(self, x): + """ + x: [b, h, l, l] + ret: [b, h, l, 2*l-1] + """ + batch, heads, length, _ = x.size() + # padd along column + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) + x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) + # add 0's in the beginning that will skew the elements after reshape + x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] + return x_final + + def _attention_bias_proximal(self, length): + """Bias for self-attention to encourage attention to close positions. + Args: + length: an integer scalar. + Returns: + a Tensor with shape [1, 1, length, length] + """ + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.activation = activation + + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + if self.activation == "gelu": + x = x * torch.sigmoid(1.702 * x) + else: + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + assert n_layers > 1, "Number of layers should be larger than 0." + + self.conv_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = nn.Sequential( + nn.ReLU(), + nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class RelTransformerEncoder(nn.Module): + def __init__(self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout=0.0, + window_size=4, + block_length=None, + prenet=True, + pre_ln=True, + ): + + super().__init__() + + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + self.block_length = block_length + self.prenet = prenet + if n_vocab > 0: + self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) + + if prenet: + self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, + kernel_size=5, n_layers=3, p_dropout=0) + self.encoder = Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + window_size=window_size, + block_length=block_length, + pre_ln=pre_ln, + ) + + def forward(self, x, x_mask=None): + if self.n_vocab > 0: + x_lengths = (x > 0).long().sum(-1) + x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] + else: + x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + if self.prenet: + x = self.pre(x, x_mask) + x = self.encoder(x, x_mask) + return x.transpose(1, 2) + + +class Pooler(nn.Module): + """ + Parameter-free poolers to get the sentence embedding + 'cls': [CLS] representation with BERT/RoBERTa's MLP pooler. + 'cls_before_pooler': [CLS] representation without the original MLP pooler. + 'avg': average of the last layers' hidden states at each token. + 'avg_top2': average of the last two layers. + 'avg_first_last': average of the first and the last layers. + """ + def __init__(self, pooler_type): + super().__init__() + self.pooler_type = pooler_type + assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type + + def forward(self, attention_mask, outputs): + last_hidden = outputs.last_hidden_state + pooler_output = outputs.pooler_output + hidden_states = outputs.hidden_states + + if self.pooler_type in ['cls_before_pooler', 'cls']: + return last_hidden[:, 0] + elif self.pooler_type == "avg": + return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)) + elif self.pooler_type == "avg_first_last": + first_hidden = hidden_states[0] + last_hidden = hidden_states[-1] + pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) + return pooled_result + elif self.pooler_type == "avg_top2": + second_last_hidden = hidden_states[-2] + last_hidden = hidden_states[-1] + pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) + return pooled_result + else: + raise NotImplementedError + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + self.record = None + self.pos_avg = 0.0 + self.neg_avg = 0.0 + + def forward(self, x, y): + sim = self.cos(x, y) + self.record = sim.detach() # [64,64] + min_size = min(self.record.shape[0], self.record.shape[1]) # 64 + num_item = self.record.shape[0] * self.record.shape[1] # 4096 + self.pos_avg = self.record.diag().sum() / min_size + if num_item - min_size == 0: + self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1 + return sim / self.temp + if torch.any(torch.isnan(self.record)).item() is True: + print("we got self.record has nan when compute neg_avg") + if torch.any(torch.isnan(self.record.diag())).item() is True: + print("we got self.record.diag() has nan when compute neg_avg") + self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size) + + return sim / self.temp + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.dense = nn.Linear(hidden_size, hidden_size) + self.transform_act_fn = F.gelu + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, hid_dim, out_dim): + super().__init__() + self.transform = BertPredictionHeadTransform(hid_dim) + self.decoder = nn.Linear(hid_dim, out_dim, bias=False) + self.bias = nn.Parameter(torch.zeros(out_dim)) + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# V2_2 +# change add to concat. +# now support finetune BERT +# grad_bert=0.1 & trainable_block_idx=0 +class BERTRelTransformerEncoder(nn.Module): + def __init__(self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout=0.0, + window_size=4, + block_length=None, + prenet=True, + pre_ln=True, + ): + + super().__init__() + + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + self.block_length = block_length + self.prenet = prenet + if n_vocab > 0: + self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) + + if prenet: + self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, + kernel_size=5, n_layers=3, p_dropout=0) + self.encoder1 = Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers//2, + kernel_size, + p_dropout, + window_size=window_size, + block_length=block_length, + pre_ln=pre_ln, + ) + + self.encoder2 = Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers - n_layers//2, + kernel_size, + p_dropout, + window_size=window_size, + block_length=block_length, + pre_ln=pre_ln, + ) + + if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']: + model_name = 'bert-base-uncased' + elif hparams['ds_name'] in ['biaobei', 'wenetspeech']: + model_name = 'bert-base-chinese' + else: + raise NotImplementedError() + + self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) + config = transformers.AutoConfig.from_pretrained(model_name) + if hparams.get("load_bert_from_pretrained", True): + print("Load BERT from pretrained model ...") + self.bert = transformers.AutoModel.from_pretrained(model_name,config=config) + trainable_start_block = hparams.get("bert_trainable_start_block", 0) + else: + print("Initialize BERT from scratch!") + self.bert = transformers.BertModel(config=config) + trainable_start_block = 0 + + for k, v in self.bert.named_parameters(): + if 'embeddings' in k: + v.requires_grad = False + elif 'encoder.layer' in k: + block_idx = int(k.split(".")[2]) + if block_idx < trainable_start_block: + v.requires_grad = False + else: + v.requires_grad = True + elif 'cls' in k: + v.requires_grad = True + else: + print("Unhandled key: {}, set to requires_grad...".format(k)) + v.requires_grad = True + + self.bert_combine = nn.Sequential(*[ + nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1), + nn.ReLU(), + ]) + self.pooler = Pooler("avg") + self.sim = Similarity(temp=0.05) + + def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs): + if self.n_vocab > 0: + x_lengths = (x > 0).long().sum(-1) + x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] + else: + x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + if self.prenet: + x = self.pre(x, x_mask) + x = self.encoder1(x, x_mask) + bert_outputs = self.bert(bert_feats['bert_input_ids'], + attention_mask=bert_feats['bert_attention_mask'], + token_type_ids=bert_feats['bert_token_type_ids'], + output_hidden_states=True) + bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert + bert_embedding = bert_outputs['hidden_states'][bert_num_blocks] + # bert_embedding = bert_outputs['last_hidden_state'] + grad_bert = hparams.get("grad_bert", 0.1) + bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert + bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item()) + bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word) + bert_ph_embedding = bert_ph_embedding.transpose(1,2) + x = torch.cat([x, bert_ph_embedding], dim=1) + x = self.bert_combine(x) + x = self.encoder2(x, x_mask) + return x.transpose(1, 2) + + diff --git a/NeuralSeq/modules/commons/ssim.py b/NeuralSeq/modules/commons/ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..0d0241f267ef58b24979e022b05f2a9adf768826 --- /dev/null +++ b/NeuralSeq/modules/commons/ssim.py @@ -0,0 +1,391 @@ +# ''' +# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py +# ''' +# +# import torch +# import torch.jit +# import torch.nn.functional as F +# +# +# @torch.jit.script +# def create_window(window_size: int, sigma: float, channel: int): +# ''' +# Create 1-D gauss kernel +# :param window_size: the size of gauss kernel +# :param sigma: sigma of normal distribution +# :param channel: input channel +# :return: 1D kernel +# ''' +# coords = torch.arange(window_size, dtype=torch.float) +# coords -= window_size // 2 +# +# g = torch.exp(-(coords ** 2) / (2 * sigma ** 2)) +# g /= g.sum() +# +# g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1) +# return g +# +# +# @torch.jit.script +# def _gaussian_filter(x, window_1d, use_padding: bool): +# ''' +# Blur input with 1-D kernel +# :param x: batch of tensors to be blured +# :param window_1d: 1-D gauss kernel +# :param use_padding: padding image before conv +# :return: blured tensors +# ''' +# C = x.shape[1] +# padding = 0 +# if use_padding: +# window_size = window_1d.shape[3] +# padding = window_size // 2 +# out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C) +# out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C) +# return out +# +# +# @torch.jit.script +# def ssim(X, Y, window, data_range: float, use_padding: bool = False): +# ''' +# Calculate ssim index for X and Y +# :param X: images [B, C, H, N_bins] +# :param Y: images [B, C, H, N_bins] +# :param window: 1-D gauss kernel +# :param data_range: value range of input images. (usually 1.0 or 255) +# :param use_padding: padding image before conv +# :return: +# ''' +# +# K1 = 0.01 +# K2 = 0.03 +# compensation = 1.0 +# +# C1 = (K1 * data_range) ** 2 +# C2 = (K2 * data_range) ** 2 +# +# mu1 = _gaussian_filter(X, window, use_padding) +# mu2 = _gaussian_filter(Y, window, use_padding) +# sigma1_sq = _gaussian_filter(X * X, window, use_padding) +# sigma2_sq = _gaussian_filter(Y * Y, window, use_padding) +# sigma12 = _gaussian_filter(X * Y, window, use_padding) +# +# mu1_sq = mu1.pow(2) +# mu2_sq = mu2.pow(2) +# mu1_mu2 = mu1 * mu2 +# +# sigma1_sq = compensation * (sigma1_sq - mu1_sq) +# sigma2_sq = compensation * (sigma2_sq - mu2_sq) +# sigma12 = compensation * (sigma12 - mu1_mu2) +# +# cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2) +# # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan. +# cs_map = cs_map.clamp_min(0.) +# ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map +# +# ssim_val = ssim_map.mean(dim=(1, 2, 3)) # reduce along CHW +# cs = cs_map.mean(dim=(1, 2, 3)) +# +# return ssim_val, cs +# +# +# @torch.jit.script +# def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8): +# ''' +# interface of ms-ssim +# :param X: a batch of images, (N,C,H,W) +# :param Y: a batch of images, (N,C,H,W) +# :param window: 1-D gauss kernel +# :param data_range: value range of input images. (usually 1.0 or 255) +# :param weights: weights for different levels +# :param use_padding: padding image before conv +# :param eps: use for avoid grad nan. +# :return: +# ''' +# levels = weights.shape[0] +# cs_vals = [] +# ssim_vals = [] +# for _ in range(levels): +# ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding) +# # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. +# ssim_val = ssim_val.clamp_min(eps) +# cs = cs.clamp_min(eps) +# cs_vals.append(cs) +# +# ssim_vals.append(ssim_val) +# padding = (X.shape[2] % 2, X.shape[3] % 2) +# X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding) +# Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding) +# +# cs_vals = torch.stack(cs_vals, dim=0) +# ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0) +# return ms_ssim_val +# +# +# class SSIM(torch.jit.ScriptModule): +# __constants__ = ['data_range', 'use_padding'] +# +# def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False): +# ''' +# :param window_size: the size of gauss kernel +# :param window_sigma: sigma of normal distribution +# :param data_range: value range of input images. (usually 1.0 or 255) +# :param channel: input channels (default: 3) +# :param use_padding: padding image before conv +# ''' +# super().__init__() +# assert window_size % 2 == 1, 'Window size must be odd.' +# window = create_window(window_size, window_sigma, channel) +# self.register_buffer('window', window) +# self.data_range = data_range +# self.use_padding = use_padding +# +# @torch.jit.script_method +# def forward(self, X, Y): +# r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding) +# return r[0] +# +# +# class MS_SSIM(torch.jit.ScriptModule): +# __constants__ = ['data_range', 'use_padding', 'eps'] +# +# def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None, +# levels=None, eps=1e-8): +# ''' +# class for ms-ssim +# :param window_size: the size of gauss kernel +# :param window_sigma: sigma of normal distribution +# :param data_range: value range of input images. (usually 1.0 or 255) +# :param channel: input channels +# :param use_padding: padding image before conv +# :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]) +# :param levels: number of downsampling +# :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. +# ''' +# super().__init__() +# assert window_size % 2 == 1, 'Window size must be odd.' +# self.data_range = data_range +# self.use_padding = use_padding +# self.eps = eps +# +# window = create_window(window_size, window_sigma, channel) +# self.register_buffer('window', window) +# +# if weights is None: +# weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] +# weights = torch.tensor(weights, dtype=torch.float) +# +# if levels is not None: +# weights = weights[:levels] +# weights = weights / weights.sum() +# +# self.register_buffer('weights', weights) +# +# @torch.jit.script_method +# def forward(self, X, Y): +# return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights, +# use_padding=self.use_padding, eps=self.eps) +# +# +# if __name__ == '__main__': +# print('Simple Test') +# im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda') +# img1 = im / 255 +# img2 = img1 * 0.5 +# +# losser = SSIM(data_range=1.).cuda() +# loss = losser(img1, img2).mean() +# +# losser2 = MS_SSIM(data_range=1.).cuda() +# loss2 = losser2(img1, img2).mean() +# +# print(loss.item()) +# print(loss2.item()) +# +# if __name__ == '__main__': +# print('Training Test') +# import cv2 +# import torch.optim +# import numpy as np +# import imageio +# import time +# +# out_test_video = False +# # 最好不要直接输出gif图，会非常大，最好先输出mkv文件后用ffmpeg转换到GIF +# video_use_gif = False +# +# im = cv2.imread('test_img1.jpg', 1) +# t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255. +# +# if out_test_video: +# if video_use_gif: +# fps = 0.5 +# out_wh = (im.shape[1] // 2, im.shape[0] // 2) +# suffix = '.gif' +# else: +# fps = 5 +# out_wh = (im.shape[1], im.shape[0]) +# suffix = '.mkv' +# video_last_time = time.perf_counter() +# video = imageio.get_writer('ssim_test' + suffix, fps=fps) +# +# # 测试ssim +# print('Training SSIM') +# rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. +# rand_im.requires_grad = True +# optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) +# losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda() +# ssim_score = 0 +# while ssim_score < 0.999: +# optim.zero_grad() +# loss = losser(rand_im, t_im) +# (-loss).sum().backward() +# ssim_score = loss.item() +# optim.step() +# r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] +# r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) +# +# if out_test_video: +# if time.perf_counter() - video_last_time > 1. / fps: +# video_last_time = time.perf_counter() +# out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) +# out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) +# if isinstance(out_frame, cv2.UMat): +# out_frame = out_frame.get() +# video.append_data(out_frame) +# +# cv2.imshow('ssim', r_im) +# cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score) +# cv2.waitKey(1) +# +# if out_test_video: +# video.close() +# +# # 测试ms_ssim +# if out_test_video: +# if video_use_gif: +# fps = 0.5 +# out_wh = (im.shape[1] // 2, im.shape[0] // 2) +# suffix = '.gif' +# else: +# fps = 5 +# out_wh = (im.shape[1], im.shape[0]) +# suffix = '.mkv' +# video_last_time = time.perf_counter() +# video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps) +# +# print('Training MS_SSIM') +# rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. +# rand_im.requires_grad = True +# optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) +# losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda() +# ssim_score = 0 +# while ssim_score < 0.999: +# optim.zero_grad() +# loss = losser(rand_im, t_im) +# (-loss).sum().backward() +# ssim_score = loss.item() +# optim.step() +# r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] +# r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) +# +# if out_test_video: +# if time.perf_counter() - video_last_time > 1. / fps: +# video_last_time = time.perf_counter() +# out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) +# out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) +# if isinstance(out_frame, cv2.UMat): +# out_frame = out_frame.get() +# video.append_data(out_frame) +# +# cv2.imshow('ms_ssim', r_im) +# cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score) +# cv2.waitKey(1) +# +# if out_test_video: +# video.close() + +""" +Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim +""" + +import torch +import torch.nn.functional as F +from torch.autograd import Variable +import numpy as np +from math import exp + + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) + return window + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1) + + +class SSIM(torch.nn.Module): + def __init__(self, window_size=11, size_average=True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window(window_size, self.channel) + + def forward(self, img1, img2): + (_, channel, _, _) = img1.size() + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = create_window(self.window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return _ssim(img1, img2, window, self.window_size, channel, self.size_average) + + +window = None + + +def ssim(img1, img2, window_size=11, size_average=True): + (_, channel, _, _) = img1.size() + global window + if window is None: + window = create_window(window_size, channel) + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + return _ssim(img1, img2, window, window_size, channel, size_average) diff --git a/NeuralSeq/modules/commons/transformer.py b/NeuralSeq/modules/commons/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..9234b7928450974ae39843aa6c870400a5d31b1c --- /dev/null +++ b/NeuralSeq/modules/commons/transformer.py @@ -0,0 +1,747 @@ +import math +import torch +from torch import nn +from torch.nn import Parameter, Linear +from modules.commons.common_layers import LayerNorm, Embedding +from utils.tts_utils import get_incremental_state, set_incremental_state, softmax, make_positions +import torch.nn.functional as F + +DEFAULT_MAX_SOURCE_POSITIONS = 2000 +DEFAULT_MAX_TARGET_POSITIONS = 2000 + + +class SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = SinusoidalPositionalEmbedding.get_embedding( + init_size, + embedding_dim, + padding_idx, + ) + self.register_buffer('_float_tensor', torch.FloatTensor(1)) + + @staticmethod + def get_embedding(num_embeddings, embedding_dim, padding_idx=None): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs): + """Input is expected to be of size [bsz x seqlen].""" + bsz, seq_len = input.shape[:2] + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + # recompute/expand embeddings if needed + self.weights = SinusoidalPositionalEmbedding.get_embedding( + max_pos, + self.embedding_dim, + self.padding_idx, + ) + self.weights = self.weights.to(self._float_tensor) + + if incremental_state is not None: + # positions is the same for every token when decoding a single step + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) + + positions = make_positions(input, self.padding_idx) if positions is None else positions + return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() + + def max_positions(self): + """Maximum number of supported positions.""" + return int(1e5) # an arbitrary large number + + +class TransformerFFNLayer(nn.Module): + def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'): + super().__init__() + self.kernel_size = kernel_size + self.dropout = dropout + self.act = act + if padding == 'SAME': + self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2) + elif padding == 'LEFT': + self.ffn_1 = nn.Sequential( + nn.ConstantPad1d((kernel_size - 1, 0), 0.0), + nn.Conv1d(hidden_size, filter_size, kernel_size) + ) + self.ffn_2 = Linear(filter_size, hidden_size) + + def forward(self, x, incremental_state=None): + # x: T x B x C + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if 'prev_input' in saved_state: + prev_input = saved_state['prev_input'] + x = torch.cat((prev_input, x), dim=0) + x = x[-self.kernel_size:] + saved_state['prev_input'] = x + self._set_input_buffer(incremental_state, saved_state) + + x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1) + x = x * self.kernel_size ** -0.5 + + if incremental_state is not None: + x = x[-1:] + if self.act == 'gelu': + x = F.gelu(x) + if self.act == 'relu': + x = F.relu(x) + x = F.dropout(x, self.dropout, training=self.training) + x = self.ffn_2(x) + return x + + def _get_input_buffer(self, incremental_state): + return get_incremental_state( + self, + incremental_state, + 'f', + ) or {} + + def _set_input_buffer(self, incremental_state, buffer): + set_incremental_state( + self, + incremental_state, + 'f', + buffer, + ) + + def clear_buffer(self, incremental_state): + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if 'prev_input' in saved_state: + del saved_state['prev_input'] + self._set_input_buffer(incremental_state, saved_state) + + +class MultiheadAttention(nn.Module): + def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, + add_bias_kv=False, add_zero_attn=False, self_attention=False, + encoder_decoder_attention=False): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim ** -0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \ + 'value to be of the same size' + + if self.qkv_same_dim: + self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim)) + else: + self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) + self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) + self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) + + if bias: + self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim)) + else: + self.register_parameter('in_proj_bias', None) + + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + self.enable_torch_version = False + if hasattr(F, "multi_head_attention_forward"): + self.enable_torch_version = True + else: + self.enable_torch_version = False + self.last_attn_probs = None + + def reset_parameters(self): + if self.qkv_same_dim: + nn.init.xavier_uniform_(self.in_proj_weight) + else: + nn.init.xavier_uniform_(self.k_proj_weight) + nn.init.xavier_uniform_(self.v_proj_weight) + nn.init.xavier_uniform_(self.q_proj_weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.in_proj_bias is not None: + nn.init.constant_(self.in_proj_bias, 0.) + nn.init.constant_(self.out_proj.bias, 0.) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def forward( + self, + query, key, value, + key_padding_mask=None, + incremental_state=None, + need_weights=True, + static_kv=False, + attn_mask=None, + before_softmax=False, + need_head_weights=False, + enc_dec_attn_constraint_mask=None, + reset_attn_weight=None + ): + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None: + if self.qkv_same_dim: + return F.multi_head_attention_forward(query, key, value, + self.embed_dim, self.num_heads, + self.in_proj_weight, + self.in_proj_bias, self.bias_k, self.bias_v, + self.add_zero_attn, self.dropout, + self.out_proj.weight, self.out_proj.bias, + self.training, key_padding_mask, need_weights, + attn_mask) + else: + return F.multi_head_attention_forward(query, key, value, + self.embed_dim, self.num_heads, + torch.empty([0]), + self.in_proj_bias, self.bias_k, self.bias_v, + self.add_zero_attn, self.dropout, + self.out_proj.weight, self.out_proj.bias, + self.training, key_padding_mask, need_weights, + attn_mask, use_separate_proj_weight=True, + q_proj_weight=self.q_proj_weight, + k_proj_weight=self.k_proj_weight, + v_proj_weight=self.v_proj_weight) + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if 'prev_key' in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + # self-attention + q, k, v = self.in_proj_qkv(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.in_proj_q(query) + if key is None: + assert value is None + k = v = None + else: + k = self.in_proj_k(key) + v = self.in_proj_v(key) + + else: + q = self.in_proj_q(query) + k = self.in_proj_k(key) + v = self.in_proj_v(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1) + + q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if k is not None: + k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if v is not None: + v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if 'prev_key' in saved_state: + prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + k = torch.cat((prev_key, k), dim=1) + if 'prev_value' in saved_state: + prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + v = torch.cat((prev_value, v), dim=1) + if 'prev_key_padding_mask' in saved_state and saved_state['prev_key_padding_mask'] is not None: + prev_key_padding_mask = saved_state['prev_key_padding_mask'] + if static_kv: + key_padding_mask = prev_key_padding_mask + else: + key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1) + + saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state['prev_key_padding_mask'] = key_padding_mask + + self._set_input_buffer(incremental_state, saved_state) + + src_len = k.size(1) + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]): + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + if len(attn_mask.shape) == 2: + attn_mask = attn_mask.unsqueeze(0) + elif len(attn_mask.shape) == 3: + attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape( + bsz * self.num_heads, tgt_len, src_len) + attn_weights = attn_weights + attn_mask + + if enc_dec_attn_constraint_mask is not None: # bs x head x L_kv + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.masked_fill( + enc_dec_attn_constraint_mask.unsqueeze(2).bool(), + -1e8, + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + -1e8, + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = softmax(attn_weights, dim=-1) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training) + + if reset_attn_weight is not None: + if reset_attn_weight: + self.last_attn_probs = attn_probs.detach() + else: + assert self.last_attn_probs is not None + attn_probs = self.last_attn_probs + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + + if need_weights: + attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + else: + attn_weights = None + + return attn, (attn_weights, attn_logits) + + def in_proj_qkv(self, query): + return self._in_proj(query).chunk(3, dim=-1) + + def in_proj_q(self, query): + if self.qkv_same_dim: + return self._in_proj(query, end=self.embed_dim) + else: + bias = self.in_proj_bias + if bias is not None: + bias = bias[:self.embed_dim] + return F.linear(query, self.q_proj_weight, bias) + + def in_proj_k(self, key): + if self.qkv_same_dim: + return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim) + else: + weight = self.k_proj_weight + bias = self.in_proj_bias + if bias is not None: + bias = bias[self.embed_dim:2 * self.embed_dim] + return F.linear(key, weight, bias) + + def in_proj_v(self, value): + if self.qkv_same_dim: + return self._in_proj(value, start=2 * self.embed_dim) + else: + weight = self.v_proj_weight + bias = self.in_proj_bias + if bias is not None: + bias = bias[2 * self.embed_dim:] + return F.linear(value, weight, bias) + + def _in_proj(self, input, start=0, end=None): + weight = self.in_proj_weight + bias = self.in_proj_bias + weight = weight[start:end, :] + if bias is not None: + bias = bias[start:end] + return F.linear(input, weight, bias) + + def _get_input_buffer(self, incremental_state): + return get_incremental_state( + self, + incremental_state, + 'attn_state', + ) or {} + + def _set_input_buffer(self, incremental_state, buffer): + set_incremental_state( + self, + incremental_state, + 'attn_state', + buffer, + ) + + def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): + return attn_weights + + def clear_buffer(self, incremental_state=None): + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if 'prev_key' in saved_state: + del saved_state['prev_key'] + if 'prev_value' in saved_state: + del saved_state['prev_value'] + self._set_input_buffer(incremental_state, saved_state) + + +class EncSALayer(nn.Module): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, + relu_dropout=0.1, kernel_size=9, padding='SAME', act='gelu'): + super().__init__() + self.c = c + self.dropout = dropout + self.num_heads = num_heads + if num_heads > 0: + self.layer_norm1 = LayerNorm(c) + self.self_attn = MultiheadAttention( + self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False) + self.layer_norm2 = LayerNorm(c) + self.ffn = TransformerFFNLayer( + c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act) + + def forward(self, x, encoder_padding_mask=None, **kwargs): + layer_norm_training = kwargs.get('layer_norm_training', None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + if self.num_heads > 0: + residual = x + x = self.layer_norm1(x) + x, _, = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask + ) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] + + residual = x + x = self.layer_norm2(x) + x = self.ffn(x) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None] + return x + + +class DecSALayer(nn.Module): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, + kernel_size=9, act='gelu'): + super().__init__() + self.c = c + self.dropout = dropout + self.layer_norm1 = LayerNorm(c) + self.self_attn = MultiheadAttention( + c, num_heads, self_attention=True, dropout=attention_dropout, bias=False + ) + self.layer_norm2 = LayerNorm(c) + self.encoder_attn = MultiheadAttention( + c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False, + ) + self.layer_norm3 = LayerNorm(c) + self.ffn = TransformerFFNLayer( + c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act) + + def forward( + self, + x, + encoder_out=None, + encoder_padding_mask=None, + incremental_state=None, + self_attn_mask=None, + self_attn_padding_mask=None, + attn_out=None, + reset_attn_weight=None, + **kwargs, + ): + layer_norm_training = kwargs.get('layer_norm_training', None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + self.layer_norm3.training = layer_norm_training + residual = x + x = self.layer_norm1(x) + x, _ = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + attn_mask=self_attn_mask + ) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + + attn_logits = None + if encoder_out is not None or attn_out is not None: + residual = x + x = self.layer_norm2(x) + if encoder_out is not None: + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + enc_dec_attn_constraint_mask=get_incremental_state(self, incremental_state, + 'enc_dec_attn_constraint_mask'), + reset_attn_weight=reset_attn_weight + ) + attn_logits = attn[1] + elif attn_out is not None: + x = self.encoder_attn.in_proj_v(attn_out) + if encoder_out is not None or attn_out is not None: + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + + residual = x + x = self.layer_norm3(x) + x = self.ffn(x, incremental_state=incremental_state) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + return x, attn_logits + + def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None): + self.encoder_attn.clear_buffer(incremental_state) + self.ffn.clear_buffer(incremental_state) + + def set_buffer(self, name, tensor, incremental_state): + return set_incremental_state(self, incremental_state, name, tensor) + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2): + super().__init__() + self.hidden_size = hidden_size + self.dropout = dropout + self.num_heads = num_heads + self.op = EncSALayer( + hidden_size, num_heads, dropout=dropout, + attention_dropout=0.0, relu_dropout=dropout, + kernel_size=kernel_size) + + def forward(self, x, **kwargs): + return self.op(x, **kwargs) + + +class TransformerDecoderLayer(nn.Module): + def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2): + super().__init__() + self.hidden_size = hidden_size + self.dropout = dropout + self.num_heads = num_heads + self.op = DecSALayer( + hidden_size, num_heads, dropout=dropout, + attention_dropout=0.0, relu_dropout=dropout, + kernel_size=kernel_size) + + def forward(self, x, **kwargs): + return self.op(x, **kwargs) + + def clear_buffer(self, *args): + return self.op.clear_buffer(*args) + + def set_buffer(self, *args): + return self.op.set_buffer(*args) + + +class FFTBlocks(nn.Module): + def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=0.0, + num_heads=2, use_pos_embed=True, use_last_norm=True, + use_pos_embed_alpha=True): + super().__init__() + self.num_layers = num_layers + embed_dim = self.hidden_size = hidden_size + self.dropout = dropout + self.use_pos_embed = use_pos_embed + self.use_last_norm = use_last_norm + if use_pos_embed: + self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS + self.padding_idx = 0 + self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1 + self.embed_positions = SinusoidalPositionalEmbedding( + embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, + ) + + self.layers = nn.ModuleList([]) + self.layers.extend([ + TransformerEncoderLayer(self.hidden_size, self.dropout, + kernel_size=ffn_kernel_size, num_heads=num_heads) + for _ in range(self.num_layers) + ]) + if self.use_last_norm: + self.layer_norm = nn.LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False): + """ + :param x: [B, T, C] + :param padding_mask: [B, T] + :return: [B, T, C] or [L, B, T, C] + """ + padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask + nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] + if self.use_pos_embed: + positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + # B x T x C -> T x B x C + x = x.transpose(0, 1) * nonpadding_mask_TB + hiddens = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB + hiddens.append(x) + if self.use_last_norm: + x = self.layer_norm(x) * nonpadding_mask_TB + if return_hiddens: + x = torch.stack(hiddens, 0) # [L, T, B, C] + x = x.transpose(1, 2) # [L, B, T, C] + else: + x = x.transpose(0, 1) # [B, T, C] + return x + + +class FastSpeechEncoder(FFTBlocks): + def __init__(self, dict_size, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2, + dropout=0.0): + super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads, + use_pos_embed=False, dropout=dropout) # use_pos_embed_alpha for compatibility + self.embed_tokens = Embedding(dict_size, hidden_size, 0) + self.embed_scale = math.sqrt(hidden_size) + self.padding_idx = 0 + self.embed_positions = SinusoidalPositionalEmbedding( + hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, + ) + + def forward(self, txt_tokens, attn_mask=None): + """ + + :param txt_tokens: [B, T] + :return: { + 'encoder_out': [B x T x C] + } + """ + encoder_padding_mask = txt_tokens.eq(self.padding_idx).data + x = self.forward_embedding(txt_tokens) # [B, T, H] + if self.num_layers > 0: + x = super(FastSpeechEncoder, self).forward(x, encoder_padding_mask, attn_mask=attn_mask) + return x + + def forward_embedding(self, txt_tokens): + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(txt_tokens) + if self.use_pos_embed: + positions = self.embed_positions(txt_tokens) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + return x + + +class FastSpeechDecoder(FFTBlocks): + def __init__(self, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2): + super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) diff --git a/NeuralSeq/modules/commons/wavenet.py b/NeuralSeq/modules/commons/wavenet.py new file mode 100644 index 0000000000000000000000000000000000000000..7809c9b9d3331ba4fd2ffd4caae14e721e4b0732 --- /dev/null +++ b/NeuralSeq/modules/commons/wavenet.py @@ -0,0 +1,97 @@ +import torch +from torch import nn + + +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +class WN(torch.nn.Module): + def __init__(self, hidden_size, kernel_size, dilation_rate, n_layers, c_cond=0, + p_dropout=0, share_cond_layers=False, is_BTC=False): + super(WN, self).__init__() + assert (kernel_size % 2 == 1) + assert (hidden_size % 2 == 0) + self.is_BTC = is_BTC + self.hidden_size = hidden_size + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = c_cond + self.p_dropout = p_dropout + self.share_cond_layers = share_cond_layers + + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + self.drop = nn.Dropout(p_dropout) + + if c_cond != 0 and not share_cond_layers: + cond_layer = torch.nn.Conv1d(c_cond, 2 * hidden_size * n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = dilation_rate ** i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d(hidden_size, 2 * hidden_size, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * hidden_size + else: + res_skip_channels = hidden_size + + res_skip_layer = torch.nn.Conv1d(hidden_size, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, x, nonpadding=None, cond=None): + if self.is_BTC: + x = x.transpose(1, 2) + cond = cond.transpose(1, 2) if cond is not None else None + nonpadding = nonpadding.transpose(1, 2) if nonpadding is not None else None + if nonpadding is None: + nonpadding = 1 + output = torch.zeros_like(x) + n_channels_tensor = torch.IntTensor([self.hidden_size]) + + if cond is not None and not self.share_cond_layers: + cond = self.cond_layer(cond) + + for i in range(self.n_layers): + x_in = self.in_layers[i](x) + x_in = self.drop(x_in) + if cond is not None: + cond_offset = i * 2 * self.hidden_size + cond_l = cond[:, cond_offset:cond_offset + 2 * self.hidden_size, :] + else: + cond_l = torch.zeros_like(x_in) + + acts = fused_add_tanh_sigmoid_multiply(x_in, cond_l, n_channels_tensor) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + x = (x + res_skip_acts[:, :self.hidden_size, :]) * nonpadding + output = output + res_skip_acts[:, self.hidden_size:, :] + else: + output = output + res_skip_acts + output = output * nonpadding + if self.is_BTC: + output = output.transpose(1, 2) + return output + + def remove_weight_norm(self): + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) diff --git a/NeuralSeq/modules/diff/__pycache__/candidate_decoder.cpython-37.pyc b/NeuralSeq/modules/diff/__pycache__/candidate_decoder.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b068efacde23774472f944f9a353151c0ef94a4d Binary files /dev/null and b/NeuralSeq/modules/diff/__pycache__/candidate_decoder.cpython-37.pyc differ diff --git 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/dev/null and b/NeuralSeq/modules/diff/__pycache__/shallow_diffusion_tts.cpython-37.pyc differ diff --git a/NeuralSeq/modules/diff/__pycache__/shallow_diffusion_tts.cpython-38.pyc b/NeuralSeq/modules/diff/__pycache__/shallow_diffusion_tts.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..67cb3c5d48cd15edc128cc7098dd6f119d0c4237 Binary files /dev/null and b/NeuralSeq/modules/diff/__pycache__/shallow_diffusion_tts.cpython-38.pyc differ diff --git a/NeuralSeq/modules/diff/candidate_decoder.py b/NeuralSeq/modules/diff/candidate_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..133a51a61942027c255841e2638e296238c07a30 --- /dev/null +++ b/NeuralSeq/modules/diff/candidate_decoder.py @@ -0,0 +1,96 @@ +from modules.fastspeech.tts_modules import FastspeechDecoder +# from modules.fastspeech.fast_tacotron import DecoderRNN +# from modules.fastspeech.speedy_speech.speedy_speech import ConvBlocks +# from modules.fastspeech.conformer.conformer import ConformerDecoder +import torch +from torch.nn import functional as F +import torch.nn as nn +import math +from utils.hparams import hparams +from .diffusion import Mish +Linear = nn.Linear + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +def Conv1d(*args, **kwargs): + layer = nn.Conv1d(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +class FFT(FastspeechDecoder): + def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None): + super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) + dim = hparams['residual_channels'] + self.input_projection = Conv1d(hparams['audio_num_mel_bins'], dim, 1) + self.diffusion_embedding = SinusoidalPosEmb(dim) + self.mlp = nn.Sequential( + nn.Linear(dim, dim * 4), + Mish(), + nn.Linear(dim * 4, dim) + ) + self.get_mel_out = Linear(hparams['hidden_size'], 80, bias=True) + self.get_decode_inp = Linear(hparams['hidden_size'] + dim + dim, + hparams['hidden_size']) # hs + dim + 80 -> hs + + def forward(self, spec, diffusion_step, cond, padding_mask=None, attn_mask=None, return_hiddens=False): + """ + :param spec: [B, 1, 80, T] + :param diffusion_step: [B, 1] + :param cond: [B, M, T] + :return: + """ + x = spec[:, 0] + x = self.input_projection(x).permute([0, 2, 1]) # [B, T, residual_channel] + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) # [B, dim] + cond = cond.permute([0, 2, 1]) # [B, T, M] + + seq_len = cond.shape[1] # [T_mel] + time_embed = diffusion_step[:, None, :] # [B, 1, dim] + time_embed = time_embed.repeat([1, seq_len, 1]) # # [B, T, dim] + + decoder_inp = torch.cat([x, cond, time_embed], dim=-1) # [B, T, dim + H + dim] + decoder_inp = self.get_decode_inp(decoder_inp) # [B, T, H] + x = decoder_inp + + ''' + Required x: [B, T, C] + :return: [B, T, C] or [L, B, T, C] + ''' + padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask + nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] + if self.use_pos_embed: + positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + # B x T x C -> T x B x C + x = x.transpose(0, 1) * nonpadding_mask_TB + hiddens = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB + hiddens.append(x) + if self.use_last_norm: + x = self.layer_norm(x) * nonpadding_mask_TB + if return_hiddens: + x = torch.stack(hiddens, 0) # [L, T, B, C] + x = x.transpose(1, 2) # [L, B, T, C] + else: + x = x.transpose(0, 1) # [B, T, C] + + x = self.get_mel_out(x).permute([0, 2, 1]) # [B, 80, T] + return x[:, None, :, :] \ No newline at end of file diff --git a/NeuralSeq/modules/diff/diffusion.py b/NeuralSeq/modules/diff/diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..c30976ab258feff830c2fa1a2d70876cb1d76eda --- /dev/null +++ b/NeuralSeq/modules/diff/diffusion.py @@ -0,0 +1,334 @@ +import math +import random +from functools import partial +from inspect import isfunction +from pathlib import Path +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from tqdm import tqdm +from einops import rearrange + +from modules.fastspeech.fs2 import FastSpeech2 +from modules.diffsinger_midi.fs2 import FastSpeech2MIDI +from utils.hparams import hparams + + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def cycle(dl): + while True: + for data in dl: + yield data + + +def num_to_groups(num, divisor): + groups = num // divisor + remainder = num % divisor + arr = [divisor] * groups + if remainder > 0: + arr.append(remainder) + return arr + + +class Residual(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def forward(self, x, *args, **kwargs): + return self.fn(x, *args, **kwargs) + x + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Mish(nn.Module): + def forward(self, x): + return x * torch.tanh(F.softplus(x)) + + +class Upsample(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class Downsample(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = nn.Conv2d(dim, dim, 3, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x): + return self.fn(x) * self.g + + +# building block modules + +class Block(nn.Module): + def __init__(self, dim, dim_out, groups=8): + super().__init__() + self.block = nn.Sequential( + nn.Conv2d(dim, dim_out, 3, padding=1), + nn.GroupNorm(groups, dim_out), + Mish() + ) + + def forward(self, x): + return self.block(x) + + +class ResnetBlock(nn.Module): + def __init__(self, dim, dim_out, *, time_emb_dim, groups=8): + super().__init__() + self.mlp = nn.Sequential( + Mish(), + nn.Linear(time_emb_dim, dim_out) + ) + + self.block1 = Block(dim, dim_out) + self.block2 = Block(dim_out, dim_out) + self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + + def forward(self, x, time_emb): + h = self.block1(x) + h += self.mlp(time_emb)[:, :, None, None] + h = self.block2(h) + return h + self.res_conv(x) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +# gaussian diffusion trainer class + +def extract(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return np.clip(betas, a_min=0, a_max=0.999) + + +class GaussianDiffusion(nn.Module): + def __init__(self, phone_encoder, out_dims, denoise_fn, + timesteps=1000, loss_type='l1', betas=None, spec_min=None, spec_max=None): + super().__init__() + self.denoise_fn = denoise_fn + if hparams.get('use_midi') is not None and hparams['use_midi']: + self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) + else: + self.fs2 = FastSpeech2(phone_encoder, out_dims) + self.fs2.decoder = None + self.mel_bins = out_dims + + if exists(betas): + betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas + else: + betas = cosine_beta_schedule(timesteps) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.loss_type = loss_type + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) + self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract(1. - self.alphas_cumprod, t, x_start.shape) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, cond, clip_denoised: bool): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + + if self.loss_type == 'l1': + if nonpadding is not None: + loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() + else: + # print('are you sure w/o nonpadding?') + loss = (noise - x_recon).abs().mean() + + elif self.loss_type == 'l2': + loss = F.mse_loss(noise, x_recon) + else: + raise NotImplementedError() + + return loss + + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, + ref_mels=None, f0=None, uv=None, energy=None, infer=False): + b, *_, device = *txt_tokens.shape, txt_tokens.device + ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, + skip_decoder=True, infer=infer) + cond = ret['decoder_inp'].transpose(1, 2) + if not infer: + t = torch.randint(0, self.num_timesteps, (b,), device=device).long() + x = ref_mels + x = self.norm_spec(x) + x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] + nonpadding = (mel2ph != 0).float() + ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) + else: + t = self.num_timesteps + shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) + x = torch.randn(shape, device=device) + for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x[:, 0].transpose(1, 2) + ret['mel_out'] = self.denorm_spec(x) + + return ret + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): + return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) + + def out2mel(self, x): + return x diff --git a/NeuralSeq/modules/diff/net.py b/NeuralSeq/modules/diff/net.py new file mode 100644 index 0000000000000000000000000000000000000000..b8811115eafb4f27165cf4d89c67c0d9455aac9d --- /dev/null +++ b/NeuralSeq/modules/diff/net.py @@ -0,0 +1,130 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from math import sqrt + +from .diffusion import Mish +from utils.hparams import hparams + +Linear = nn.Linear +ConvTranspose2d = nn.ConvTranspose2d + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + def override(self, attrs): + if isinstance(attrs, dict): + self.__dict__.update(**attrs) + elif isinstance(attrs, (list, tuple, set)): + for attr in attrs: + self.override(attr) + elif attrs is not None: + raise NotImplementedError + return self + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +def Conv1d(*args, **kwargs): + layer = nn.Conv1d(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +@torch.jit.script +def silu(x): + return x * torch.sigmoid(x) + + +class ResidualBlock(nn.Module): + def __init__(self, encoder_hidden, residual_channels, dilation): + super().__init__() + self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) + self.diffusion_projection = Linear(residual_channels, residual_channels) + self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) + self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) + + def forward(self, x, conditioner, diffusion_step): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) + conditioner = self.conditioner_projection(conditioner) + y = x + diffusion_step + + y = self.dilated_conv(y) + conditioner + + gate, filter = torch.chunk(y, 2, dim=1) + y = torch.sigmoid(gate) * torch.tanh(filter) + + y = self.output_projection(y) + residual, skip = torch.chunk(y, 2, dim=1) + return (x + residual) / sqrt(2.0), skip + + +class DiffNet(nn.Module): + def __init__(self, in_dims=80): + super().__init__() + self.params = params = AttrDict( + # Model params + encoder_hidden=hparams['hidden_size'], + residual_layers=hparams['residual_layers'], + residual_channels=hparams['residual_channels'], + dilation_cycle_length=hparams['dilation_cycle_length'], + ) + self.input_projection = Conv1d(in_dims, params.residual_channels, 1) + self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels) + dim = params.residual_channels + self.mlp = nn.Sequential( + nn.Linear(dim, dim * 4), + Mish(), + nn.Linear(dim * 4, dim) + ) + self.residual_layers = nn.ModuleList([ + ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length)) + for i in range(params.residual_layers) + ]) + self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1) + self.output_projection = Conv1d(params.residual_channels, in_dims, 1) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + + :param spec: [B, 1, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, M, T] + :return: + """ + x = spec[:, 0] + x = self.input_projection(x) # x [B, residual_channel, T] + + x = F.relu(x) + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) + skip = [] + for layer_id, layer in enumerate(self.residual_layers): + x, skip_connection = layer(x, cond, diffusion_step) + skip.append(skip_connection) + + x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = F.relu(x) + x = self.output_projection(x) # [B, 80, T] + return x[:, None, :, :] diff --git a/NeuralSeq/modules/diff/shallow_diffusion_tts.py b/NeuralSeq/modules/diff/shallow_diffusion_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..8295d48ea0028dd0b3fdf1315bb8f129d0070810 --- /dev/null +++ b/NeuralSeq/modules/diff/shallow_diffusion_tts.py @@ -0,0 +1,324 @@ +import math +import random +from collections import deque +from functools import partial +from inspect import isfunction +from pathlib import Path +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from tqdm import tqdm +from einops import rearrange + +from modules.fastspeech.fs2 import FastSpeech2 +from modules.diffsinger_midi.fs2 import FastSpeech2MIDI +from utils.hparams import hparams + + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +# gaussian diffusion trainer class + +def extract(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)): + """ + linear schedule + """ + betas = np.linspace(1e-4, max_beta, timesteps) + return betas + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return np.clip(betas, a_min=0, a_max=0.999) + + +beta_schedule = { + "cosine": cosine_beta_schedule, + "linear": linear_beta_schedule, +} + + +class GaussianDiffusion(nn.Module): + def __init__(self, phone_encoder, out_dims, denoise_fn, + timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None, spec_max=None): + super().__init__() + self.denoise_fn = denoise_fn + if hparams.get('use_midi') is not None and hparams['use_midi']: + self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) + else: + self.fs2 = FastSpeech2(phone_encoder, out_dims) + self.mel_bins = out_dims + + if exists(betas): + betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas + else: + if 'schedule_type' in hparams.keys(): + betas = beta_schedule[hparams['schedule_type']](timesteps) + else: + betas = cosine_beta_schedule(timesteps) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.K_step = K_step + self.loss_type = loss_type + + self.noise_list = deque(maxlen=4) + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) + self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract(1. - self.alphas_cumprod, t, x_start.shape) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, cond, clip_denoised: bool): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): + """ + Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). + """ + + def get_x_pred(x, noise_t, t): + a_t = extract(self.alphas_cumprod, t, x.shape) + if t[0] < interval: + a_prev = torch.ones_like(a_t) + else: + a_prev = extract(self.alphas_cumprod, torch.max(t-interval, torch.zeros_like(t)), x.shape) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x + x_delta + + return x_pred + + noise_list = self.noise_list + noise_pred = self.denoise_fn(x, t, cond=cond) + + if len(noise_list) == 0: + x_pred = get_x_pred(x, noise_pred, t) + noise_pred_prev = self.denoise_fn(x_pred, max(t-interval, 0), cond=cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2 + elif len(noise_list) == 1: + noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 + elif len(noise_list) == 2: + noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 + elif len(noise_list) >= 3: + noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 + + x_prev = get_x_pred(x, noise_pred_prime, t) + noise_list.append(noise_pred) + + return x_prev + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + + if self.loss_type == 'l1': + if nonpadding is not None: + loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() + else: + # print('are you sure w/o nonpadding?') + loss = (noise - x_recon).abs().mean() + + elif self.loss_type == 'l2': + loss = F.mse_loss(noise, x_recon) + else: + raise NotImplementedError() + + return loss + + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, + ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): + b, *_, device = *txt_tokens.shape, txt_tokens.device + ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, + skip_decoder=(not infer), infer=infer, **kwargs) + cond = ret['decoder_inp'].transpose(1, 2) + + if not infer: + t = torch.randint(0, self.K_step, (b,), device=device).long() + x = ref_mels + x = self.norm_spec(x) + x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] + ret['diff_loss'] = self.p_losses(x, t, cond) + # nonpadding = (mel2ph != 0).float() + # ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) + else: + ret['fs2_mel'] = ret['mel_out'] + fs2_mels = ret['mel_out'] + t = self.K_step + fs2_mels = self.norm_spec(fs2_mels) + fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] + + x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) + if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: + print('===> gaussion start.') + shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) + x = torch.randn(shape, device=device) + + if hparams.get('pndm_speedup'): + print('===> pndm speed:', hparams['pndm_speedup']) + self.noise_list = deque(maxlen=4) + iteration_interval = hparams['pndm_speedup'] + for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step', + total=t // iteration_interval): + x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval, + cond) + else: + for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x[:, 0].transpose(1, 2) + if mel2ph is not None: # for singing + ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None]) + else: + ret['mel_out'] = self.denorm_spec(x) + return ret + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): + return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) + + def out2mel(self, x): + return x + + +class OfflineGaussianDiffusion(GaussianDiffusion): + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, + ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): + b, *_, device = *txt_tokens.shape, txt_tokens.device + + ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, + skip_decoder=True, infer=True, **kwargs) + cond = ret['decoder_inp'].transpose(1, 2) + fs2_mels = ref_mels[1] + ref_mels = ref_mels[0] + + if not infer: + t = torch.randint(0, self.K_step, (b,), device=device).long() + x = ref_mels + x = self.norm_spec(x) + x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] + ret['diff_loss'] = self.p_losses(x, t, cond) + else: + t = self.K_step + fs2_mels = self.norm_spec(fs2_mels) + fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] + + x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) + + if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: + print('===> gaussion start.') + shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) + x = torch.randn(shape, device=device) + for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x[:, 0].transpose(1, 2) + ret['mel_out'] = self.denorm_spec(x) + return ret diff --git a/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-37.pyc b/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5752eddef5ef5e9c3198af1abf195793961e61e4 Binary files /dev/null and b/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-37.pyc differ diff --git a/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-38.pyc b/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..93e6f773fcc23e4033f61fbb39be7d96b297c529 Binary files /dev/null and b/NeuralSeq/modules/diffsinger_midi/__pycache__/fs2.cpython-38.pyc differ diff --git a/NeuralSeq/modules/diffsinger_midi/fs2.py b/NeuralSeq/modules/diffsinger_midi/fs2.py new file mode 100644 index 0000000000000000000000000000000000000000..94a9ec3c51cd749576a5f60caec5e5b08d2a7d02 --- /dev/null +++ b/NeuralSeq/modules/diffsinger_midi/fs2.py @@ -0,0 +1,119 @@ +from modules.commons.common_layers import * +from modules.commons.common_layers import Embedding +from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \ + EnergyPredictor, FastspeechEncoder +from utils.cwt import cwt2f0 +from utils.hparams import hparams +from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0 +from modules.fastspeech.fs2 import FastSpeech2 + + +class FastspeechMIDIEncoder(FastspeechEncoder): + def forward_embedding(self, txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(txt_tokens) + x = x + midi_embedding + midi_dur_embedding + slur_embedding + if hparams['use_pos_embed']: + if hparams.get('rel_pos') is not None and hparams['rel_pos']: + x = self.embed_positions(x) + else: + positions = self.embed_positions(txt_tokens) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + return x + + def forward(self, txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding): + """ + + :param txt_tokens: [B, T] + :return: { + 'encoder_out': [T x B x C] + } + """ + encoder_padding_mask = txt_tokens.eq(self.padding_idx).data + x = self.forward_embedding(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding) # [B, T, H] + x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask) + return x + + +FS_ENCODERS = { + 'fft': lambda hp, embed_tokens, d: FastspeechMIDIEncoder( + embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'], + num_heads=hp['num_heads']), +} + + +class FastSpeech2MIDI(FastSpeech2): + def __init__(self, dictionary, out_dims=None): + super().__init__(dictionary, out_dims) + del self.encoder + self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary) + self.midi_embed = Embedding(300, self.hidden_size, self.padding_idx) + self.midi_dur_layer = Linear(1, self.hidden_size) + self.is_slur_embed = Embedding(2, self.hidden_size) + + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, + ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False, + spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs): + ret = {} + + midi_embedding = self.midi_embed(kwargs['pitch_midi']) + midi_dur_embedding, slur_embedding = 0, 0 + if kwargs.get('midi_dur') is not None: + midi_dur_embedding = self.midi_dur_layer(kwargs['midi_dur'][:, :, None]) # [B, T, 1] -> [B, T, H] + if kwargs.get('is_slur') is not None: + slur_embedding = self.is_slur_embed(kwargs['is_slur']) + encoder_out = self.encoder(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding) # [B, T, C] + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + + # add ref style embed + # Not implemented + # variance encoder + var_embed = 0 + + # encoder_out_dur denotes encoder outputs for duration predictor + # in speech adaptation, duration predictor use old speaker embedding + if hparams['use_spk_embed']: + spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] + elif hparams['use_spk_id']: + spk_embed_id = spk_embed + if spk_embed_dur_id is None: + spk_embed_dur_id = spk_embed_id + if spk_embed_f0_id is None: + spk_embed_f0_id = spk_embed_id + spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :] + spk_embed_dur = spk_embed_f0 = spk_embed + if hparams['use_split_spk_id']: + spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :] + spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :] + else: + spk_embed_dur = spk_embed_f0 = spk_embed = 0 + + # add dur + dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding + + mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) + + decoder_inp = F.pad(encoder_out, [0, 0, 1, 0]) + + mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]]) + decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H] + + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + + # add pitch and energy embed + pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding + if hparams['use_pitch_embed']: + pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding + decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph) + if hparams['use_energy_embed']: + decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret) + + ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding + + if skip_decoder: + return ret + ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) + + return ret + diff --git a/NeuralSeq/modules/fastspeech/__pycache__/fs2.cpython-37.pyc b/NeuralSeq/modules/fastspeech/__pycache__/fs2.cpython-37.pyc new file mode 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--git a/NeuralSeq/modules/fastspeech/fs2.py b/NeuralSeq/modules/fastspeech/fs2.py new file mode 100644 index 0000000000000000000000000000000000000000..a4487ee37075e1fc2209698e75b164410c191a18 --- /dev/null +++ b/NeuralSeq/modules/fastspeech/fs2.py @@ -0,0 +1,250 @@ +from utils.hparams import hparams +from modules.commons.common_layers import * +from modules.commons.common_layers import Embedding +from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \ + EnergyPredictor, FastspeechEncoder +from utils.cwt import cwt2f0 +from utils.pitch_utils import f0_to_coarse, denorm_f0, norm_f0 +import torch.nn as nn +from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder +FS_ENCODERS = { + 'fft': lambda hp, embed_tokens, d: FastspeechEncoder( + embed_tokens, hp['hidden_size'], hp['enc_layers'], hp['enc_ffn_kernel_size'], + num_heads=hp['num_heads']), +} + +FS_DECODERS = { + 'fft': lambda hp: FastspeechDecoder( + hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']), +} + + +class FastSpeech2(nn.Module): + def __init__(self, dictionary, out_dims=None): + super().__init__() + self.dictionary = dictionary + self.padding_idx = dictionary.pad() + self.enc_layers = hparams['enc_layers'] + self.dec_layers = hparams['dec_layers'] + self.hidden_size = hparams['hidden_size'] + self.encoder_embed_tokens = self.build_embedding(self.dictionary, self.hidden_size) + if hparams.get("use_bert", False): + self.ph_encoder = BERTRelTransformerEncoder(len(self.dictionary), hparams['hidden_size'], hparams['hidden_size'], + hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'], + hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln']) + else: + self.encoder = FS_ENCODERS[hparams['encoder_type']](hparams, self.encoder_embed_tokens, self.dictionary) + self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) + self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims + self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True) + + if hparams['use_spk_id']: + self.spk_embed_proj = Embedding(hparams['num_spk'] + 1, self.hidden_size) + if hparams['use_split_spk_id']: + self.spk_embed_f0 = Embedding(hparams['num_spk'] + 1, self.hidden_size) + self.spk_embed_dur = Embedding(hparams['num_spk'] + 1, self.hidden_size) + elif hparams['use_spk_embed']: + self.spk_embed_proj = Linear(256, self.hidden_size, bias=True) + predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size + self.dur_predictor = DurationPredictor( + self.hidden_size, + n_chans=predictor_hidden, + n_layers=hparams['dur_predictor_layers'], + dropout_rate=hparams['predictor_dropout'], + kernel_size=hparams['dur_predictor_kernel']) + self.length_regulator = LengthRegulator() + if hparams['use_pitch_embed']: + self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx) + self.pitch_predictor = PitchPredictor( + self.hidden_size, + n_chans=predictor_hidden, + n_layers=hparams['predictor_layers'], + dropout_rate=hparams['predictor_dropout'], + odim=2 if hparams['pitch_type'] == 'frame' else 1, + kernel_size=hparams['predictor_kernel']) + if hparams.get('use_energy_embed', False): + self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx) + self.energy_predictor = EnergyPredictor( + self.hidden_size, + n_chans=predictor_hidden, + n_layers=hparams['predictor_layers'], + dropout_rate=hparams['predictor_dropout'], odim=1, + kernel_size=hparams['predictor_kernel']) + + def build_embedding(self, dictionary, embed_dim): + num_embeddings = len(dictionary) + emb = Embedding(num_embeddings, embed_dim, self.padding_idx) + return emb + + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, + ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=False, + spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs): + ret = {} + if hparams.get("use_bert", False): + encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'], ret=ret) + else: + encoder_out = self.encoder(txt_tokens) # [B, T, C] + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + + # add ref style embed + # Not implemented + # variance encoder + var_embed = 0 + + # encoder_out_dur denotes encoder outputs for duration predictor + # in speech adaptation, duration predictor use old speaker embedding + if hparams['use_spk_embed']: + spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :] + elif hparams['use_spk_id']: + spk_embed_id = spk_embed + if spk_embed_dur_id is None: + spk_embed_dur_id = spk_embed_id + if spk_embed_f0_id is None: + spk_embed_f0_id = spk_embed_id + spk_embed = self.spk_embed_proj(spk_embed_id)[:, None, :] + spk_embed_dur = spk_embed_f0 = spk_embed + if hparams['use_split_spk_id']: + spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :] + spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :] + else: + spk_embed_dur = spk_embed_f0 = spk_embed = 0 + + # add dur + dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding + + mel2ph = self.add_dur(dur_inp, mel2ph, txt_tokens, ret) + + decoder_inp = F.pad(encoder_out, [0, 0, 1, 0]) + + mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]]) + decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H] + + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + + # add pitch and energy embed + pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding + if hparams['use_pitch_embed']: + pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding + decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph) + if hparams.get('use_energy_embed', False): + decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret) + + ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding + + if skip_decoder: + return ret + ret['mel_out'] = self.run_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) + + return ret + + def add_dur(self, dur_input, mel2ph, txt_tokens, ret): + """ + + :param dur_input: [B, T_txt, H] + :param mel2ph: [B, T_mel] + :param txt_tokens: [B, T_txt] + :param ret: + :return: + """ + src_padding = txt_tokens == 0 + dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) + if mel2ph is None: + dur, xs = self.dur_predictor.inference(dur_input, src_padding) + ret['dur'] = xs + ret['dur_choice'] = dur + mel2ph = self.length_regulator(dur, src_padding).detach() + # from modules.fastspeech.fake_modules import FakeLengthRegulator + # fake_lr = FakeLengthRegulator() + # fake_mel2ph = fake_lr(dur, (1 - src_padding.long()).sum(-1))[..., 0].detach() + # print(mel2ph == fake_mel2ph) + else: + ret['dur'] = self.dur_predictor(dur_input, src_padding) + ret['mel2ph'] = mel2ph + return mel2ph + + def add_energy(self, decoder_inp, energy, ret): + decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach()) + ret['energy_pred'] = energy_pred = self.energy_predictor(decoder_inp)[:, :, 0] + if energy is None: + energy = energy_pred + energy = torch.clamp(energy * 256 // 4, max=255).long() + energy_embed = self.energy_embed(energy) + return energy_embed + + def add_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None): + if hparams['pitch_type'] == 'ph': + pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach()) + pitch_padding = encoder_out.sum().abs() == 0 + ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp) + if f0 is None: + f0 = pitch_pred[:, :, 0] + ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding) + pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt] + pitch = F.pad(pitch, [1, 0]) + pitch = torch.gather(pitch, 1, mel2ph) # [B, T_mel] + pitch_embed = self.pitch_embed(pitch) + return pitch_embed + decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach()) + + pitch_padding = mel2ph == 0 + + if hparams['pitch_type'] == 'cwt': + pitch_padding = None + ret['cwt'] = cwt_out = self.cwt_predictor(decoder_inp) + stats_out = self.cwt_stats_layers(encoder_out[:, 0, :]) # [B, 2] + mean = ret['f0_mean'] = stats_out[:, 0] + std = ret['f0_std'] = stats_out[:, 1] + cwt_spec = cwt_out[:, :, :10] + if f0 is None: + std = std * hparams['cwt_std_scale'] + f0 = self.cwt2f0_norm(cwt_spec, mean, std, mel2ph) + if hparams['use_uv']: + assert cwt_out.shape[-1] == 11 + uv = cwt_out[:, :, -1] > 0 + elif hparams['pitch_ar']: + ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp, f0 if self.training else None) + if f0 is None: + f0 = pitch_pred[:, :, 0] + else: + ret['pitch_pred'] = pitch_pred = self.pitch_predictor(decoder_inp) + if f0 is None: + f0 = pitch_pred[:, :, 0] + if hparams['use_uv'] and uv is None: + uv = pitch_pred[:, :, 1] > 0 + ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding) + if pitch_padding is not None: + f0[pitch_padding] = 0 + + pitch = f0_to_coarse(f0_denorm) # start from 0 + pitch_embed = self.pitch_embed(pitch) + return pitch_embed + + def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs): + x = decoder_inp # [B, T, H] + x = self.decoder(x) + x = self.mel_out(x) + return x * tgt_nonpadding + + def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): + f0 = cwt2f0(cwt_spec, mean, std, hparams['cwt_scales']) + f0 = torch.cat( + [f0] + [f0[:, -1:]] * (mel2ph.shape[1] - f0.shape[1]), 1) + f0_norm = norm_f0(f0, None, hparams) + return f0_norm + + def out2mel(self, out): + return out + + @staticmethod + def mel_norm(x): + return (x + 5.5) / (6.3 / 2) - 1 + + @staticmethod + def mel_denorm(x): + return (x + 1) * (6.3 / 2) - 5.5 + + def expand_states(self, h, mel2ph): + h = F.pad(h, [0, 0, 1, 0]) + mel2ph_ = mel2ph[..., None].repeat([1, 1, h.shape[-1]]) + h = torch.gather(h, 1, mel2ph_) # [B, T, H] + return h diff --git a/NeuralSeq/modules/fastspeech/pe.py b/NeuralSeq/modules/fastspeech/pe.py new file mode 100644 index 0000000000000000000000000000000000000000..d9fa5098b378bb4ed10f97f05a9ff725d1d2239c --- /dev/null +++ b/NeuralSeq/modules/fastspeech/pe.py @@ -0,0 +1,149 @@ +from modules.commons.common_layers import * +from utils.hparams import hparams +from modules.fastspeech.tts_modules import PitchPredictor +from utils.pitch_utils import denorm_f0 + + +class Prenet(nn.Module): + def __init__(self, in_dim=80, out_dim=256, kernel=5, n_layers=3, strides=None): + super(Prenet, self).__init__() + padding = kernel // 2 + self.layers = [] + self.strides = strides if strides is not None else [1] * n_layers + for l in range(n_layers): + self.layers.append(nn.Sequential( + nn.Conv1d(in_dim, out_dim, kernel_size=kernel, padding=padding, stride=self.strides[l]), + nn.ReLU(), + nn.BatchNorm1d(out_dim) + )) + in_dim = out_dim + self.layers = nn.ModuleList(self.layers) + self.out_proj = nn.Linear(out_dim, out_dim) + + def forward(self, x): + """ + + :param x: [B, T, 80] + :return: [L, B, T, H], [B, T, H] + """ + padding_mask = x.abs().sum(-1).eq(0).data # [B, T] + nonpadding_mask_TB = 1 - padding_mask.float()[:, None, :] # [B, 1, T] + x = x.transpose(1, 2) + hiddens = [] + for i, l in enumerate(self.layers): + nonpadding_mask_TB = nonpadding_mask_TB[:, :, ::self.strides[i]] + x = l(x) * nonpadding_mask_TB + hiddens.append(x) + hiddens = torch.stack(hiddens, 0) # [L, B, H, T] + hiddens = hiddens.transpose(2, 3) # [L, B, T, H] + x = self.out_proj(x.transpose(1, 2)) # [B, T, H] + x = x * nonpadding_mask_TB.transpose(1, 2) + return hiddens, x + + +class ConvBlock(nn.Module): + def __init__(self, idim=80, n_chans=256, kernel_size=3, stride=1, norm='gn', dropout=0): + super().__init__() + self.conv = ConvNorm(idim, n_chans, kernel_size, stride=stride) + self.norm = norm + if self.norm == 'bn': + self.norm = nn.BatchNorm1d(n_chans) + elif self.norm == 'in': + self.norm = nn.InstanceNorm1d(n_chans, affine=True) + elif self.norm == 'gn': + self.norm = nn.GroupNorm(n_chans // 16, n_chans) + elif self.norm == 'ln': + self.norm = LayerNorm(n_chans // 16, n_chans) + elif self.norm == 'wn': + self.conv = torch.nn.utils.weight_norm(self.conv.conv) + self.dropout = nn.Dropout(dropout) + self.relu = nn.ReLU() + + def forward(self, x): + """ + + :param x: [B, C, T] + :return: [B, C, T] + """ + x = self.conv(x) + if not isinstance(self.norm, str): + if self.norm == 'none': + pass + elif self.norm == 'ln': + x = self.norm(x.transpose(1, 2)).transpose(1, 2) + else: + x = self.norm(x) + x = self.relu(x) + x = self.dropout(x) + return x + + +class ConvStacks(nn.Module): + def __init__(self, idim=80, n_layers=5, n_chans=256, odim=32, kernel_size=5, norm='gn', + dropout=0, strides=None, res=True): + super().__init__() + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + self.res = res + self.in_proj = Linear(idim, n_chans) + if strides is None: + strides = [1] * n_layers + else: + assert len(strides) == n_layers + for idx in range(n_layers): + self.conv.append(ConvBlock( + n_chans, n_chans, kernel_size, stride=strides[idx], norm=norm, dropout=dropout)) + self.out_proj = Linear(n_chans, odim) + + def forward(self, x, return_hiddens=False): + """ + + :param x: [B, T, H] + :return: [B, T, H] + """ + x = self.in_proj(x) + x = x.transpose(1, -1) # (B, idim, Tmax) + hiddens = [] + for f in self.conv: + x_ = f(x) + x = x + x_ if self.res else x_ # (B, C, Tmax) + hiddens.append(x) + x = x.transpose(1, -1) + x = self.out_proj(x) # (B, Tmax, H) + if return_hiddens: + hiddens = torch.stack(hiddens, 1) # [B, L, C, T] + return x, hiddens + return x + + +class PitchExtractor(nn.Module): + def __init__(self, n_mel_bins=80, conv_layers=2): + super().__init__() + self.hidden_size = hparams['hidden_size'] + self.predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size + self.conv_layers = conv_layers + + self.mel_prenet = Prenet(n_mel_bins, self.hidden_size, strides=[1, 1, 1]) + if self.conv_layers > 0: + self.mel_encoder = ConvStacks( + idim=self.hidden_size, n_chans=self.hidden_size, odim=self.hidden_size, n_layers=self.conv_layers) + self.pitch_predictor = PitchPredictor( + self.hidden_size, n_chans=self.predictor_hidden, + n_layers=5, dropout_rate=0.1, odim=2, + padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel']) + + def forward(self, mel_input=None): + ret = {} + mel_hidden = self.mel_prenet(mel_input)[1] + if self.conv_layers > 0: + mel_hidden = self.mel_encoder(mel_hidden) + + ret['pitch_pred'] = pitch_pred = self.pitch_predictor(mel_hidden) + + pitch_padding = mel_input.abs().sum(-1) == 0 + use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv'] + + ret['f0_denorm_pred'] = denorm_f0( + pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None, + hparams, pitch_padding=pitch_padding) + return ret \ No newline at end of file diff --git a/NeuralSeq/modules/fastspeech/tts_modules.py b/NeuralSeq/modules/fastspeech/tts_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..2d65438656fe6860ae272042a9631025326edd75 --- /dev/null +++ b/NeuralSeq/modules/fastspeech/tts_modules.py @@ -0,0 +1,385 @@ +import logging +import math + +import torch +import torch.nn as nn +from torch.nn import functional as F + +from modules.commons.espnet_positional_embedding import RelPositionalEncoding +from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC +from utils.hparams import hparams + +DEFAULT_MAX_SOURCE_POSITIONS = 2000 +DEFAULT_MAX_TARGET_POSITIONS = 2000 + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, hidden_size, dropout, kernel_size=None, num_heads=2, norm='ln'): + super().__init__() + self.hidden_size = hidden_size + self.dropout = dropout + self.num_heads = num_heads + self.op = EncSALayer( + hidden_size, num_heads, dropout=dropout, + attention_dropout=0.0, relu_dropout=dropout, + kernel_size=kernel_size + if kernel_size is not None else hparams['enc_ffn_kernel_size'], + padding=hparams['ffn_padding'], + norm=norm, act=hparams['ffn_act']) + + def forward(self, x, **kwargs): + return self.op(x, **kwargs) + + +###################### +# fastspeech modules +###################### +class LayerNorm(torch.nn.LayerNorm): + """Layer normalization module. + :param int nout: output dim size + :param int dim: dimension to be normalized + """ + + def __init__(self, nout, dim=-1, eps=1e-5): + """Construct an LayerNorm object.""" + super(LayerNorm, self).__init__(nout, eps=eps) + self.dim = dim + + def forward(self, x): + """Apply layer normalization. + :param torch.Tensor x: input tensor + :return: layer normalized tensor + :rtype torch.Tensor + """ + if self.dim == -1: + return super(LayerNorm, self).forward(x) + return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1) + + +class DurationPredictor(torch.nn.Module): + """Duration predictor module. + This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_. + The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder. + .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: + https://arxiv.org/pdf/1905.09263.pdf + Note: + The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, + the outputs are calculated in log domain but in `inference`, those are calculated in linear domain. + """ + + def __init__(self, idim, odims = 1, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, padding='SAME'): + """Initilize duration predictor module. + Args: + idim (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + offset (float, optional): Offset value to avoid nan in log domain. + """ + super(DurationPredictor, self).__init__() + self.offset = offset + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + self.padding = padding + for idx in range(n_layers): + in_chans = idim if idx == 0 else n_chans + self.conv += [torch.nn.Sequential( + torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2) + if padding == 'SAME' + else (kernel_size - 1, 0), 0), + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )] + self.linear = torch.nn.Linear(n_chans, odims) + + def _forward(self, xs, x_masks=None, is_inference=False): + xs = xs.transpose(1, -1) # (B, idim, Tmax) + for f in self.conv: + xs = f(xs) # (B, C, Tmax) + if x_masks is not None: + xs = xs * (1 - x_masks.float())[:, None, :] + + xs = self.linear(xs.transpose(1, -1)) # [B, T, C] + xs = xs * (1 - x_masks.float())[:, :, None] # (B, T, C) + if is_inference: + return self.out2dur(xs), xs + else: + if hparams['dur_loss'] in ['mse']: + xs = xs.squeeze(-1) # (B, Tmax) + return xs + + def out2dur(self, xs): + if hparams['dur_loss'] in ['mse']: + # NOTE: calculate in log domain + xs = xs.squeeze(-1) # (B, Tmax) + dur = torch.clamp(torch.round(xs.exp() - self.offset), min=0).long() # avoid negative value + elif hparams['dur_loss'] == 'mog': + return NotImplementedError + elif hparams['dur_loss'] == 'crf': + dur = torch.LongTensor(self.crf.decode(xs)).cuda() + return dur + + def forward(self, xs, x_masks=None): + """Calculate forward propagation. + Args: + xs (Tensor): Batch of input sequences (B, Tmax, idim). + x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax). + Returns: + Tensor: Batch of predicted durations in log domain (B, Tmax). + """ + return self._forward(xs, x_masks, False) + + def inference(self, xs, x_masks=None): + """Inference duration. + Args: + xs (Tensor): Batch of input sequences (B, Tmax, idim). + x_masks (ByteTensor, optional): Batch of masks indicating padded part (B, Tmax). + Returns: + LongTensor: Batch of predicted durations in linear domain (B, Tmax). + """ + return self._forward(xs, x_masks, True) + +class SyntaDurationPredictor(torch.nn.Module): + def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0): + super(SyntaDurationPredictor, self).__init__() + from modules.syntaspeech.syntactic_graph_encoder import GraphAuxEnc + self.graph_encoder = GraphAuxEnc(in_dim=idim, hid_dim=idim, out_dim=idim) + self.offset = offset + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = idim if idx == 0 else n_chans + self.conv += [torch.nn.Sequential( + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )] + self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus()) + + def forward(self, x, x_padding=None, ph2word=None, graph_lst=None, etypes_lst=None): + x = x.transpose(1, -1) # (B, idim, Tmax) + assert ph2word is not None and graph_lst is not None and etypes_lst is not None + x_graph = self.graph_encoder(graph_lst, x, ph2word, etypes_lst) + x = x + x_graph * 1. + + for f in self.conv: + x = f(x) # (B, C, Tmax) + if x_padding is not None: + x = x * (1 - x_padding.float())[:, None, :] + + x = self.linear(x.transpose(1, -1)) # [B, T, C] + x = x * (1 - x_padding.float())[:, :, None] # (B, T, C) + x = x[..., 0] # (B, Tmax) + return x + +class LengthRegulator(torch.nn.Module): + def __init__(self, pad_value=0.0): + super(LengthRegulator, self).__init__() + self.pad_value = pad_value + + def forward(self, dur, dur_padding=None, alpha=1.0): + """ + Example (no batch dim version): + 1. dur = [2,2,3] + 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4] + 3. token_mask = [[1,1,0,0,0,0,0], + [0,0,1,1,0,0,0], + [0,0,0,0,1,1,1]] + 4. token_idx * token_mask = [[1,1,0,0,0,0,0], + [0,0,2,2,0,0,0], + [0,0,0,0,3,3,3]] + 5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3] + + :param dur: Batch of durations of each frame (B, T_txt) + :param dur_padding: Batch of padding of each frame (B, T_txt) + :param alpha: duration rescale coefficient + :return: + mel2ph (B, T_speech) + """ + assert alpha > 0 + dur = torch.round(dur.float() * alpha).long() + if dur_padding is not None: + dur = dur * (1 - dur_padding.long()) + token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device) + dur_cumsum = torch.cumsum(dur, 1) + dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0) + + pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device) + token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) + mel2ph = (token_idx * token_mask.long()).sum(1) + return mel2ph + + +class PitchPredictor(torch.nn.Module): + def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5, + dropout_rate=0.1, padding='SAME'): + """Initilize pitch predictor module. + Args: + idim (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + """ + super(PitchPredictor, self).__init__() + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + self.padding = padding + for idx in range(n_layers): + in_chans = idim if idx == 0 else n_chans + self.conv += [torch.nn.Sequential( + torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2) + if padding == 'SAME' + else (kernel_size - 1, 0), 0), + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )] + self.linear = torch.nn.Linear(n_chans, odim) + self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096) + self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) + + def forward(self, xs): + """ + + :param xs: [B, T, H] + :return: [B, T, H] + """ + positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) + xs = xs + positions + xs = xs.transpose(1, -1) # (B, idim, Tmax) + for f in self.conv: + xs = f(xs) # (B, C, Tmax) + # NOTE: calculate in log domain + xs = self.linear(xs.transpose(1, -1)) # (B, Tmax, H) + return xs + + +class EnergyPredictor(PitchPredictor): + pass + + +def mel2ph_to_dur(mel2ph, T_txt, max_dur=None): + B, _ = mel2ph.shape + dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph)) + dur = dur[:, 1:] + if max_dur is not None: + dur = dur.clamp(max=max_dur) + return dur + + +class FFTBlocks(nn.Module): + def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=None, num_heads=2, + use_pos_embed=True, use_last_norm=True, norm='ln', use_pos_embed_alpha=True): + super().__init__() + self.num_layers = num_layers + embed_dim = self.hidden_size = hidden_size + self.dropout = dropout if dropout is not None else hparams['dropout'] + self.use_pos_embed = use_pos_embed + self.use_last_norm = use_last_norm + if use_pos_embed: + self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS + self.padding_idx = 0 + self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1 + self.embed_positions = SinusoidalPositionalEmbedding( + embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, + ) + + self.layers = nn.ModuleList([]) + self.layers.extend([ + TransformerEncoderLayer(self.hidden_size, self.dropout, + kernel_size=ffn_kernel_size, num_heads=num_heads) + for _ in range(self.num_layers) + ]) + if self.use_last_norm: + if norm == 'ln': + self.layer_norm = nn.LayerNorm(embed_dim) + elif norm == 'bn': + self.layer_norm = BatchNorm1dTBC(embed_dim) + else: + self.layer_norm = None + + def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False): + """ + :param x: [B, T, C] + :param padding_mask: [B, T] + :return: [B, T, C] or [L, B, T, C] + """ + padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask + nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1] + if self.use_pos_embed: + positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + # B x T x C -> T x B x C + x = x.transpose(0, 1) * nonpadding_mask_TB + hiddens = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB + hiddens.append(x) + if self.use_last_norm: + x = self.layer_norm(x) * nonpadding_mask_TB + if return_hiddens: + x = torch.stack(hiddens, 0) # [L, T, B, C] + x = x.transpose(1, 2) # [L, B, T, C] + else: + x = x.transpose(0, 1) # [B, T, C] + return x + + +class FastspeechEncoder(FFTBlocks): + def __init__(self, embed_tokens, hidden_size=None, num_layers=None, kernel_size=None, num_heads=2): + hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size + kernel_size = hparams['enc_ffn_kernel_size'] if kernel_size is None else kernel_size + num_layers = hparams['dec_layers'] if num_layers is None else num_layers + super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads, + use_pos_embed=False) # use_pos_embed_alpha for compatibility + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(hidden_size) + self.padding_idx = 0 + if hparams.get('rel_pos') is not None and hparams['rel_pos']: + self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0) + else: + self.embed_positions = SinusoidalPositionalEmbedding( + hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, + ) + + def forward(self, txt_tokens): + """ + + :param txt_tokens: [B, T] + :return: { + 'encoder_out': [T x B x C] + } + """ + encoder_padding_mask = txt_tokens.eq(self.padding_idx).data + x = self.forward_embedding(txt_tokens) # [B, T, H] + x = super(FastspeechEncoder, self).forward(x, encoder_padding_mask) + return x + + def forward_embedding(self, txt_tokens): + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(txt_tokens) + if hparams['use_pos_embed']: + if hparams.get('rel_pos') is not None and hparams['rel_pos']: + x = self.embed_positions(x) + else: + positions = self.embed_positions(txt_tokens) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + return x + + +class FastspeechDecoder(FFTBlocks): + def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None): + num_heads = hparams['num_heads'] if num_heads is None else num_heads + hidden_size = hparams['hidden_size'] if hidden_size is None else hidden_size + kernel_size = hparams['dec_ffn_kernel_size'] if kernel_size is None else kernel_size + num_layers = hparams['dec_layers'] if num_layers is None else num_layers + super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads) + diff --git a/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-37.pyc b/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b1b15b430cf380d93503b943f76693ae843d1ae1 Binary files /dev/null and b/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-37.pyc differ diff --git a/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-38.pyc b/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bf3a3429d75cef713cba85628edffe5d2d74e66b Binary files /dev/null and b/NeuralSeq/modules/hifigan/__pycache__/hifigan.cpython-38.pyc differ diff --git a/NeuralSeq/modules/hifigan/hifigan.py b/NeuralSeq/modules/hifigan/hifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..ae7e61f56b00d60bcc49a18ece3edbe54746f7ea --- /dev/null +++ b/NeuralSeq/modules/hifigan/hifigan.py @@ -0,0 +1,365 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork +from modules.parallel_wavegan.models.source import SourceModuleHnNSF +import numpy as np + +LRELU_SLOPE = 0.1 + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Conv1d1x1(Conv1d): + """1x1 Conv1d with customized initialization.""" + + def __init__(self, in_channels, out_channels, bias): + """Initialize 1x1 Conv1d module.""" + super(Conv1d1x1, self).__init__(in_channels, out_channels, + kernel_size=1, padding=0, + dilation=1, bias=bias) + + +class HifiGanGenerator(torch.nn.Module): + def __init__(self, h, c_out=1): + super(HifiGanGenerator, self).__init__() + self.h = h + self.num_kernels = len(h['resblock_kernel_sizes']) + self.num_upsamples = len(h['upsample_rates']) + + if h['use_pitch_embed']: + self.harmonic_num = 8 + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates'])) + self.m_source = SourceModuleHnNSF( + sampling_rate=h['audio_sample_rate'], + harmonic_num=self.harmonic_num) + self.noise_convs = nn.ModuleList() + self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3)) + resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])): + c_cur = h['upsample_initial_channel'] // (2 ** (i + 1)) + self.ups.append(weight_norm( + ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2))) + if h['use_pitch_embed']: + if i + 1 < len(h['upsample_rates']): + stride_f0 = np.prod(h['upsample_rates'][i + 1:]) + self.noise_convs.append(Conv1d( + 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) + else: + self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h['upsample_initial_channel'] // (2 ** (i + 1)) + for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x, f0=None): + if f0 is not None: + # harmonic-source signal, noise-source signal, uv flag + f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) + har_source, noi_source, uv = self.m_source(f0) + har_source = har_source.transpose(1, 2) + + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + if f0 is not None: + x_source = self.noise_convs[i](har_source) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1): + super(DiscriminatorP, self).__init__() + self.use_cond = use_cond + if use_cond: + from utils.hparams import hparams + t = hparams['hop_size'] + self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) + c_in = 2 + + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x, mel): + fmap = [] + if self.use_cond: + x_mel = self.cond_net(mel) + x = torch.cat([x_mel, x], 1) + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, use_cond=False, c_in=1): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorP(2, use_cond=use_cond, c_in=c_in), + DiscriminatorP(3, use_cond=use_cond, c_in=c_in), + DiscriminatorP(5, use_cond=use_cond, c_in=c_in), + DiscriminatorP(7, use_cond=use_cond, c_in=c_in), + DiscriminatorP(11, use_cond=use_cond, c_in=c_in), + ]) + + def forward(self, y, y_hat, mel=None): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y, mel) + y_d_g, fmap_g = d(y_hat, mel) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1): + super(DiscriminatorS, self).__init__() + self.use_cond = use_cond + if use_cond: + t = np.prod(upsample_rates) + self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) + c_in = 2 + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(c_in, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x, mel): + if self.use_cond: + x_mel = self.cond_net(mel) + x = torch.cat([x_mel, x], 1) + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self, use_cond=False, c_in=1): + super(MultiScaleDiscriminator, self).__init__() + from utils.hparams import hparams + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True, use_cond=use_cond, + upsample_rates=[4, 4, hparams['hop_size'] // 16], + c_in=c_in), + DiscriminatorS(use_cond=use_cond, + upsample_rates=[4, 4, hparams['hop_size'] // 32], + c_in=c_in), + DiscriminatorS(use_cond=use_cond, + upsample_rates=[4, 4, hparams['hop_size'] // 64], + c_in=c_in), + ]) + self.meanpools = nn.ModuleList([ + AvgPool1d(4, 2, padding=1), + AvgPool1d(4, 2, padding=1) + ]) + + def forward(self, y, y_hat, mel=None): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y, mel) + y_d_g, fmap_g = d(y_hat, mel) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + r_losses = 0 + g_losses = 0 + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + r_losses += r_loss + g_losses += g_loss + r_losses = r_losses / len(disc_real_outputs) + g_losses = g_losses / len(disc_real_outputs) + return r_losses, g_losses + + +def cond_discriminator_loss(outputs): + loss = 0 + for dg in outputs: + g_loss = torch.mean(dg ** 2) + loss += g_loss + loss = loss / len(outputs) + return loss + + +def generator_loss(disc_outputs): + loss = 0 + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + loss += l + loss = loss / len(disc_outputs) + return loss diff --git a/NeuralSeq/modules/hifigan/mel_utils.py b/NeuralSeq/modules/hifigan/mel_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..06e0f7d4d16fa3e4aefc8949347455f5a6e938da --- /dev/null +++ b/NeuralSeq/modules/hifigan/mel_utils.py @@ -0,0 +1,80 @@ +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, hparams, center=False, complex=False): + # hop_size: 512 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate) + # win_size: 2048 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate) + # fmin: 55 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) + # fmax: 10000 # To be increased/reduced depending on data. + # fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter + # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, + n_fft = hparams['fft_size'] + num_mels = hparams['audio_num_mel_bins'] + sampling_rate = hparams['audio_sample_rate'] + hop_size = hparams['hop_size'] + win_size = hparams['win_size'] + fmin = hparams['fmin'] + fmax = hparams['fmax'] + y = y.clamp(min=-1., max=1.) + global mel_basis, hann_window + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), + mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True) + + if not complex: + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + else: + B, C, T, _ = spec.shape + spec = spec.transpose(1, 2) # [B, T, n_fft, 2] + return spec diff --git a/NeuralSeq/modules/parallel_wavegan/__init__.py b/NeuralSeq/modules/parallel_wavegan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/NeuralSeq/modules/parallel_wavegan/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/modules/parallel_wavegan/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ab6449f0e9546d6f8d5d451b2b509bd007e082b9 Binary files /dev/null and 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b/NeuralSeq/modules/parallel_wavegan/layers/causal_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..fca77daf65f234e6fbe355ed148fc8f0ee85038a --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/causal_conv.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- + +# Copyright 2020 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""Causal convolusion layer modules.""" + + +import torch + + +class CausalConv1d(torch.nn.Module): + """CausalConv1d module with customized initialization.""" + + def __init__(self, in_channels, out_channels, kernel_size, + dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}): + """Initialize CausalConv1d module.""" + super(CausalConv1d, self).__init__() + self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params) + self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size, + dilation=dilation, bias=bias) + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, in_channels, T). + + Returns: + Tensor: Output tensor (B, out_channels, T). + + """ + return self.conv(self.pad(x))[:, :, :x.size(2)] + + +class CausalConvTranspose1d(torch.nn.Module): + """CausalConvTranspose1d module with customized initialization.""" + + def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True): + """Initialize CausalConvTranspose1d module.""" + super(CausalConvTranspose1d, self).__init__() + self.deconv = torch.nn.ConvTranspose1d( + in_channels, out_channels, kernel_size, stride, bias=bias) + self.stride = stride + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, in_channels, T_in). + + Returns: + Tensor: Output tensor (B, out_channels, T_out). + + """ + return self.deconv(x)[:, :, :-self.stride] diff --git a/NeuralSeq/modules/parallel_wavegan/layers/pqmf.py b/NeuralSeq/modules/parallel_wavegan/layers/pqmf.py new file mode 100644 index 0000000000000000000000000000000000000000..ac21074fd32a370a099fa2facb62cfd3253d7579 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/pqmf.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- + +# Copyright 2020 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""Pseudo QMF modules.""" + +import numpy as np +import torch +import torch.nn.functional as F + +from scipy.signal import kaiser + + +def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0): + """Design prototype filter for PQMF. + + This method is based on `A Kaiser window approach for the design of prototype + filters of cosine modulated filterbanks`_. + + Args: + taps (int): The number of filter taps. + cutoff_ratio (float): Cut-off frequency ratio. + beta (float): Beta coefficient for kaiser window. + + Returns: + ndarray: Impluse response of prototype filter (taps + 1,). + + .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`: + https://ieeexplore.ieee.org/abstract/document/681427 + + """ + # check the arguments are valid + assert taps % 2 == 0, "The number of taps mush be even number." + assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0." + + # make initial filter + omega_c = np.pi * cutoff_ratio + with np.errstate(invalid='ignore'): + h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \ + / (np.pi * (np.arange(taps + 1) - 0.5 * taps)) + h_i[taps // 2] = np.cos(0) * cutoff_ratio # fix nan due to indeterminate form + + # apply kaiser window + w = kaiser(taps + 1, beta) + h = h_i * w + + return h + + +class PQMF(torch.nn.Module): + """PQMF module. + + This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_. + + .. _`Near-perfect-reconstruction pseudo-QMF banks`: + https://ieeexplore.ieee.org/document/258122 + + """ + + def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0): + """Initilize PQMF module. + + Args: + subbands (int): The number of subbands. + taps (int): The number of filter taps. + cutoff_ratio (float): Cut-off frequency ratio. + beta (float): Beta coefficient for kaiser window. + + """ + super(PQMF, self).__init__() + + # define filter coefficient + h_proto = design_prototype_filter(taps, cutoff_ratio, beta) + h_analysis = np.zeros((subbands, len(h_proto))) + h_synthesis = np.zeros((subbands, len(h_proto))) + for k in range(subbands): + h_analysis[k] = 2 * h_proto * np.cos( + (2 * k + 1) * (np.pi / (2 * subbands)) * + (np.arange(taps + 1) - ((taps - 1) / 2)) + + (-1) ** k * np.pi / 4) + h_synthesis[k] = 2 * h_proto * np.cos( + (2 * k + 1) * (np.pi / (2 * subbands)) * + (np.arange(taps + 1) - ((taps - 1) / 2)) - + (-1) ** k * np.pi / 4) + + # convert to tensor + analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1) + synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0) + + # register coefficients as beffer + self.register_buffer("analysis_filter", analysis_filter) + self.register_buffer("synthesis_filter", synthesis_filter) + + # filter for downsampling & upsampling + updown_filter = torch.zeros((subbands, subbands, subbands)).float() + for k in range(subbands): + updown_filter[k, k, 0] = 1.0 + self.register_buffer("updown_filter", updown_filter) + self.subbands = subbands + + # keep padding info + self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0) + + def analysis(self, x): + """Analysis with PQMF. + + Args: + x (Tensor): Input tensor (B, 1, T). + + Returns: + Tensor: Output tensor (B, subbands, T // subbands). + + """ + x = F.conv1d(self.pad_fn(x), self.analysis_filter) + return F.conv1d(x, self.updown_filter, stride=self.subbands) + + def synthesis(self, x): + """Synthesis with PQMF. + + Args: + x (Tensor): Input tensor (B, subbands, T // subbands). + + Returns: + Tensor: Output tensor (B, 1, T). + + """ + x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands) + return F.conv1d(self.pad_fn(x), self.synthesis_filter) diff --git a/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py b/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py new file mode 100644 index 0000000000000000000000000000000000000000..7a267a86c1fa521c2824addf9dda304c43f1ff1f --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- + +"""Residual block module in WaveNet. + +This code is modified from https://github.com/r9y9/wavenet_vocoder. + +""" + +import math + +import torch +import torch.nn.functional as F + + +class Conv1d(torch.nn.Conv1d): + """Conv1d module with customized initialization.""" + + def __init__(self, *args, **kwargs): + """Initialize Conv1d module.""" + super(Conv1d, self).__init__(*args, **kwargs) + + def reset_parameters(self): + """Reset parameters.""" + torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu") + if self.bias is not None: + torch.nn.init.constant_(self.bias, 0.0) + + +class Conv1d1x1(Conv1d): + """1x1 Conv1d with customized initialization.""" + + def __init__(self, in_channels, out_channels, bias): + """Initialize 1x1 Conv1d module.""" + super(Conv1d1x1, self).__init__(in_channels, out_channels, + kernel_size=1, padding=0, + dilation=1, bias=bias) + + +class ResidualBlock(torch.nn.Module): + """Residual block module in WaveNet.""" + + def __init__(self, + kernel_size=3, + residual_channels=64, + gate_channels=128, + skip_channels=64, + aux_channels=80, + dropout=0.0, + dilation=1, + bias=True, + use_causal_conv=False + ): + """Initialize ResidualBlock module. + + Args: + kernel_size (int): Kernel size of dilation convolution layer. + residual_channels (int): Number of channels for residual connection. + skip_channels (int): Number of channels for skip connection. + aux_channels (int): Local conditioning channels i.e. auxiliary input dimension. + dropout (float): Dropout probability. + dilation (int): Dilation factor. + bias (bool): Whether to add bias parameter in convolution layers. + use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution. + + """ + super(ResidualBlock, self).__init__() + self.dropout = dropout + # no future time stamps available + if use_causal_conv: + padding = (kernel_size - 1) * dilation + else: + assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." + padding = (kernel_size - 1) // 2 * dilation + self.use_causal_conv = use_causal_conv + + # dilation conv + self.conv = Conv1d(residual_channels, gate_channels, kernel_size, + padding=padding, dilation=dilation, bias=bias) + + # local conditioning + if aux_channels > 0: + self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False) + else: + self.conv1x1_aux = None + + # conv output is split into two groups + gate_out_channels = gate_channels // 2 + self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) + self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias) + + def forward(self, x, c): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, residual_channels, T). + c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T). + + Returns: + Tensor: Output tensor for residual connection (B, residual_channels, T). + Tensor: Output tensor for skip connection (B, skip_channels, T). + + """ + residual = x + x = F.dropout(x, p=self.dropout, training=self.training) + x = self.conv(x) + + # remove future time steps if use_causal_conv conv + x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x + + # split into two part for gated activation + splitdim = 1 + xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim) + + # local conditioning + if c is not None: + assert self.conv1x1_aux is not None + c = self.conv1x1_aux(c) + ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) + xa, xb = xa + ca, xb + cb + + x = torch.tanh(xa) * torch.sigmoid(xb) + + # for skip connection + s = self.conv1x1_skip(x) + + # for residual connection + x = (self.conv1x1_out(x) + residual) * math.sqrt(0.5) + + return x, s diff --git a/NeuralSeq/modules/parallel_wavegan/layers/residual_stack.py b/NeuralSeq/modules/parallel_wavegan/layers/residual_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..6e07c8803ad348dd923f6b7c0f7aff14aab9cf78 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/residual_stack.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- + +# Copyright 2020 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""Residual stack module in MelGAN.""" + +import torch + +from . import CausalConv1d + + +class ResidualStack(torch.nn.Module): + """Residual stack module introduced in MelGAN.""" + + def __init__(self, + kernel_size=3, + channels=32, + dilation=1, + bias=True, + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + pad="ReflectionPad1d", + pad_params={}, + use_causal_conv=False, + ): + """Initialize ResidualStack module. + + Args: + kernel_size (int): Kernel size of dilation convolution layer. + channels (int): Number of channels of convolution layers. + dilation (int): Dilation factor. + bias (bool): Whether to add bias parameter in convolution layers. + nonlinear_activation (str): Activation function module name. + nonlinear_activation_params (dict): Hyperparameters for activation function. + pad (str): Padding function module name before dilated convolution layer. + pad_params (dict): Hyperparameters for padding function. + use_causal_conv (bool): Whether to use causal convolution. + + """ + super(ResidualStack, self).__init__() + + # defile residual stack part + if not use_causal_conv: + assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." + self.stack = torch.nn.Sequential( + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params), + torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias), + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + torch.nn.Conv1d(channels, channels, 1, bias=bias), + ) + else: + self.stack = torch.nn.Sequential( + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + CausalConv1d(channels, channels, kernel_size, dilation=dilation, + bias=bias, pad=pad, pad_params=pad_params), + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + torch.nn.Conv1d(channels, channels, 1, bias=bias), + ) + + # defile extra layer for skip connection + self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias) + + def forward(self, c): + """Calculate forward propagation. + + Args: + c (Tensor): Input tensor (B, channels, T). + + Returns: + Tensor: Output tensor (B, chennels, T). + + """ + return self.stack(c) + self.skip_layer(c) diff --git a/NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py b/NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..c0f46bd755c161cda2ac904fe37f3f3c6357a88d --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- + +# Copyright 2020 MINH ANH (@dathudeptrai) +# MIT License (https://opensource.org/licenses/MIT) + +"""Tensorflow Layer modules complatible with pytorch.""" + +import tensorflow as tf + + +class TFReflectionPad1d(tf.keras.layers.Layer): + """Tensorflow ReflectionPad1d module.""" + + def __init__(self, padding_size): + """Initialize TFReflectionPad1d module. + + Args: + padding_size (int): Padding size. + + """ + super(TFReflectionPad1d, self).__init__() + self.padding_size = padding_size + + @tf.function + def call(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, T, 1, C). + + Returns: + Tensor: Padded tensor (B, T + 2 * padding_size, 1, C). + + """ + return tf.pad(x, [[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]], "REFLECT") + + +class TFConvTranspose1d(tf.keras.layers.Layer): + """Tensorflow ConvTranspose1d module.""" + + def __init__(self, channels, kernel_size, stride, padding): + """Initialize TFConvTranspose1d( module. + + Args: + channels (int): Number of channels. + kernel_size (int): kernel size. + strides (int): Stride width. + padding (str): Padding type ("same" or "valid"). + + """ + super(TFConvTranspose1d, self).__init__() + self.conv1d_transpose = tf.keras.layers.Conv2DTranspose( + filters=channels, + kernel_size=(kernel_size, 1), + strides=(stride, 1), + padding=padding, + ) + + @tf.function + def call(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, T, 1, C). + + Returns: + Tensors: Output tensor (B, T', 1, C'). + + """ + x = self.conv1d_transpose(x) + return x + + +class TFResidualStack(tf.keras.layers.Layer): + """Tensorflow ResidualStack module.""" + + def __init__(self, + kernel_size, + channels, + dilation, + bias, + nonlinear_activation, + nonlinear_activation_params, + padding, + ): + """Initialize TFResidualStack module. + + Args: + kernel_size (int): Kernel size. + channles (int): Number of channels. + dilation (int): Dilation ine. + bias (bool): Whether to add bias parameter in convolution layers. + nonlinear_activation (str): Activation function module name. + nonlinear_activation_params (dict): Hyperparameters for activation function. + padding (str): Padding type ("same" or "valid"). + + """ + super(TFResidualStack, self).__init__() + self.block = [ + getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params), + TFReflectionPad1d(dilation), + tf.keras.layers.Conv2D( + filters=channels, + kernel_size=(kernel_size, 1), + dilation_rate=(dilation, 1), + use_bias=bias, + padding="valid", + ), + getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params), + tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias) + ] + self.shortcut = tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias) + + @tf.function + def call(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, T, 1, C). + + Returns: + Tensor: Output tensor (B, T, 1, C). + + """ + _x = tf.identity(x) + for i, layer in enumerate(self.block): + _x = layer(_x) + shortcut = self.shortcut(x) + return shortcut + _x diff --git a/NeuralSeq/modules/parallel_wavegan/layers/upsample.py b/NeuralSeq/modules/parallel_wavegan/layers/upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..18c6397c420a81fadc5320e3a48f3249534decd8 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/layers/upsample.py @@ -0,0 +1,183 @@ +# -*- coding: utf-8 -*- + +"""Upsampling module. + +This code is modified from https://github.com/r9y9/wavenet_vocoder. + +""" + +import numpy as np +import torch +import torch.nn.functional as F + +from . import Conv1d + + +class Stretch2d(torch.nn.Module): + """Stretch2d module.""" + + def __init__(self, x_scale, y_scale, mode="nearest"): + """Initialize Stretch2d module. + + Args: + x_scale (int): X scaling factor (Time axis in spectrogram). + y_scale (int): Y scaling factor (Frequency axis in spectrogram). + mode (str): Interpolation mode. + + """ + super(Stretch2d, self).__init__() + self.x_scale = x_scale + self.y_scale = y_scale + self.mode = mode + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input tensor (B, C, F, T). + + Returns: + Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale), + + """ + return F.interpolate( + x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode) + + +class Conv2d(torch.nn.Conv2d): + """Conv2d module with customized initialization.""" + + def __init__(self, *args, **kwargs): + """Initialize Conv2d module.""" + super(Conv2d, self).__init__(*args, **kwargs) + + def reset_parameters(self): + """Reset parameters.""" + self.weight.data.fill_(1. / np.prod(self.kernel_size)) + if self.bias is not None: + torch.nn.init.constant_(self.bias, 0.0) + + +class UpsampleNetwork(torch.nn.Module): + """Upsampling network module.""" + + def __init__(self, + upsample_scales, + nonlinear_activation=None, + nonlinear_activation_params={}, + interpolate_mode="nearest", + freq_axis_kernel_size=1, + use_causal_conv=False, + ): + """Initialize upsampling network module. + + Args: + upsample_scales (list): List of upsampling scales. + nonlinear_activation (str): Activation function name. + nonlinear_activation_params (dict): Arguments for specified activation function. + interpolate_mode (str): Interpolation mode. + freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. + + """ + super(UpsampleNetwork, self).__init__() + self.use_causal_conv = use_causal_conv + self.up_layers = torch.nn.ModuleList() + for scale in upsample_scales: + # interpolation layer + stretch = Stretch2d(scale, 1, interpolate_mode) + self.up_layers += [stretch] + + # conv layer + assert (freq_axis_kernel_size - 1) % 2 == 0, "Not support even number freq axis kernel size." + freq_axis_padding = (freq_axis_kernel_size - 1) // 2 + kernel_size = (freq_axis_kernel_size, scale * 2 + 1) + if use_causal_conv: + padding = (freq_axis_padding, scale * 2) + else: + padding = (freq_axis_padding, scale) + conv = Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) + self.up_layers += [conv] + + # nonlinear + if nonlinear_activation is not None: + nonlinear = getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params) + self.up_layers += [nonlinear] + + def forward(self, c): + """Calculate forward propagation. + + Args: + c : Input tensor (B, C, T). + + Returns: + Tensor: Upsampled tensor (B, C, T'), where T' = T * prod(upsample_scales). + + """ + c = c.unsqueeze(1) # (B, 1, C, T) + for f in self.up_layers: + if self.use_causal_conv and isinstance(f, Conv2d): + c = f(c)[..., :c.size(-1)] + else: + c = f(c) + return c.squeeze(1) # (B, C, T') + + +class ConvInUpsampleNetwork(torch.nn.Module): + """Convolution + upsampling network module.""" + + def __init__(self, + upsample_scales, + nonlinear_activation=None, + nonlinear_activation_params={}, + interpolate_mode="nearest", + freq_axis_kernel_size=1, + aux_channels=80, + aux_context_window=0, + use_causal_conv=False + ): + """Initialize convolution + upsampling network module. + + Args: + upsample_scales (list): List of upsampling scales. + nonlinear_activation (str): Activation function name. + nonlinear_activation_params (dict): Arguments for specified activation function. + mode (str): Interpolation mode. + freq_axis_kernel_size (int): Kernel size in the direction of frequency axis. + aux_channels (int): Number of channels of pre-convolutional layer. + aux_context_window (int): Context window size of the pre-convolutional layer. + use_causal_conv (bool): Whether to use causal structure. + + """ + super(ConvInUpsampleNetwork, self).__init__() + self.aux_context_window = aux_context_window + self.use_causal_conv = use_causal_conv and aux_context_window > 0 + # To capture wide-context information in conditional features + kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1 + # NOTE(kan-bayashi): Here do not use padding because the input is already padded + self.conv_in = Conv1d(aux_channels, aux_channels, kernel_size=kernel_size, bias=False) + self.upsample = UpsampleNetwork( + upsample_scales=upsample_scales, + nonlinear_activation=nonlinear_activation, + nonlinear_activation_params=nonlinear_activation_params, + interpolate_mode=interpolate_mode, + freq_axis_kernel_size=freq_axis_kernel_size, + use_causal_conv=use_causal_conv, + ) + + def forward(self, c): + """Calculate forward propagation. + + Args: + c : Input tensor (B, C, T'). + + Returns: + Tensor: Upsampled tensor (B, C, T), + where T = (T' - aux_context_window * 2) * prod(upsample_scales). + + Note: + The length of inputs considers the context window size. + + """ + c_ = self.conv_in(c) + c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_ + return self.upsample(c) diff --git a/NeuralSeq/modules/parallel_wavegan/losses/__init__.py b/NeuralSeq/modules/parallel_wavegan/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b03080a907cb5cb4b316ceb74866ddbc406b33bf --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/losses/__init__.py @@ -0,0 +1 @@ +from .stft_loss import * # NOQA diff --git a/NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py b/NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..74d2aa21ad30ba094c406366e652067462f49cd2 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py @@ -0,0 +1,153 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""STFT-based Loss modules.""" + +import torch +import torch.nn.functional as F + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + + """ + x_stft = torch.stft(x, fft_size, hop_size, win_length, window) + real = x_stft[..., 0] + imag = x_stft[..., 1] + + # NOTE(kan-bayashi): clamp is needed to avoid nan or inf + return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1) + + +class SpectralConvergengeLoss(torch.nn.Module): + """Spectral convergence loss module.""" + + def __init__(self): + """Initilize spectral convergence loss module.""" + super(SpectralConvergengeLoss, self).__init__() + + def forward(self, x_mag, y_mag): + """Calculate forward propagation. + + Args: + x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). + y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). + + Returns: + Tensor: Spectral convergence loss value. + + """ + return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro") + + +class LogSTFTMagnitudeLoss(torch.nn.Module): + """Log STFT magnitude loss module.""" + + def __init__(self): + """Initilize los STFT magnitude loss module.""" + super(LogSTFTMagnitudeLoss, self).__init__() + + def forward(self, x_mag, y_mag): + """Calculate forward propagation. + + Args: + x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). + y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). + + Returns: + Tensor: Log STFT magnitude loss value. + + """ + return F.l1_loss(torch.log(y_mag), torch.log(x_mag)) + + +class STFTLoss(torch.nn.Module): + """STFT loss module.""" + + def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"): + """Initialize STFT loss module.""" + super(STFTLoss, self).__init__() + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length) + self.spectral_convergenge_loss = SpectralConvergengeLoss() + self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() + + def forward(self, x, y): + """Calculate forward propagation. + + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + + Returns: + Tensor: Spectral convergence loss value. + Tensor: Log STFT magnitude loss value. + + """ + x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) + y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) + sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) + mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) + + return sc_loss, mag_loss + + +class MultiResolutionSTFTLoss(torch.nn.Module): + """Multi resolution STFT loss module.""" + + def __init__(self, + fft_sizes=[1024, 2048, 512], + hop_sizes=[120, 240, 50], + win_lengths=[600, 1200, 240], + window="hann_window"): + """Initialize Multi resolution STFT loss module. + + Args: + fft_sizes (list): List of FFT sizes. + hop_sizes (list): List of hop sizes. + win_lengths (list): List of window lengths. + window (str): Window function type. + + """ + super(MultiResolutionSTFTLoss, self).__init__() + assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) + self.stft_losses = torch.nn.ModuleList() + for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): + self.stft_losses += [STFTLoss(fs, ss, wl, window)] + + def forward(self, x, y): + """Calculate forward propagation. + + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + + Returns: + Tensor: Multi resolution spectral convergence loss value. + Tensor: Multi resolution log STFT magnitude loss value. + + """ + sc_loss = 0.0 + mag_loss = 0.0 + for f in self.stft_losses: + sc_l, mag_l = f(x, y) + sc_loss += sc_l + mag_loss += mag_l + sc_loss /= len(self.stft_losses) + mag_loss /= len(self.stft_losses) + + return sc_loss, mag_loss diff --git a/NeuralSeq/modules/parallel_wavegan/models/__init__.py b/NeuralSeq/modules/parallel_wavegan/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4803ba6b2a0afc8022e756ae5b3f4c7403c3c1bd --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/models/__init__.py @@ -0,0 +1,2 @@ +from .melgan import * # NOQA +from .parallel_wavegan import * # NOQA diff --git a/NeuralSeq/modules/parallel_wavegan/models/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/modules/parallel_wavegan/models/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..154de3ad4893273bc4d7e59c300914b23b88b3f7 Binary files /dev/null and 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a/NeuralSeq/modules/parallel_wavegan/models/melgan.py b/NeuralSeq/modules/parallel_wavegan/models/melgan.py new file mode 100644 index 0000000000000000000000000000000000000000..e021ae4817a8c1c97338e61b00b230c881836fd8 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/models/melgan.py @@ -0,0 +1,427 @@ +# -*- coding: utf-8 -*- + +# Copyright 2020 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""MelGAN Modules.""" + +import logging + +import numpy as np +import torch + +from modules.parallel_wavegan.layers import CausalConv1d +from modules.parallel_wavegan.layers import CausalConvTranspose1d +from modules.parallel_wavegan.layers import ResidualStack + + +class MelGANGenerator(torch.nn.Module): + """MelGAN generator module.""" + + def __init__(self, + in_channels=80, + out_channels=1, + kernel_size=7, + channels=512, + bias=True, + upsample_scales=[8, 8, 2, 2], + stack_kernel_size=3, + stacks=3, + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + pad="ReflectionPad1d", + pad_params={}, + use_final_nonlinear_activation=True, + use_weight_norm=True, + use_causal_conv=False, + ): + """Initialize MelGANGenerator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + kernel_size (int): Kernel size of initial and final conv layer. + channels (int): Initial number of channels for conv layer. + bias (bool): Whether to add bias parameter in convolution layers. + upsample_scales (list): List of upsampling scales. + stack_kernel_size (int): Kernel size of dilated conv layers in residual stack. + stacks (int): Number of stacks in a single residual stack. + nonlinear_activation (str): Activation function module name. + nonlinear_activation_params (dict): Hyperparameters for activation function. + pad (str): Padding function module name before dilated convolution layer. + pad_params (dict): Hyperparameters for padding function. + use_final_nonlinear_activation (torch.nn.Module): Activation function for the final layer. + use_weight_norm (bool): Whether to use weight norm. + If set to true, it will be applied to all of the conv layers. + use_causal_conv (bool): Whether to use causal convolution. + + """ + super(MelGANGenerator, self).__init__() + + # check hyper parameters is valid + assert channels >= np.prod(upsample_scales) + assert channels % (2 ** len(upsample_scales)) == 0 + if not use_causal_conv: + assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." + + # add initial layer + layers = [] + if not use_causal_conv: + layers += [ + getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params), + torch.nn.Conv1d(in_channels, channels, kernel_size, bias=bias), + ] + else: + layers += [ + CausalConv1d(in_channels, channels, kernel_size, + bias=bias, pad=pad, pad_params=pad_params), + ] + + for i, upsample_scale in enumerate(upsample_scales): + # add upsampling layer + layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)] + if not use_causal_conv: + layers += [ + torch.nn.ConvTranspose1d( + channels // (2 ** i), + channels // (2 ** (i + 1)), + upsample_scale * 2, + stride=upsample_scale, + padding=upsample_scale // 2 + upsample_scale % 2, + output_padding=upsample_scale % 2, + bias=bias, + ) + ] + else: + layers += [ + CausalConvTranspose1d( + channels // (2 ** i), + channels // (2 ** (i + 1)), + upsample_scale * 2, + stride=upsample_scale, + bias=bias, + ) + ] + + # add residual stack + for j in range(stacks): + layers += [ + ResidualStack( + kernel_size=stack_kernel_size, + channels=channels // (2 ** (i + 1)), + dilation=stack_kernel_size ** j, + bias=bias, + nonlinear_activation=nonlinear_activation, + nonlinear_activation_params=nonlinear_activation_params, + pad=pad, + pad_params=pad_params, + use_causal_conv=use_causal_conv, + ) + ] + + # add final layer + layers += [getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params)] + if not use_causal_conv: + layers += [ + getattr(torch.nn, pad)((kernel_size - 1) // 2, **pad_params), + torch.nn.Conv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, bias=bias), + ] + else: + layers += [ + CausalConv1d(channels // (2 ** (i + 1)), out_channels, kernel_size, + bias=bias, pad=pad, pad_params=pad_params), + ] + if use_final_nonlinear_activation: + layers += [torch.nn.Tanh()] + + # define the model as a single function + self.melgan = torch.nn.Sequential(*layers) + + # apply weight norm + if use_weight_norm: + self.apply_weight_norm() + + # reset parameters + self.reset_parameters() + + def forward(self, c): + """Calculate forward propagation. + + Args: + c (Tensor): Input tensor (B, channels, T). + + Returns: + Tensor: Output tensor (B, 1, T ** prod(upsample_scales)). + + """ + return self.melgan(c) + + def remove_weight_norm(self): + """Remove weight normalization module from all of the layers.""" + def _remove_weight_norm(m): + try: + logging.debug(f"Weight norm is removed from {m}.") + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(_remove_weight_norm) + + def apply_weight_norm(self): + """Apply weight normalization module from all of the layers.""" + def _apply_weight_norm(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): + torch.nn.utils.weight_norm(m) + logging.debug(f"Weight norm is applied to {m}.") + + self.apply(_apply_weight_norm) + + def reset_parameters(self): + """Reset parameters. + + This initialization follows official implementation manner. + https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py + + """ + def _reset_parameters(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): + m.weight.data.normal_(0.0, 0.02) + logging.debug(f"Reset parameters in {m}.") + + self.apply(_reset_parameters) + + +class MelGANDiscriminator(torch.nn.Module): + """MelGAN discriminator module.""" + + def __init__(self, + in_channels=1, + out_channels=1, + kernel_sizes=[5, 3], + channels=16, + max_downsample_channels=1024, + bias=True, + downsample_scales=[4, 4, 4, 4], + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + pad="ReflectionPad1d", + pad_params={}, + ): + """Initilize MelGAN discriminator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer, + and the first and the second kernel sizes will be used for the last two layers. + For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15, + the last two layers' kernel size will be 5 and 3, respectively. + channels (int): Initial number of channels for conv layer. + max_downsample_channels (int): Maximum number of channels for downsampling layers. + bias (bool): Whether to add bias parameter in convolution layers. + downsample_scales (list): List of downsampling scales. + nonlinear_activation (str): Activation function module name. + nonlinear_activation_params (dict): Hyperparameters for activation function. + pad (str): Padding function module name before dilated convolution layer. + pad_params (dict): Hyperparameters for padding function. + + """ + super(MelGANDiscriminator, self).__init__() + self.layers = torch.nn.ModuleList() + + # check kernel size is valid + assert len(kernel_sizes) == 2 + assert kernel_sizes[0] % 2 == 1 + assert kernel_sizes[1] % 2 == 1 + + # add first layer + self.layers += [ + torch.nn.Sequential( + getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params), + torch.nn.Conv1d(in_channels, channels, np.prod(kernel_sizes), bias=bias), + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + ) + ] + + # add downsample layers + in_chs = channels + for downsample_scale in downsample_scales: + out_chs = min(in_chs * downsample_scale, max_downsample_channels) + self.layers += [ + torch.nn.Sequential( + torch.nn.Conv1d( + in_chs, out_chs, + kernel_size=downsample_scale * 10 + 1, + stride=downsample_scale, + padding=downsample_scale * 5, + groups=in_chs // 4, + bias=bias, + ), + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + ) + ] + in_chs = out_chs + + # add final layers + out_chs = min(in_chs * 2, max_downsample_channels) + self.layers += [ + torch.nn.Sequential( + torch.nn.Conv1d( + in_chs, out_chs, kernel_sizes[0], + padding=(kernel_sizes[0] - 1) // 2, + bias=bias, + ), + getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), + ) + ] + self.layers += [ + torch.nn.Conv1d( + out_chs, out_channels, kernel_sizes[1], + padding=(kernel_sizes[1] - 1) // 2, + bias=bias, + ), + ] + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input noise signal (B, 1, T). + + Returns: + List: List of output tensors of each layer. + + """ + outs = [] + for f in self.layers: + x = f(x) + outs += [x] + + return outs + + +class MelGANMultiScaleDiscriminator(torch.nn.Module): + """MelGAN multi-scale discriminator module.""" + + def __init__(self, + in_channels=1, + out_channels=1, + scales=3, + downsample_pooling="AvgPool1d", + # follow the official implementation setting + downsample_pooling_params={ + "kernel_size": 4, + "stride": 2, + "padding": 1, + "count_include_pad": False, + }, + kernel_sizes=[5, 3], + channels=16, + max_downsample_channels=1024, + bias=True, + downsample_scales=[4, 4, 4, 4], + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + pad="ReflectionPad1d", + pad_params={}, + use_weight_norm=True, + ): + """Initilize MelGAN multi-scale discriminator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + downsample_pooling (str): Pooling module name for downsampling of the inputs. + downsample_pooling_params (dict): Parameters for the above pooling module. + kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer, + and the first and the second kernel sizes will be used for the last two layers. + channels (int): Initial number of channels for conv layer. + max_downsample_channels (int): Maximum number of channels for downsampling layers. + bias (bool): Whether to add bias parameter in convolution layers. + downsample_scales (list): List of downsampling scales. + nonlinear_activation (str): Activation function module name. + nonlinear_activation_params (dict): Hyperparameters for activation function. + pad (str): Padding function module name before dilated convolution layer. + pad_params (dict): Hyperparameters for padding function. + use_causal_conv (bool): Whether to use causal convolution. + + """ + super(MelGANMultiScaleDiscriminator, self).__init__() + self.discriminators = torch.nn.ModuleList() + + # add discriminators + for _ in range(scales): + self.discriminators += [ + MelGANDiscriminator( + in_channels=in_channels, + out_channels=out_channels, + kernel_sizes=kernel_sizes, + channels=channels, + max_downsample_channels=max_downsample_channels, + bias=bias, + downsample_scales=downsample_scales, + nonlinear_activation=nonlinear_activation, + nonlinear_activation_params=nonlinear_activation_params, + pad=pad, + pad_params=pad_params, + ) + ] + self.pooling = getattr(torch.nn, downsample_pooling)(**downsample_pooling_params) + + # apply weight norm + if use_weight_norm: + self.apply_weight_norm() + + # reset parameters + self.reset_parameters() + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input noise signal (B, 1, T). + + Returns: + List: List of list of each discriminator outputs, which consists of each layer output tensors. + + """ + outs = [] + for f in self.discriminators: + outs += [f(x)] + x = self.pooling(x) + + return outs + + def remove_weight_norm(self): + """Remove weight normalization module from all of the layers.""" + def _remove_weight_norm(m): + try: + logging.debug(f"Weight norm is removed from {m}.") + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(_remove_weight_norm) + + def apply_weight_norm(self): + """Apply weight normalization module from all of the layers.""" + def _apply_weight_norm(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): + torch.nn.utils.weight_norm(m) + logging.debug(f"Weight norm is applied to {m}.") + + self.apply(_apply_weight_norm) + + def reset_parameters(self): + """Reset parameters. + + This initialization follows official implementation manner. + https://github.com/descriptinc/melgan-neurips/blob/master/spec2wav/modules.py + + """ + def _reset_parameters(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): + m.weight.data.normal_(0.0, 0.02) + logging.debug(f"Reset parameters in {m}.") + + self.apply(_reset_parameters) diff --git a/NeuralSeq/modules/parallel_wavegan/models/parallel_wavegan.py b/NeuralSeq/modules/parallel_wavegan/models/parallel_wavegan.py new file mode 100644 index 0000000000000000000000000000000000000000..c63b59f67aa48342179415c1d1beac68574a5498 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/models/parallel_wavegan.py @@ -0,0 +1,434 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""Parallel WaveGAN Modules.""" + +import logging +import math + +import torch +from torch import nn + +from modules.parallel_wavegan.layers import Conv1d +from modules.parallel_wavegan.layers import Conv1d1x1 +from modules.parallel_wavegan.layers import ResidualBlock +from modules.parallel_wavegan.layers import upsample +from modules.parallel_wavegan import models + + +class ParallelWaveGANGenerator(torch.nn.Module): + """Parallel WaveGAN Generator module.""" + + def __init__(self, + in_channels=1, + out_channels=1, + kernel_size=3, + layers=30, + stacks=3, + residual_channels=64, + gate_channels=128, + skip_channels=64, + aux_channels=80, + aux_context_window=2, + dropout=0.0, + bias=True, + use_weight_norm=True, + use_causal_conv=False, + upsample_conditional_features=True, + upsample_net="ConvInUpsampleNetwork", + upsample_params={"upsample_scales": [4, 4, 4, 4]}, + use_pitch_embed=False, + ): + """Initialize Parallel WaveGAN Generator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + kernel_size (int): Kernel size of dilated convolution. + layers (int): Number of residual block layers. + stacks (int): Number of stacks i.e., dilation cycles. + residual_channels (int): Number of channels in residual conv. + gate_channels (int): Number of channels in gated conv. + skip_channels (int): Number of channels in skip conv. + aux_channels (int): Number of channels for auxiliary feature conv. + aux_context_window (int): Context window size for auxiliary feature. + dropout (float): Dropout rate. 0.0 means no dropout applied. + bias (bool): Whether to use bias parameter in conv layer. + use_weight_norm (bool): Whether to use weight norm. + If set to true, it will be applied to all of the conv layers. + use_causal_conv (bool): Whether to use causal structure. + upsample_conditional_features (bool): Whether to use upsampling network. + upsample_net (str): Upsampling network architecture. + upsample_params (dict): Upsampling network parameters. + + """ + super(ParallelWaveGANGenerator, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.aux_channels = aux_channels + self.layers = layers + self.stacks = stacks + self.kernel_size = kernel_size + + # check the number of layers and stacks + assert layers % stacks == 0 + layers_per_stack = layers // stacks + + # define first convolution + self.first_conv = Conv1d1x1(in_channels, residual_channels, bias=True) + + # define conv + upsampling network + if upsample_conditional_features: + upsample_params.update({ + "use_causal_conv": use_causal_conv, + }) + if upsample_net == "MelGANGenerator": + assert aux_context_window == 0 + upsample_params.update({ + "use_weight_norm": False, # not to apply twice + "use_final_nonlinear_activation": False, + }) + self.upsample_net = getattr(models, upsample_net)(**upsample_params) + else: + if upsample_net == "ConvInUpsampleNetwork": + upsample_params.update({ + "aux_channels": aux_channels, + "aux_context_window": aux_context_window, + }) + self.upsample_net = getattr(upsample, upsample_net)(**upsample_params) + else: + self.upsample_net = None + + # define residual blocks + self.conv_layers = torch.nn.ModuleList() + for layer in range(layers): + dilation = 2 ** (layer % layers_per_stack) + conv = ResidualBlock( + kernel_size=kernel_size, + residual_channels=residual_channels, + gate_channels=gate_channels, + skip_channels=skip_channels, + aux_channels=aux_channels, + dilation=dilation, + dropout=dropout, + bias=bias, + use_causal_conv=use_causal_conv, + ) + self.conv_layers += [conv] + + # define output layers + self.last_conv_layers = torch.nn.ModuleList([ + torch.nn.ReLU(inplace=True), + Conv1d1x1(skip_channels, skip_channels, bias=True), + torch.nn.ReLU(inplace=True), + Conv1d1x1(skip_channels, out_channels, bias=True), + ]) + + self.use_pitch_embed = use_pitch_embed + if use_pitch_embed: + self.pitch_embed = nn.Embedding(300, aux_channels, 0) + self.c_proj = nn.Linear(2 * aux_channels, aux_channels) + + # apply weight norm + if use_weight_norm: + self.apply_weight_norm() + + def forward(self, x, c=None, pitch=None, **kwargs): + """Calculate forward propagation. + + Args: + x (Tensor): Input noise signal (B, C_in, T). + c (Tensor): Local conditioning auxiliary features (B, C ,T'). + pitch (Tensor): Local conditioning pitch (B, T'). + + Returns: + Tensor: Output tensor (B, C_out, T) + + """ + # perform upsampling + if c is not None and self.upsample_net is not None: + if self.use_pitch_embed: + p = self.pitch_embed(pitch) + c = self.c_proj(torch.cat([c.transpose(1, 2), p], -1)).transpose(1, 2) + c = self.upsample_net(c) + assert c.size(-1) == x.size(-1), (c.size(-1), x.size(-1)) + + # encode to hidden representation + x = self.first_conv(x) + skips = 0 + for f in self.conv_layers: + x, h = f(x, c) + skips += h + skips *= math.sqrt(1.0 / len(self.conv_layers)) + + # apply final layers + x = skips + for f in self.last_conv_layers: + x = f(x) + + return x + + def remove_weight_norm(self): + """Remove weight normalization module from all of the layers.""" + def _remove_weight_norm(m): + try: + logging.debug(f"Weight norm is removed from {m}.") + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(_remove_weight_norm) + + def apply_weight_norm(self): + """Apply weight normalization module from all of the layers.""" + def _apply_weight_norm(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): + torch.nn.utils.weight_norm(m) + logging.debug(f"Weight norm is applied to {m}.") + + self.apply(_apply_weight_norm) + + @staticmethod + def _get_receptive_field_size(layers, stacks, kernel_size, + dilation=lambda x: 2 ** x): + assert layers % stacks == 0 + layers_per_cycle = layers // stacks + dilations = [dilation(i % layers_per_cycle) for i in range(layers)] + return (kernel_size - 1) * sum(dilations) + 1 + + @property + def receptive_field_size(self): + """Return receptive field size.""" + return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size) + + +class ParallelWaveGANDiscriminator(torch.nn.Module): + """Parallel WaveGAN Discriminator module.""" + + def __init__(self, + in_channels=1, + out_channels=1, + kernel_size=3, + layers=10, + conv_channels=64, + dilation_factor=1, + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + bias=True, + use_weight_norm=True, + ): + """Initialize Parallel WaveGAN Discriminator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + kernel_size (int): Number of output channels. + layers (int): Number of conv layers. + conv_channels (int): Number of chnn layers. + dilation_factor (int): Dilation factor. For example, if dilation_factor = 2, + the dilation will be 2, 4, 8, ..., and so on. + nonlinear_activation (str): Nonlinear function after each conv. + nonlinear_activation_params (dict): Nonlinear function parameters + bias (bool): Whether to use bias parameter in conv. + use_weight_norm (bool) Whether to use weight norm. + If set to true, it will be applied to all of the conv layers. + + """ + super(ParallelWaveGANDiscriminator, self).__init__() + assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." + assert dilation_factor > 0, "Dilation factor must be > 0." + self.conv_layers = torch.nn.ModuleList() + conv_in_channels = in_channels + for i in range(layers - 1): + if i == 0: + dilation = 1 + else: + dilation = i if dilation_factor == 1 else dilation_factor ** i + conv_in_channels = conv_channels + padding = (kernel_size - 1) // 2 * dilation + conv_layer = [ + Conv1d(conv_in_channels, conv_channels, + kernel_size=kernel_size, padding=padding, + dilation=dilation, bias=bias), + getattr(torch.nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params) + ] + self.conv_layers += conv_layer + padding = (kernel_size - 1) // 2 + last_conv_layer = Conv1d( + conv_in_channels, out_channels, + kernel_size=kernel_size, padding=padding, bias=bias) + self.conv_layers += [last_conv_layer] + + # apply weight norm + if use_weight_norm: + self.apply_weight_norm() + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input noise signal (B, 1, T). + + Returns: + Tensor: Output tensor (B, 1, T) + + """ + for f in self.conv_layers: + x = f(x) + return x + + def apply_weight_norm(self): + """Apply weight normalization module from all of the layers.""" + def _apply_weight_norm(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): + torch.nn.utils.weight_norm(m) + logging.debug(f"Weight norm is applied to {m}.") + + self.apply(_apply_weight_norm) + + def remove_weight_norm(self): + """Remove weight normalization module from all of the layers.""" + def _remove_weight_norm(m): + try: + logging.debug(f"Weight norm is removed from {m}.") + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(_remove_weight_norm) + + +class ResidualParallelWaveGANDiscriminator(torch.nn.Module): + """Parallel WaveGAN Discriminator module.""" + + def __init__(self, + in_channels=1, + out_channels=1, + kernel_size=3, + layers=30, + stacks=3, + residual_channels=64, + gate_channels=128, + skip_channels=64, + dropout=0.0, + bias=True, + use_weight_norm=True, + use_causal_conv=False, + nonlinear_activation="LeakyReLU", + nonlinear_activation_params={"negative_slope": 0.2}, + ): + """Initialize Parallel WaveGAN Discriminator module. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + kernel_size (int): Kernel size of dilated convolution. + layers (int): Number of residual block layers. + stacks (int): Number of stacks i.e., dilation cycles. + residual_channels (int): Number of channels in residual conv. + gate_channels (int): Number of channels in gated conv. + skip_channels (int): Number of channels in skip conv. + dropout (float): Dropout rate. 0.0 means no dropout applied. + bias (bool): Whether to use bias parameter in conv. + use_weight_norm (bool): Whether to use weight norm. + If set to true, it will be applied to all of the conv layers. + use_causal_conv (bool): Whether to use causal structure. + nonlinear_activation_params (dict): Nonlinear function parameters + + """ + super(ResidualParallelWaveGANDiscriminator, self).__init__() + assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." + + self.in_channels = in_channels + self.out_channels = out_channels + self.layers = layers + self.stacks = stacks + self.kernel_size = kernel_size + + # check the number of layers and stacks + assert layers % stacks == 0 + layers_per_stack = layers // stacks + + # define first convolution + self.first_conv = torch.nn.Sequential( + Conv1d1x1(in_channels, residual_channels, bias=True), + getattr(torch.nn, nonlinear_activation)( + inplace=True, **nonlinear_activation_params), + ) + + # define residual blocks + self.conv_layers = torch.nn.ModuleList() + for layer in range(layers): + dilation = 2 ** (layer % layers_per_stack) + conv = ResidualBlock( + kernel_size=kernel_size, + residual_channels=residual_channels, + gate_channels=gate_channels, + skip_channels=skip_channels, + aux_channels=-1, + dilation=dilation, + dropout=dropout, + bias=bias, + use_causal_conv=use_causal_conv, + ) + self.conv_layers += [conv] + + # define output layers + self.last_conv_layers = torch.nn.ModuleList([ + getattr(torch.nn, nonlinear_activation)( + inplace=True, **nonlinear_activation_params), + Conv1d1x1(skip_channels, skip_channels, bias=True), + getattr(torch.nn, nonlinear_activation)( + inplace=True, **nonlinear_activation_params), + Conv1d1x1(skip_channels, out_channels, bias=True), + ]) + + # apply weight norm + if use_weight_norm: + self.apply_weight_norm() + + def forward(self, x): + """Calculate forward propagation. + + Args: + x (Tensor): Input noise signal (B, 1, T). + + Returns: + Tensor: Output tensor (B, 1, T) + + """ + x = self.first_conv(x) + + skips = 0 + for f in self.conv_layers: + x, h = f(x, None) + skips += h + skips *= math.sqrt(1.0 / len(self.conv_layers)) + + # apply final layers + x = skips + for f in self.last_conv_layers: + x = f(x) + return x + + def apply_weight_norm(self): + """Apply weight normalization module from all of the layers.""" + def _apply_weight_norm(m): + if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d): + torch.nn.utils.weight_norm(m) + logging.debug(f"Weight norm is applied to {m}.") + + self.apply(_apply_weight_norm) + + def remove_weight_norm(self): + """Remove weight normalization module from all of the layers.""" + def _remove_weight_norm(m): + try: + logging.debug(f"Weight norm is removed from {m}.") + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(_remove_weight_norm) diff --git a/NeuralSeq/modules/parallel_wavegan/models/source.py b/NeuralSeq/modules/parallel_wavegan/models/source.py new file mode 100644 index 0000000000000000000000000000000000000000..f2a006e53c0e2194036fd08ea9d6ed4d9a10d6cf --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/models/source.py @@ -0,0 +1,538 @@ +import torch +import numpy as np +import sys +import torch.nn.functional as torch_nn_func + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + + def _f02uv(self, f0): + # generate uv signal + uv = torch.ones_like(f0) + uv = uv * (f0 > self.voiced_threshold) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ + device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + # for normal case + + # To prevent torch.cumsum numerical overflow, + # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. + # Buffer tmp_over_one_idx indicates the time step to add -1. + # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi + tmp_over_one = torch.cumsum(rad_values, 1) % 1 + tmp_over_one_idx = (tmp_over_one[:, 1:, :] - + tmp_over_one[:, :-1, :]) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + + sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) + * 2 * np.pi) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + with torch.no_grad(): + f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, + device=f0.device) + # fundamental component + f0_buf[:, :, 0] = f0[:, :, 0] + for idx in np.arange(self.harmonic_num): + # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic + f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) + + # generate sine waveforms + sine_waves = self._f02sine(f0_buf) * self.sine_amp + + # generate uv signal + # uv = torch.ones(f0.shape) + # uv = uv * (f0 > self.voiced_threshold) + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class PulseGen(torch.nn.Module): + """ Definition of Pulse train generator + + There are many ways to implement pulse generator. + Here, PulseGen is based on SinGen. For a perfect + """ + def __init__(self, samp_rate, pulse_amp = 0.1, + noise_std = 0.003, voiced_threshold = 0): + super(PulseGen, self).__init__() + self.pulse_amp = pulse_amp + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.noise_std = noise_std + self.l_sinegen = SineGen(self.sampling_rate, harmonic_num=0, \ + sine_amp=self.pulse_amp, noise_std=0, \ + voiced_threshold=self.voiced_threshold, \ + flag_for_pulse=True) + + def forward(self, f0): + """ Pulse train generator + pulse_train, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output pulse_train: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + + Note: self.l_sine doesn't make sure that the initial phase of + a voiced segment is np.pi, the first pulse in a voiced segment + may not be at the first time step within a voiced segment + """ + with torch.no_grad(): + sine_wav, uv, noise = self.l_sinegen(f0) + + # sine without additive noise + pure_sine = sine_wav - noise + + # step t corresponds to a pulse if + # sine[t] > sine[t+1] & sine[t] > sine[t-1] + # & sine[t-1], sine[t+1], and sine[t] are voiced + # or + # sine[t] is voiced, sine[t-1] is unvoiced + # we use torch.roll to simulate sine[t+1] and sine[t-1] + sine_1 = torch.roll(pure_sine, shifts=1, dims=1) + uv_1 = torch.roll(uv, shifts=1, dims=1) + uv_1[:, 0, :] = 0 + sine_2 = torch.roll(pure_sine, shifts=-1, dims=1) + uv_2 = torch.roll(uv, shifts=-1, dims=1) + uv_2[:, -1, :] = 0 + + loc = (pure_sine > sine_1) * (pure_sine > sine_2) \ + * (uv_1 > 0) * (uv_2 > 0) * (uv > 0) \ + + (uv_1 < 1) * (uv > 0) + + # pulse train without noise + pulse_train = pure_sine * loc + + # additive noise to pulse train + # note that noise from sinegen is zero in voiced regions + pulse_noise = torch.randn_like(pure_sine) * self.noise_std + + # with additive noise on pulse, and unvoiced regions + pulse_train += pulse_noise * loc + pulse_noise * (1 - uv) + return pulse_train, sine_wav, uv, pulse_noise + + +class SignalsConv1d(torch.nn.Module): + """ Filtering input signal with time invariant filter + Note: FIRFilter conducted filtering given fixed FIR weight + SignalsConv1d convolves two signals + Note: this is based on torch.nn.functional.conv1d + + """ + + def __init__(self): + super(SignalsConv1d, self).__init__() + + def forward(self, signal, system_ir): + """ output = forward(signal, system_ir) + + signal: (batchsize, length1, dim) + system_ir: (length2, dim) + + output: (batchsize, length1, dim) + """ + if signal.shape[-1] != system_ir.shape[-1]: + print("Error: SignalsConv1d expects shape:") + print("signal (batchsize, length1, dim)") + print("system_id (batchsize, length2, dim)") + print("But received signal: {:s}".format(str(signal.shape))) + print(" system_ir: {:s}".format(str(system_ir.shape))) + sys.exit(1) + padding_length = system_ir.shape[0] - 1 + groups = signal.shape[-1] + + # pad signal on the left + signal_pad = torch_nn_func.pad(signal.permute(0, 2, 1), \ + (padding_length, 0)) + # prepare system impulse response as (dim, 1, length2) + # also flip the impulse response + ir = torch.flip(system_ir.unsqueeze(1).permute(2, 1, 0), \ + dims=[2]) + # convolute + output = torch_nn_func.conv1d(signal_pad, ir, groups=groups) + return output.permute(0, 2, 1) + + +class CyclicNoiseGen_v1(torch.nn.Module): + """ CyclicnoiseGen_v1 + Cyclic noise with a single parameter of beta. + Pytorch v1 implementation assumes f_t is also fixed + """ + + def __init__(self, samp_rate, + noise_std=0.003, voiced_threshold=0): + super(CyclicNoiseGen_v1, self).__init__() + self.samp_rate = samp_rate + self.noise_std = noise_std + self.voiced_threshold = voiced_threshold + + self.l_pulse = PulseGen(samp_rate, pulse_amp=1.0, + noise_std=noise_std, + voiced_threshold=voiced_threshold) + self.l_conv = SignalsConv1d() + + def noise_decay(self, beta, f0mean): + """ decayed_noise = noise_decay(beta, f0mean) + decayed_noise = n[t]exp(-t * f_mean / beta / samp_rate) + + beta: (dim=1) or (batchsize=1, 1, dim=1) + f0mean (batchsize=1, 1, dim=1) + + decayed_noise (batchsize=1, length, dim=1) + """ + with torch.no_grad(): + # exp(-1.0 n / T) < 0.01 => n > -log(0.01)*T = 4.60*T + # truncate the noise when decayed by -40 dB + length = 4.6 * self.samp_rate / f0mean + length = length.int() + time_idx = torch.arange(0, length, device=beta.device) + time_idx = time_idx.unsqueeze(0).unsqueeze(2) + time_idx = time_idx.repeat(beta.shape[0], 1, beta.shape[2]) + + noise = torch.randn(time_idx.shape, device=beta.device) + + # due to Pytorch implementation, use f0_mean as the f0 factor + decay = torch.exp(-time_idx * f0mean / beta / self.samp_rate) + return noise * self.noise_std * decay + + def forward(self, f0s, beta): + """ Producde cyclic-noise + """ + # pulse train + pulse_train, sine_wav, uv, noise = self.l_pulse(f0s) + pure_pulse = pulse_train - noise + + # decayed_noise (length, dim=1) + if (uv < 1).all(): + # all unvoiced + cyc_noise = torch.zeros_like(sine_wav) + else: + f0mean = f0s[uv > 0].mean() + + decayed_noise = self.noise_decay(beta, f0mean)[0, :, :] + # convolute + cyc_noise = self.l_conv(pure_pulse, decayed_noise) + + # add noise in invoiced segments + cyc_noise = cyc_noise + noise * (1.0 - uv) + return cyc_noise, pulse_train, sine_wav, uv, noise + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + + def _f02uv(self, f0): + # generate uv signal + uv = torch.ones_like(f0) + uv = uv * (f0 > self.voiced_threshold) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ + device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + # for normal case + + # To prevent torch.cumsum numerical overflow, + # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. + # Buffer tmp_over_one_idx indicates the time step to add -1. + # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi + tmp_over_one = torch.cumsum(rad_values, 1) % 1 + tmp_over_one_idx = (tmp_over_one[:, 1:, :] - + tmp_over_one[:, :-1, :]) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + + sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) + * 2 * np.pi) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + with torch.no_grad(): + f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, \ + device=f0.device) + # fundamental component + f0_buf[:, :, 0] = f0[:, :, 0] + for idx in np.arange(self.harmonic_num): + # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic + f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2) + + # generate sine waveforms + sine_waves = self._f02sine(f0_buf) * self.sine_amp + + # generate uv signal + # uv = torch.ones(f0.shape) + # uv = uv * (f0 > self.voiced_threshold) + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleCycNoise_v1(torch.nn.Module): + """ SourceModuleCycNoise_v1 + SourceModule(sampling_rate, noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + + noise_std: std of Gaussian noise (default: 0.003) + voiced_threshold: threshold to set U/V given F0 (default: 0) + + cyc, noise, uv = SourceModuleCycNoise_v1(F0_upsampled, beta) + F0_upsampled (batchsize, length, 1) + beta (1) + cyc (batchsize, length, 1) + noise (batchsize, length, 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0): + super(SourceModuleCycNoise_v1, self).__init__() + self.sampling_rate = sampling_rate + self.noise_std = noise_std + self.l_cyc_gen = CyclicNoiseGen_v1(sampling_rate, noise_std, + voiced_threshod) + + def forward(self, f0_upsamped, beta): + """ + cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta) + F0_upsampled (batchsize, length, 1) + beta (1) + cyc (batchsize, length, 1) + noise (batchsize, length, 1) + uv (batchsize, length, 1) + """ + # source for harmonic branch + cyc, pulse, sine, uv, add_noi = self.l_cyc_gen(f0_upsamped, beta) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.noise_std / 3 + return cyc, noise, uv + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +if __name__ == '__main__': + source = SourceModuleCycNoise_v1(24000) + x = torch.randn(16, 25600, 1) + + diff --git a/NeuralSeq/modules/parallel_wavegan/optimizers/__init__.py b/NeuralSeq/modules/parallel_wavegan/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e0c5932838281e912079e5784d84d43444a61a --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/optimizers/__init__.py @@ -0,0 +1,2 @@ +from torch.optim import * # NOQA +from .radam import * # NOQA diff --git a/NeuralSeq/modules/parallel_wavegan/optimizers/radam.py b/NeuralSeq/modules/parallel_wavegan/optimizers/radam.py new file mode 100644 index 0000000000000000000000000000000000000000..e805d7e34921bee436e1e7fd9e1f753c7609186b --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/optimizers/radam.py @@ -0,0 +1,91 @@ +# -*- coding: utf-8 -*- + +"""RAdam optimizer. + +This code is drived from https://github.com/LiyuanLucasLiu/RAdam. +""" + +import math +import torch + +from torch.optim.optimizer import Optimizer + + +class RAdam(Optimizer): + """Rectified Adam optimizer.""" + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): + """Initilize RAdam optimizer.""" + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + self.buffer = [[None, None, None] for ind in range(10)] + super(RAdam, self).__init__(params, defaults) + + def __setstate__(self, state): + """Set state.""" + super(RAdam, self).__setstate__(state) + + def step(self, closure=None): + """Run one step.""" + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + if grad.is_sparse: + raise RuntimeError('RAdam does not support sparse gradients') + + p_data_fp32 = p.data.float() + + state = self.state[p] + + if len(state) == 0: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + state['step'] += 1 + buffered = self.buffer[int(state['step'] % 10)] + if state['step'] == buffered[0]: + N_sma, step_size = buffered[1], buffered[2] + else: + buffered[0] = state['step'] + beta2_t = beta2 ** state['step'] + N_sma_max = 2 / (1 - beta2) - 1 + N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) + buffered[1] = N_sma + + # more conservative since it's an approximated value + if N_sma >= 5: + step_size = math.sqrt( + (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) # NOQA + else: + step_size = 1.0 / (1 - beta1 ** state['step']) + buffered[2] = step_size + + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + + # more conservative since it's an approximated value + if N_sma >= 5: + denom = exp_avg_sq.sqrt().add_(group['eps']) + p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom) + else: + p_data_fp32.add_(-step_size * group['lr'], exp_avg) + + p.data.copy_(p_data_fp32) + + return loss diff --git a/NeuralSeq/modules/parallel_wavegan/stft_loss.py b/NeuralSeq/modules/parallel_wavegan/stft_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..229e6c777dc9ec7f710842d1e648dba1189ec8b4 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/stft_loss.py @@ -0,0 +1,100 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""STFT-based Loss modules.""" +import librosa +import torch + +from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft + + +class STFTLoss(torch.nn.Module): + """STFT loss module.""" + + def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", + use_mel_loss=False): + """Initialize STFT loss module.""" + super(STFTLoss, self).__init__() + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length) + self.spectral_convergenge_loss = SpectralConvergengeLoss() + self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() + self.use_mel_loss = use_mel_loss + self.mel_basis = None + + def forward(self, x, y): + """Calculate forward propagation. + + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + + Returns: + Tensor: Spectral convergence loss value. + Tensor: Log STFT magnitude loss value. + + """ + x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) + y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) + if self.use_mel_loss: + if self.mel_basis is None: + self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T + x_mag = x_mag @ self.mel_basis + y_mag = y_mag @ self.mel_basis + + sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) + mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) + + return sc_loss, mag_loss + + +class MultiResolutionSTFTLoss(torch.nn.Module): + """Multi resolution STFT loss module.""" + + def __init__(self, + fft_sizes=[1024, 2048, 512], + hop_sizes=[120, 240, 50], + win_lengths=[600, 1200, 240], + window="hann_window", + use_mel_loss=False): + """Initialize Multi resolution STFT loss module. + + Args: + fft_sizes (list): List of FFT sizes. + hop_sizes (list): List of hop sizes. + win_lengths (list): List of window lengths. + window (str): Window function type. + + """ + super(MultiResolutionSTFTLoss, self).__init__() + assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) + self.stft_losses = torch.nn.ModuleList() + for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): + self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)] + + def forward(self, x, y): + """Calculate forward propagation. + + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + + Returns: + Tensor: Multi resolution spectral convergence loss value. + Tensor: Multi resolution log STFT magnitude loss value. + + """ + sc_loss = 0.0 + mag_loss = 0.0 + for f in self.stft_losses: + sc_l, mag_l = f(x, y) + sc_loss += sc_l + mag_loss += mag_l + sc_loss /= len(self.stft_losses) + mag_loss /= len(self.stft_losses) + + return sc_loss, mag_loss diff --git a/NeuralSeq/modules/parallel_wavegan/utils/__init__.py b/NeuralSeq/modules/parallel_wavegan/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e8fa95a020706b5412c3959fbf6e5980019c0d5f --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/utils/__init__.py @@ -0,0 +1 @@ +from .utils import * # NOQA diff --git a/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..169bc970f37a72b64bdfff8b12c4d7d3e3546058 Binary files /dev/null and b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64a92a1e5741f67fde6c5fe26bc881931721eabd Binary files /dev/null and b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/__init__.cpython-38.pyc differ diff --git a/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-37.pyc b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b9e12811faed2d42ea2021702c74e1b187e9fc07 Binary files /dev/null and b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-37.pyc differ diff --git a/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-38.pyc b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..66dcd938198b5d4fbb37dc9b6b550926d2650d9d Binary files /dev/null and b/NeuralSeq/modules/parallel_wavegan/utils/__pycache__/utils.cpython-38.pyc differ diff --git a/NeuralSeq/modules/parallel_wavegan/utils/utils.py b/NeuralSeq/modules/parallel_wavegan/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d48a5ed28e8555d4b8cfb15fdee86426bbb9e368 --- /dev/null +++ b/NeuralSeq/modules/parallel_wavegan/utils/utils.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- + +# Copyright 2019 Tomoki Hayashi +# MIT License (https://opensource.org/licenses/MIT) + +"""Utility functions.""" + +import fnmatch +import logging +import os +import sys + +import h5py +import numpy as np + + +def find_files(root_dir, query="*.wav", include_root_dir=True): + """Find files recursively. + + Args: + root_dir (str): Root root_dir to find. + query (str): Query to find. + include_root_dir (bool): If False, root_dir name is not included. + + Returns: + list: List of found filenames. + + """ + files = [] + for root, dirnames, filenames in os.walk(root_dir, followlinks=True): + for filename in fnmatch.filter(filenames, query): + files.append(os.path.join(root, filename)) + if not include_root_dir: + files = [file_.replace(root_dir + "/", "") for file_ in files] + + return files + + +def read_hdf5(hdf5_name, hdf5_path): + """Read hdf5 dataset. + + Args: + hdf5_name (str): Filename of hdf5 file. + hdf5_path (str): Dataset name in hdf5 file. + + Return: + any: Dataset values. + + """ + if not os.path.exists(hdf5_name): + logging.error(f"There is no such a hdf5 file ({hdf5_name}).") + sys.exit(1) + + hdf5_file = h5py.File(hdf5_name, "r") + + if hdf5_path not in hdf5_file: + logging.error(f"There is no such a data in hdf5 file. ({hdf5_path})") + sys.exit(1) + + hdf5_data = hdf5_file[hdf5_path][()] + hdf5_file.close() + + return hdf5_data + + +def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True): + """Write dataset to hdf5. + + Args: + hdf5_name (str): Hdf5 dataset filename. + hdf5_path (str): Dataset path in hdf5. + write_data (ndarray): Data to write. + is_overwrite (bool): Whether to overwrite dataset. + + """ + # convert to numpy array + write_data = np.array(write_data) + + # check folder existence + folder_name, _ = os.path.split(hdf5_name) + if not os.path.exists(folder_name) and len(folder_name) != 0: + os.makedirs(folder_name) + + # check hdf5 existence + if os.path.exists(hdf5_name): + # if already exists, open with r+ mode + hdf5_file = h5py.File(hdf5_name, "r+") + # check dataset existence + if hdf5_path in hdf5_file: + if is_overwrite: + logging.warning("Dataset in hdf5 file already exists. " + "recreate dataset in hdf5.") + hdf5_file.__delitem__(hdf5_path) + else: + logging.error("Dataset in hdf5 file already exists. " + "if you want to overwrite, please set is_overwrite = True.") + hdf5_file.close() + sys.exit(1) + else: + # if not exists, open with w mode + hdf5_file = h5py.File(hdf5_name, "w") + + # write data to hdf5 + hdf5_file.create_dataset(hdf5_path, data=write_data) + hdf5_file.flush() + hdf5_file.close() + + +class HDF5ScpLoader(object): + """Loader class for a fests.scp file of hdf5 file. + + Examples: + key1 /some/path/a.h5:feats + key2 /some/path/b.h5:feats + key3 /some/path/c.h5:feats + key4 /some/path/d.h5:feats + ... + >>> loader = HDF5ScpLoader("hdf5.scp") + >>> array = loader["key1"] + + key1 /some/path/a.h5 + key2 /some/path/b.h5 + key3 /some/path/c.h5 + key4 /some/path/d.h5 + ... + >>> loader = HDF5ScpLoader("hdf5.scp", "feats") + >>> array = loader["key1"] + + """ + + def __init__(self, feats_scp, default_hdf5_path="feats"): + """Initialize HDF5 scp loader. + + Args: + feats_scp (str): Kaldi-style feats.scp file with hdf5 format. + default_hdf5_path (str): Path in hdf5 file. If the scp contain the info, not used. + + """ + self.default_hdf5_path = default_hdf5_path + with open(feats_scp) as f: + lines = [line.replace("\n", "") for line in f.readlines()] + self.data = {} + for line in lines: + key, value = line.split() + self.data[key] = value + + def get_path(self, key): + """Get hdf5 file path for a given key.""" + return self.data[key] + + def __getitem__(self, key): + """Get ndarray for a given key.""" + p = self.data[key] + if ":" in p: + return read_hdf5(*p.split(":")) + else: + return read_hdf5(p, self.default_hdf5_path) + + def __len__(self): + """Return the length of the scp file.""" + return len(self.data) + + def __iter__(self): + """Return the iterator of the scp file.""" + return iter(self.data) + + def keys(self): + """Return the keys of the scp file.""" + return self.data.keys() diff --git a/NeuralSeq/modules/portaspeech/fs.py b/NeuralSeq/modules/portaspeech/fs.py new file mode 100644 index 0000000000000000000000000000000000000000..477cc6501bc700367eb43f17be4a7bf5822b6a1e --- /dev/null +++ b/NeuralSeq/modules/portaspeech/fs.py @@ -0,0 +1,188 @@ +from utils.hparams import hparams +import torch +from torch import nn +import torch.nn.functional as F +from modules.commons.conv import TextConvEncoder, ConvBlocks +from modules.commons.common_layers import Embedding +from modules.fastspeech.tts_modules import LayerNorm, PitchPredictor, LengthRegulator +from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder +from modules.commons.align_ops import clip_mel2token_to_multiple, expand_states +from utils.pitch_utils import denorm_f0, f0_to_coarse + +FS_ENCODERS = { + 'rel_fft': lambda hp, dict: RelTransformerEncoder( + len(dict), hp['hidden_size'], hp['hidden_size'], + hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'], + hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']), +} + +FS_DECODERS = { + 'conv': lambda hp: ConvBlocks(hp['hidden_size'], hp['hidden_size'], hp['dec_dilations'], + hp['dec_kernel_size'], layers_in_block=hp['layers_in_block'], + norm_type=hp['enc_dec_norm'], dropout=hp['dropout'], + post_net_kernel=hp.get('dec_post_net_kernel', 3)), +} + +class DurationPredictor(torch.nn.Module): + def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0): + super(DurationPredictor, self).__init__() + self.offset = offset + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = idim if idx == 0 else n_chans + self.conv += [torch.nn.Sequential( + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )] + self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus()) + + def forward(self, x, x_padding=None): + x = x.transpose(1, -1) # (B, idim, Tmax) + for f in self.conv: + x = f(x) # (B, C, Tmax) + if x_padding is not None: + x = x * (1 - x_padding.float())[:, None, :] + + x = self.linear(x.transpose(1, -1)) # [B, T, C] + x = x * (1 - x_padding.float())[:, :, None] # (B, T, C) + x = x[..., 0] # (B, Tmax) + return x + +class FastSpeech(nn.Module): + def __init__(self, dict_size, out_dims=None): + super().__init__() + self.enc_layers = hparams['enc_layers'] + self.dec_layers = hparams['dec_layers'] + self.hidden_size = hparams['hidden_size'] + if hparams.get("use_bert") is True: + self.ph_encoder = BERTRelTransformerEncoder(dict_size, hparams['hidden_size'], hparams['hidden_size'], + hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'], + hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln']) + else: + self.ph_encoder = FS_ENCODERS[hparams['encoder_type']](hparams, dict_size) + self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) + self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims + self.mel_out = nn.Linear(self.hidden_size, self.out_dims, bias=True) + if hparams['use_spk_id']: + self.spk_id_proj = Embedding(hparams['num_spk'], self.hidden_size) + if hparams['use_spk_embed']: + self.spk_embed_proj = nn.Linear(256, self.hidden_size, bias=True) + predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size + self.dur_predictor = DurationPredictor( + self.hidden_size, + n_chans=predictor_hidden, + n_layers=hparams['dur_predictor_layers'], + dropout_rate=hparams['predictor_dropout'], + kernel_size=hparams['dur_predictor_kernel']) + self.length_regulator = LengthRegulator() + if hparams['use_pitch_embed']: + self.pitch_embed = Embedding(300, self.hidden_size, 0) + self.pitch_predictor = PitchPredictor( + self.hidden_size, n_chans=predictor_hidden, + n_layers=5, dropout_rate=0.1, odim=2, + kernel_size=hparams['predictor_kernel']) + if hparams['dec_inp_add_noise']: + self.z_channels = hparams['z_channels'] + self.dec_inp_noise_proj = nn.Linear(self.hidden_size + self.z_channels, self.hidden_size) + + def forward(self, txt_tokens, mel2ph=None, spk_embed=None, spk_id=None, + f0=None, uv=None, infer=False, **kwargs): + ret = {} + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + style_embed = self.forward_style_embed(spk_embed, spk_id) + + use_bert = hparams.get("use_bert") is True + if use_bert: + encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'], + ret=ret) * src_nonpadding + style_embed + else: + encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed + + # add dur + dur_inp = (encoder_out + style_embed) * src_nonpadding + mel2ph = self.forward_dur(dur_inp, mel2ph, txt_tokens, ret) + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + decoder_inp = expand_states(encoder_out, mel2ph) + + # add pitch embed + if hparams['use_pitch_embed']: + pitch_inp = (decoder_inp + style_embed) * tgt_nonpadding + decoder_inp = decoder_inp + self.forward_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out) + + # decoder input + ret['decoder_inp'] = decoder_inp = (decoder_inp + style_embed) * tgt_nonpadding + if hparams['dec_inp_add_noise']: + B, T, _ = decoder_inp.shape + z = kwargs.get('adv_z', torch.randn([B, T, self.z_channels])).to(decoder_inp.device) + ret['adv_z'] = z + decoder_inp = torch.cat([decoder_inp, z], -1) + decoder_inp = self.dec_inp_noise_proj(decoder_inp) * tgt_nonpadding + ret['mel_out'] = self.forward_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs) + return ret + + def forward_style_embed(self, spk_embed=None, spk_id=None): + # add spk embed + style_embed = 0 + if hparams['use_spk_embed']: + style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :] + if hparams['use_spk_id']: + style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :] + return style_embed + + def forward_dur(self, dur_input, mel2ph, txt_tokens, ret): + """ + + :param dur_input: [B, T_txt, H] + :param mel2ph: [B, T_mel] + :param txt_tokens: [B, T_txt] + :param ret: + :return: + """ + src_padding = txt_tokens == 0 + if hparams['predictor_grad'] != 1: + dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) + dur = self.dur_predictor(dur_input, src_padding) + ret['dur'] = dur + if mel2ph is None: + mel2ph = self.length_regulator(dur, src_padding).detach() + ret['mel2ph'] = mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple']) + return mel2ph + + def forward_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None): + if hparams['pitch_type'] == 'frame': + pitch_pred_inp = decoder_inp + pitch_padding = mel2ph == 0 + else: + pitch_pred_inp = encoder_out + pitch_padding = encoder_out.abs().sum(-1) == 0 + uv = None + if hparams['predictor_grad'] != 1: + pitch_pred_inp = pitch_pred_inp.detach() + \ + hparams['predictor_grad'] * (pitch_pred_inp - pitch_pred_inp.detach()) + ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp) + use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv'] + if f0 is None: + f0 = pitch_pred[:, :, 0] + if use_uv: + uv = pitch_pred[:, :, 1] > 0 + f0_denorm = denorm_f0(f0, uv if use_uv else None, pitch_padding=pitch_padding) + pitch = f0_to_coarse(f0_denorm) # start from 0 [B, T_txt] + ret['f0_denorm'] = f0_denorm + ret['f0_denorm_pred'] = denorm_f0( + pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None, + pitch_padding=pitch_padding) + if hparams['pitch_type'] == 'ph': + pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph) + ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph) + ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph) + pitch_embed = self.pitch_embed(pitch) + return pitch_embed + + def forward_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs): + x = decoder_inp # [B, T, H] + x = self.decoder(x) + x = self.mel_out(x) + return x * tgt_nonpadding diff --git a/NeuralSeq/modules/portaspeech/fvae.py b/NeuralSeq/modules/portaspeech/fvae.py new file mode 100644 index 0000000000000000000000000000000000000000..9659a4f2abb9fd2ef887a432b7ad55b0129a1c6f --- /dev/null +++ b/NeuralSeq/modules/portaspeech/fvae.py @@ -0,0 +1,202 @@ +import numpy as np +import torch +import torch.distributions as dist +from torch import nn + +from modules.commons.conv import ConditionalConvBlocks +from modules.commons.normalizing_flow.res_flow import ResFlow +from modules.commons.wavenet import WN + + +class FVAEEncoder(nn.Module): + def __init__(self, c_in, hidden_size, c_latent, kernel_size, + n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'): + super().__init__() + self.strides = strides + self.hidden_size = hidden_size + if np.prod(strides) == 1: + self.pre_net = nn.Conv1d(c_in, hidden_size, kernel_size=1) + else: + self.pre_net = nn.Sequential(*[ + nn.Conv1d(c_in, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2) + if i == 0 else + nn.Conv1d(hidden_size, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2) + for i, s in enumerate(strides) + ]) + if nn_type == 'wn': + self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout) + elif nn_type == 'conv': + self.nn = ConditionalConvBlocks( + hidden_size, c_cond, hidden_size, None, kernel_size, + layers_in_block=2, is_BTC=False, num_layers=n_layers) + + self.out_proj = nn.Conv1d(hidden_size, c_latent * 2, 1) + self.latent_channels = c_latent + + def forward(self, x, nonpadding, cond): + x = self.pre_net(x) + nonpadding = nonpadding[:, :, ::np.prod(self.strides)][:, :, :x.shape[-1]] + x = x * nonpadding + x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding + x = self.out_proj(x) + m, logs = torch.split(x, self.latent_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) + return z, m, logs, nonpadding + + +class FVAEDecoder(nn.Module): + def __init__(self, c_latent, hidden_size, out_channels, kernel_size, + n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'): + super().__init__() + self.strides = strides + self.hidden_size = hidden_size + self.pre_net = nn.Sequential(*[ + nn.ConvTranspose1d(c_latent, hidden_size, kernel_size=s, stride=s) + if i == 0 else + nn.ConvTranspose1d(hidden_size, hidden_size, kernel_size=s, stride=s) + for i, s in enumerate(strides) + ]) + if nn_type == 'wn': + self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout) + elif nn_type == 'conv': + self.nn = ConditionalConvBlocks( + hidden_size, c_cond, hidden_size, [1] * n_layers, kernel_size, + layers_in_block=2, is_BTC=False) + self.out_proj = nn.Conv1d(hidden_size, out_channels, 1) + + def forward(self, x, nonpadding, cond): + x = self.pre_net(x) + x = x * nonpadding + x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding + x = self.out_proj(x) + return x + + +class FVAE(nn.Module): + def __init__(self, + c_in_out, hidden_size, c_latent, + kernel_size, enc_n_layers, dec_n_layers, c_cond, strides, + use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None, + encoder_type='wn', decoder_type='wn'): + super(FVAE, self).__init__() + self.strides = strides + self.hidden_size = hidden_size + self.latent_size = c_latent + self.use_prior_flow = use_prior_flow + if np.prod(strides) == 1: + self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1) + else: + self.g_pre_net = nn.Sequential(*[ + nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2) + for i, s in enumerate(strides) + ]) + self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size, + enc_n_layers, c_cond, strides=strides, nn_type=encoder_type) + if use_prior_flow: + self.prior_flow = ResFlow( + c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond) + self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size, + dec_n_layers, c_cond, strides=strides, nn_type=decoder_type) + self.prior_dist = dist.Normal(0, 1) + + def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, **kwargs): + """ + + :param x: [B, C_in_out, T] + :param nonpadding: [B, 1, T] + :param cond: [B, C_g, T] + :return: + """ + if nonpadding is None: + nonpadding = 1 + cond_sqz = self.g_pre_net(cond) + if not infer: + z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz) + q_dist = dist.Normal(m_q, logs_q.exp()) + if self.use_prior_flow: + logqx = q_dist.log_prob(z_q) + z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz) + logpx = self.prior_dist.log_prob(z_p) + loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1] + else: + loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist) + loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1] + z_p = None + return z_q, loss_kl, z_p, m_q, logs_q + else: + latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]] + z_p = torch.randn(latent_shape).to(cond.device) * noise_scale + if self.use_prior_flow: + z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True) + return z_p + + +class SyntaFVAE(nn.Module): + def __init__(self, + c_in_out, hidden_size, c_latent, + kernel_size, enc_n_layers, dec_n_layers, c_cond, strides, + use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None, + encoder_type='wn', decoder_type='wn'): + super(SyntaFVAE, self).__init__() + self.strides = strides + self.hidden_size = hidden_size + self.latent_size = c_latent + self.use_prior_flow = use_prior_flow + if np.prod(strides) == 1: + self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1) + else: + self.g_pre_net = nn.Sequential(*[ + nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2) + for i, s in enumerate(strides) + ]) + self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size, + enc_n_layers, c_cond, strides=strides, nn_type=encoder_type) + if use_prior_flow: + self.prior_flow = ResFlow( + c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond) + self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size, + dec_n_layers, c_cond, strides=strides, nn_type=decoder_type) + self.prior_dist = dist.Normal(0, 1) + self.graph_encoder = GraphAuxEnc(in_dim=hidden_size, hid_dim=hidden_size,out_dim=hidden_size) + + def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, + mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None): + """ + + :param x: target mel, [B, C_in_out, T] + :param nonpadding: [B, 1, T] + :param cond: phoneme encoding, [B, C_g, T] + :return: + """ + word_len = ph2word.max(dim=1)[0] + ph_encoding_for_graph = cond.detach() + 0.1 * (cond - cond.detach()) # only 0.1x grad can pass through + _, ph_out_word_encoding_for_graph = GraphAuxEnc.ph_encoding_to_word_encoding(ph_encoding_for_graph.transpose(1,2), mel2word, word_len) + t_m = mel2word.shape[-1] + g_graph = self.graph_encoder.word_forward(graph_lst=graph_lst, word_encoding=ph_out_word_encoding_for_graph, etypes_lst=etypes_lst) + g_graph = g_graph.transpose(1,2) + g_graph = GraphAuxEnc._postprocess_word2ph(g_graph,mel2word,t_m) + g_graph = g_graph.transpose(1,2) + cond = cond + g_graph * 1. + + if nonpadding is None: + nonpadding = 1 + cond_sqz = self.g_pre_net(cond) + if not infer: + z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz) + q_dist = dist.Normal(m_q, logs_q.exp()) + if self.use_prior_flow: + logqx = q_dist.log_prob(z_q) + z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz) + logpx = self.prior_dist.log_prob(z_p) + loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1] + else: + loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist) + loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1] + z_p = None + return z_q, loss_kl, z_p, m_q, logs_q + else: + latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]] + z_p = torch.randn(latent_shape).to(cond.device) * noise_scale + if self.use_prior_flow: + z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True) + return z_p \ No newline at end of file diff --git a/NeuralSeq/modules/portaspeech/portaspeech.py b/NeuralSeq/modules/portaspeech/portaspeech.py new file mode 100644 index 0000000000000000000000000000000000000000..e313add6037ae87f41361633fcf6d92f39e8fd92 --- /dev/null +++ b/NeuralSeq/modules/portaspeech/portaspeech.py @@ -0,0 +1,230 @@ +import math +import torch +from torch import nn +from torch.nn import Linear + +from modules.commons.conv import ConvBlocks, ConditionalConvBlocks +from modules.commons.common_layers import Embedding +from modules.commons.rel_transformer import RelTransformerEncoder +from modules.commons.transformer import MultiheadAttention, FFTBlocks +from modules.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word +from modules.portaspeech.fs import FS_DECODERS, FastSpeech +from modules.portaspeech.fvae import FVAE +from utils.tts_utils import group_hidden_by_segs +from utils.hparams import hparams + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + """ + + :param x: [B, T] + :return: [B, T, H] + """ + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, :, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class PortaSpeech(FastSpeech): + def __init__(self, ph_dictionary, word_dictionary, out_dims=None): + super().__init__(ph_dictionary, out_dims) + # build linguistic encoder + if hparams['use_word_encoder']: + # default False, use independent word embedding instead of phoneme encoding to represent word + self.word_encoder = RelTransformerEncoder( + len(word_dictionary), self.hidden_size, self.hidden_size, self.hidden_size, 2, + hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) + if hparams['dur_level'] == 'word': + if hparams['word_encoder_type'] == 'rel_fft': + self.ph2word_encoder = RelTransformerEncoder( + 0, self.hidden_size, self.hidden_size, self.hidden_size, 2, + hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) + if hparams['word_encoder_type'] == 'fft': + self.ph2word_encoder = FFTBlocks( + self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads']) + self.sin_pos = SinusoidalPosEmb(self.hidden_size) + self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False) + self.attn.enable_torch_version = False + if hparams['text_encoder_postnet']: + self.text_encoder_postnet = ConvBlocks( + self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2) + else: + self.sin_pos = SinusoidalPosEmb(self.hidden_size) + # build VAE decoder + if hparams['use_fvae']: + del self.decoder + del self.mel_out + self.fvae = FVAE( + c_in_out=self.out_dims, + hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'], + kernel_size=hparams['fvae_kernel_size'], + enc_n_layers=hparams['fvae_enc_n_layers'], + dec_n_layers=hparams['fvae_dec_n_layers'], + c_cond=self.hidden_size, + use_prior_flow=hparams['use_prior_flow'], + flow_hidden=hparams['prior_flow_hidden'], + flow_kernel_size=hparams['prior_flow_kernel_size'], + flow_n_steps=hparams['prior_flow_n_blocks'], + strides=[hparams['fvae_strides']], + encoder_type=hparams['fvae_encoder_type'], + decoder_type=hparams['fvae_decoder_type'], + ) + else: + self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) + self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True) + if hparams['use_pitch_embed']: + self.pitch_embed = Embedding(300, self.hidden_size, 0) + if hparams['add_word_pos']: + self.word_pos_proj = Linear(self.hidden_size, self.hidden_size) + + def build_embedding(self, dictionary, embed_dim): + num_embeddings = len(dictionary) + emb = Embedding(num_embeddings, embed_dim, self.padding_idx) + return emb + + def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None, + spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None, + global_step=None, *args, **kwargs): + ret = {} + style_embed = self.forward_style_embed(spk_embed, spk_id) + x, tgt_nonpadding = self.run_text_encoder( + txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs) + x = x * tgt_nonpadding + ret['nonpadding'] = tgt_nonpadding + if hparams['use_pitch_embed']: + x = x + self.pitch_embed(pitch) + ret['decoder_inp'] = x + ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step) + return ret + + def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs): + word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1 # [B, T_mel, T_word] + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + use_bert = hparams.get("use_bert") is True + if use_bert: + ph_encoder_out = self.ph_encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word, + graph_lst=kwargs['graph_lst'], etypes_lst=kwargs['etypes_lst'], + cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed + else: + ph_encoder_out = self.ph_encoder(txt_tokens) * src_nonpadding + style_embed + if hparams['use_word_encoder']: + word_encoder_out = self.word_encoder(word_tokens) + style_embed + ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word) + if hparams['dur_level'] == 'word': + word_encoder_out = 0 + h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0] + word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word) + if hparams['use_word_encoder']: + word_encoder_out = word_encoder_out + self.word_encoder(word_tokens) + mel2word = self.forward_dur(ph_encoder_out, mel2word, ret, ph2word=ph2word, word_len=word_len) + mel2word = clip_mel2token_to_multiple(mel2word, hparams['frames_multiple']) + tgt_nonpadding = (mel2word > 0).float()[:, :, None] + enc_pos = self.get_pos_embed(word2word, ph2word) # [B, T_ph, H] + dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] + dec_word_mask = build_word_mask(mel2word, ph2word) # [B, T_mel, T_ph] + x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask) + if hparams['add_word_pos']: + x = x + self.word_pos_proj(dec_pos) + ret['attn'] = weight + else: + mel2ph = self.forward_dur(ph_encoder_out, mel2ph, ret) + mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple']) + mel2word = mel2ph_to_mel2word(mel2ph, ph2word) + x = expand_states(ph_encoder_out, mel2ph) + if hparams['add_word_pos']: + dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] + x = x + self.word_pos_proj(dec_pos) + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + if hparams['use_word_encoder']: + x = x + expand_states(word_encoder_out, mel2word) + return x, tgt_nonpadding + + def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask): + ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1)) + word_enc_out_expend = expand_states(word_encoder_out, mel2word) + word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1) + if hparams['text_encoder_postnet']: + word_enc_out_expend = self.dec_res_proj(word_enc_out_expend) + word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend) + dec_q = x_res = word_enc_out_expend + else: + dec_q = self.dec_query_proj(word_enc_out_expend) + x_res = self.dec_res_proj(word_enc_out_expend) + ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1) + x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9) + x = x.transpose(0, 1) + x = x + x_res + return x, weight + + def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0): + if not hparams['use_fvae']: + x = self.decoder(x) + x = self.mel_out(x) + ret['kl'] = 0 + return x * tgt_nonpadding + else: + decoder_inp = x + x = x.transpose(1, 2) # [B, H, T] + tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2) # [B, H, T] + if infer: + z = self.fvae(cond=x, infer=True) + else: + tgt_mels = tgt_mels.transpose(1, 2) # [B, 80, T] + z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae( + tgt_mels, tgt_nonpadding_BHT, cond=x) + if global_step < hparams['posterior_start_steps']: + z = torch.randn_like(z) + x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2) + ret['pre_mel_out'] = x_recon + return x_recon + + def forward_dur(self, dur_input, mel2word, ret, **kwargs): + """ + + :param dur_input: [B, T_txt, H] + :param mel2ph: [B, T_mel] + :param txt_tokens: [B, T_txt] + :param ret: + :return: + """ + src_padding = dur_input.data.abs().sum(-1) == 0 + dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) + dur = self.dur_predictor(dur_input, src_padding) + if hparams['dur_level'] == 'word': + word_len = kwargs['word_len'] + ph2word = kwargs['ph2word'] + B, T_ph = ph2word.shape + dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur) + dur = dur[:, 1:] + ret['dur'] = dur + if mel2word is None: + mel2word = self.length_regulator(dur).detach() + return mel2word + + def get_pos_embed(self, word2word, x2word): + x_pos = build_word_mask(word2word, x2word).float() # [B, T_word, T_ph] + x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1) + x_pos = self.sin_pos(x_pos.float()) # [B, T_ph, H] + return x_pos + + def store_inverse_all(self): + def remove_weight_norm(m): + try: + if hasattr(m, 'store_inverse'): + m.store_inverse() + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) diff --git a/NeuralSeq/modules/portaspeech/portaspeech_flow.py b/NeuralSeq/modules/portaspeech/portaspeech_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7c563365c142b43679848fead9a65ae2bb5746 --- /dev/null +++ b/NeuralSeq/modules/portaspeech/portaspeech_flow.py @@ -0,0 +1,75 @@ +import torch +import torch.distributions as dist +from torch import nn +from modules.commons.normalizing_flow.glow_modules import Glow +from modules.portaspeech.portaspeech import PortaSpeech +from utils.hparams import hparams + +class PortaSpeechFlow(PortaSpeech): + def __init__(self, ph_dict_size, word_dict_size, out_dims=None): + super().__init__(ph_dict_size, word_dict_size, out_dims) + cond_hs = 80 + if hparams.get('use_txt_cond', True): + cond_hs = cond_hs + hparams['hidden_size'] + if hparams.get('use_latent_cond', False): + cond_hs = cond_hs + hparams['latent_size'] + if hparams['use_cond_proj']: + self.g_proj = nn.Conv1d(cond_hs, 160, 5, padding=2) + cond_hs = 160 + self.post_flow = Glow( + 80, hparams['post_glow_hidden'], hparams['post_glow_kernel_size'], 1, + hparams['post_glow_n_blocks'], hparams['post_glow_n_block_layers'], + n_split=4, n_sqz=2, + gin_channels=cond_hs, + share_cond_layers=hparams['post_share_cond_layers'], + share_wn_layers=hparams['share_wn_layers'], + sigmoid_scale=hparams['sigmoid_scale'] + ) + self.prior_dist = dist.Normal(0, 1) + + def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None, + spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None, + forward_post_glow=True, two_stage=True, global_step=None, **kwargs): + is_training = self.training + train_fvae = not (forward_post_glow and two_stage) + if not train_fvae: + self.eval() + with torch.set_grad_enabled(mode=train_fvae): + ret = super(PortaSpeechFlow, self).forward( + txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, + spk_embed, spk_id, pitch, infer, tgt_mels, global_step, **kwargs) + if (forward_post_glow or not two_stage) and hparams['use_post_flow']: + self.run_post_glow(tgt_mels, infer, is_training, ret) + return ret + + def run_post_glow(self, tgt_mels, infer, is_training, ret): + x_recon = ret['mel_out'].transpose(1, 2) + g = x_recon + B, _, T = g.shape + if hparams.get('use_txt_cond', True): + g = torch.cat([g, ret['decoder_inp'].transpose(1, 2)], 1) + if hparams.get('use_latent_cond', False): + g_z = ret['z_p'][:, :, :, None].repeat(1, 1, 1, 4).reshape(B, -1, T) + g = torch.cat([g, g_z], 1) + if hparams['use_cond_proj']: + g = self.g_proj(g) + prior_dist = self.prior_dist + if not infer: + if is_training: + self.post_flow.train() + nonpadding = ret['nonpadding'].transpose(1, 2) + y_lengths = nonpadding.sum(-1) + if hparams['detach_postflow_input']: + g = g.detach() + tgt_mels = tgt_mels.transpose(1, 2) + z_postflow, ldj = self.post_flow(tgt_mels, nonpadding, g=g) + ldj = ldj / y_lengths / 80 + ret['z_pf'], ret['ldj_pf'] = z_postflow, ldj + ret['postflow'] = -prior_dist.log_prob(z_postflow).mean() - ldj.mean() + if torch.isnan(ret['postflow']): + ret['postflow'] = None + else: + nonpadding = torch.ones_like(x_recon[:, :1, :]) + z_post = torch.randn(x_recon.shape).to(g.device) * hparams['noise_scale'] + x_recon, _ = self.post_flow(z_post, nonpadding, g, reverse=True) + ret['mel_out'] = x_recon.transpose(1, 2) diff --git a/NeuralSeq/modules/syntaspeech/multi_window_disc.py b/NeuralSeq/modules/syntaspeech/multi_window_disc.py new file mode 100644 index 0000000000000000000000000000000000000000..a8166ac5b514e501043b9fed13aab01421a6c10e --- /dev/null +++ b/NeuralSeq/modules/syntaspeech/multi_window_disc.py @@ -0,0 +1,136 @@ +import numpy as np +import torch +import torch.nn as nn + + +class SingleWindowDisc(nn.Module): + def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128): + super().__init__() + padding = (kernel[0] // 2, kernel[1] // 2) + self.model = nn.ModuleList([ + nn.Sequential(*[ + nn.Conv2d(c_in, hidden_size, kernel, (2, 2), padding), + nn.LeakyReLU(0.2, inplace=True), + nn.Dropout2d(0.25), + nn.BatchNorm2d(hidden_size, 0.8) + ]), + nn.Sequential(*[ + nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding), + nn.LeakyReLU(0.2, inplace=True), + nn.Dropout2d(0.25), + nn.BatchNorm2d(hidden_size, 0.8) + ]), + nn.Sequential(*[ + nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding), + nn.LeakyReLU(0.2, inplace=True), + nn.Dropout2d(0.25), + ]), + ]) + ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3) + self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1) + + def forward(self, x): + """ + :param x: [B, C, T, n_bins] + :return: validity: [B, 1], h: List of hiddens + """ + h = [] + for l in self.model: + x = l(x) + h.append(x) + x = x.view(x.shape[0], -1) + validity = self.adv_layer(x) # [B, 1] + return validity, h + + +class MultiWindowDiscriminator(nn.Module): + def __init__(self, time_lengths, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128): + super(MultiWindowDiscriminator, self).__init__() + self.win_lengths = time_lengths + self.discriminators = nn.ModuleList() + + for time_length in time_lengths: + self.discriminators += [SingleWindowDisc(time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size)] + + def forward(self, x, x_len, start_frames_wins=None): + ''' + Args: + x (tensor): input mel, (B, c_in, T, n_bins). + x_length (tensor): len of per mel. (B,). + + Returns: + tensor : (B). + ''' + validity = [] + if start_frames_wins is None: + start_frames_wins = [None] * len(self.discriminators) + h = [] + for i, start_frames in zip(range(len(self.discriminators)), start_frames_wins): + x_clip, start_frames = self.clip(x, x_len, self.win_lengths[i], start_frames) # (B, win_length, C) + start_frames_wins[i] = start_frames + if x_clip is None: + continue + x_clip, h_ = self.discriminators[i](x_clip) + h += h_ + validity.append(x_clip) + if len(validity) != len(self.discriminators): + return None, start_frames_wins, h + validity = sum(validity) # [B] + return validity, start_frames_wins, h + + def clip(self, x, x_len, win_length, start_frames=None): + '''Ramdom clip x to win_length. + Args: + x (tensor) : (B, c_in, T, n_bins). + cond (tensor) : (B, T, H). + x_len (tensor) : (B,). + win_length (int): target clip length + + Returns: + (tensor) : (B, c_in, win_length, n_bins). + + ''' + T_start = 0 + T_end = x_len.max() - win_length + if T_end < 0: + return None, None, start_frames + T_end = T_end.item() + if start_frames is None: + start_frame = np.random.randint(low=T_start, high=T_end + 1) + start_frames = [start_frame] * x.size(0) + else: + start_frame = start_frames[0] + x_batch = x[:, :, start_frame: start_frame + win_length] + return x_batch, start_frames + + +class Discriminator(nn.Module): + def __init__(self, time_lengths=[32, 64, 128], freq_length=80, kernel=(3, 3), c_in=1, + hidden_size=128): + super(Discriminator, self).__init__() + self.time_lengths = time_lengths + self.discriminator = MultiWindowDiscriminator( + freq_length=freq_length, + time_lengths=time_lengths, + kernel=kernel, + c_in=c_in, hidden_size=hidden_size + ) + + + def forward(self, x, start_frames_wins=None): + """ + + :param x: [B, T, 80] + :param return_y_only: + :return: + """ + if len(x.shape) == 3: + x = x[:, None, :, :] # [B,1,T,80] + x_len = x.sum([1, -1]).ne(0).int().sum([-1]) + ret = {'y_c': None, 'y': None} + ret['y'], start_frames_wins, ret['h'] = self.discriminator( + x, x_len, start_frames_wins=start_frames_wins) + + ret['start_frames_wins'] = start_frames_wins + return ret + diff --git a/NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py b/NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py new file mode 100644 index 0000000000000000000000000000000000000000..6334b6a0435390abb05f04638afc6e2a57a8f000 --- /dev/null +++ b/NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py @@ -0,0 +1,294 @@ +from copy import deepcopy +import torch +import dgl +import stanza +import networkx as nx + +class Sentence2GraphParser: + def __init__(self, language='zh', use_gpu=False, download=False): + self.language = language + if download: + self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu) + else: + self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu, download_method=None) + + def parse(self, clean_sentence=None, words=None, ph_words=None): + if self.language == 'zh': + assert words is not None and ph_words is not None + ret = self._parse_zh(words, ph_words) + elif self.language == 'en': + assert clean_sentence is not None + ret = self._parse_en(clean_sentence) + else: + raise NotImplementedError + return ret + + def _parse_zh(self, words, ph_words, enable_backward_edge=True, enable_recur_edge=True, + enable_inter_sentence_edge=True, sequential_edge=False): + """ + words: , each character in chinese is one item + ph_words: , each character in chinese is one item, represented by the phoneme + Example: + text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.' + words = ['', '宝', '马', '配', '挂', '跛', '骡', '鞍', ',' + , '貂', '蝉', '怨', '枕', '董', '翁', '榻', ''] + ph_words = ['', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#', + 'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|', + 'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', ''] + """ + words, ph_words = words[1:-1], ph_words[1:-1] # delete and + for i, p_w in enumerate(ph_words): + if p_w == ',': + # change english ',' into chinese + # we found it necessary in stanza's dependency parsing + words[i], ph_words[i] = '，', '，' + tmp_words = deepcopy(words) + num_added_space = 0 + for i, p_w in enumerate(ph_words): + if p_w.endswith("#"): + # add a blank after the p_w with '#', to separate words + tmp_words.insert(num_added_space + i + 1, " ") + num_added_space += 1 + if p_w in ['，', ',']: + # add one blank before and after ', ', respectively + tmp_words.insert(num_added_space + i + 1, " ") # insert behind ',' first + tmp_words.insert(num_added_space + i, " ") # insert before + num_added_space += 2 + clean_text = ''.join(tmp_words).strip() + parser_out = self.stanza_parser(clean_text) + + idx_to_word = {i + 1: w for i, w in enumerate(words)} + + vocab_nodes = {} + vocab_idx_offset = 0 + for sentence in parser_out.sentences: + num_nodes_in_current_sentence = 0 + for vocab_node in sentence.words: + num_nodes_in_current_sentence += 1 + vocab_idx = vocab_node.id + vocab_idx_offset + vocab_text = vocab_node.text.replace(" ", "") # delete blank in vocab + vocab_nodes[vocab_idx] = vocab_text + vocab_idx_offset += num_nodes_in_current_sentence + + # start vocab-to-word alignment + vocab_to_word = {} + current_word_idx = 1 + for vocab_i in vocab_nodes.keys(): + vocab_to_word[vocab_i] = [] + for w_in_vocab_i in vocab_nodes[vocab_i]: + if w_in_vocab_i != idx_to_word[current_word_idx]: + raise ValueError("Word Mismatch!") + vocab_to_word[vocab_i].append(current_word_idx) # add a path (vocab_node_idx, word_global_idx) + current_word_idx += 1 + + # then we compute the vocab-level edges + if len(parser_out.sentences) > 5: + print("Detect more than 5 input sentence! pls check whether the sentence is too long!") + vocab_level_source_id, vocab_level_dest_id = [], [] + vocab_level_edge_types = [] + sentences_heads = [] + vocab_id_offset = 0 + # get forward edges + for s in parser_out.sentences: + for w in s.words: + w_idx = w.id + vocab_id_offset # it starts from 1, just same as binarizer + w_dest_idx = w.head + vocab_id_offset + if w.head == 0: + sentences_heads.append(w_idx) + continue + vocab_level_source_id.append(w_idx) + vocab_level_dest_id.append(w_dest_idx) + vocab_id_offset += len(s.words) + vocab_level_edge_types += [0] * len(vocab_level_source_id) + num_vocab = vocab_id_offset + + # optional: get backward edges + if enable_backward_edge: + back_source, back_dest = deepcopy(vocab_level_dest_id), deepcopy(vocab_level_source_id) + vocab_level_source_id += back_source + vocab_level_dest_id += back_dest + vocab_level_edge_types += [1] * len(back_source) + + # optional: get inter-sentence edges if num_sentences > 1 + inter_sentence_source, inter_sentence_dest = [], [] + if enable_inter_sentence_edge and len(sentences_heads) > 1: + def get_full_graph_edges(nodes): + tmp_edges = [] + for i, node_i in enumerate(nodes): + for j, node_j in enumerate(nodes): + if i == j: + continue + tmp_edges.append((node_i, node_j)) + return tmp_edges + + tmp_edges = get_full_graph_edges(sentences_heads) + for (source, dest) in tmp_edges: + inter_sentence_source.append(source) + inter_sentence_dest.append(dest) + vocab_level_source_id += inter_sentence_source + vocab_level_dest_id += inter_sentence_dest + vocab_level_edge_types += [3] * len(inter_sentence_source) + + if sequential_edge: + seq_source, seq_dest = list(range(1, num_vocab)) + list(range(num_vocab, 0, -1)), \ + list(range(2, num_vocab + 1)) + list(range(num_vocab - 1, -1, -1)) + vocab_level_source_id += seq_source + vocab_level_dest_id += seq_dest + vocab_level_edge_types += [4] * (num_vocab - 1) + [5] * (num_vocab - 1) + + # Then, we use the vocab-level edges and the vocab-to-word path, to construct the word-level graph + num_word = len(words) + source_id, dest_id, edge_types = [], [], [] + for (vocab_start, vocab_end, vocab_edge_type) in zip(vocab_level_source_id, vocab_level_dest_id, + vocab_level_edge_types): + # connect the first word in the vocab + word_start = min(vocab_to_word[vocab_start]) + word_end = min(vocab_to_word[vocab_end]) + source_id.append(word_start) + dest_id.append(word_end) + edge_types.append(vocab_edge_type) + + # sequential connection in words + for word_indices_in_v in vocab_to_word.values(): + for i, word_idx in enumerate(word_indices_in_v): + if i + 1 < len(word_indices_in_v): + source_id.append(word_idx) + dest_id.append(word_idx + 1) + edge_types.append(4) + if i - 1 >= 0: + source_id.append(word_idx) + dest_id.append(word_idx - 1) + edge_types.append(5) + + # optional: get recurrent edges + if enable_recur_edge: + recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1)) + source_id += recur_source + dest_id += recur_dest + edge_types += [2] * len(recur_source) + + # add and + source_id += [0, num_word + 1, 1, num_word] + dest_id += [1, num_word, 0, num_word + 1] + edge_types += [4, 4, 5, 5] # 4 represents sequentially forward, 5 is sequential backward + + edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id)) + dgl_graph = dgl.graph(edges) + assert dgl_graph.num_edges() == len(edge_types) + return dgl_graph, torch.LongTensor(edge_types) + + def _parse_en(self, clean_sentence, enable_backward_edge=True, enable_recur_edge=True, + enable_inter_sentence_edge=True, sequential_edge=False, consider_bos_for_index=True): + """ + clean_sentence: , each word or punctuation should be separated by one blank. + """ + edge_types = [] # required for gated graph neural network + clean_sentence = clean_sentence.strip() + if clean_sentence.endswith((" .", " ,", " ;", " :", " ?", " !")): + clean_sentence = clean_sentence[:-2] + if clean_sentence.startswith(". "): + clean_sentence = clean_sentence[2:] + parser_out = self.stanza_parser(clean_sentence) + if len(parser_out.sentences) > 5: + print("Detect more than 5 input sentence! pls check whether the sentence is too long!") + print(clean_sentence) + source_id, dest_id = [], [] + sentences_heads = [] + word_id_offset = 0 + # get forward edges + for s in parser_out.sentences: + for w in s.words: + w_idx = w.id + word_id_offset # it starts from 1, just same as binarizer + w_dest_idx = w.head + word_id_offset + if w.head == 0: + sentences_heads.append(w_idx) + continue + source_id.append(w_idx) + dest_id.append(w_dest_idx) + word_id_offset += len(s.words) + num_word = word_id_offset + edge_types += [0] * len(source_id) + + # optional: get backward edges + if enable_backward_edge: + back_source, back_dest = deepcopy(dest_id), deepcopy(source_id) + source_id += back_source + dest_id += back_dest + edge_types += [1] * len(back_source) + + # optional: get recurrent edges + if enable_recur_edge: + recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1)) + source_id += recur_source + dest_id += recur_dest + edge_types += [2] * len(recur_source) + + # optional: get inter-sentence edges if num_sentences > 1 + inter_sentence_source, inter_sentence_dest = [], [] + if enable_inter_sentence_edge and len(sentences_heads) > 1: + def get_full_graph_edges(nodes): + tmp_edges = [] + for i, node_i in enumerate(nodes): + for j, node_j in enumerate(nodes): + if i == j: + continue + tmp_edges.append((node_i, node_j)) + return tmp_edges + + tmp_edges = get_full_graph_edges(sentences_heads) + for (source, dest) in tmp_edges: + inter_sentence_source.append(source) + inter_sentence_dest.append(dest) + source_id += inter_sentence_source + dest_id += inter_sentence_dest + edge_types += [3] * len(inter_sentence_source) + + # add and + source_id += [0, num_word + 1, 1, num_word] + dest_id += [1, num_word, 0, num_word + 1] + edge_types += [4, 4, 5, 5] # 4 represents sequentially forward, 5 is sequential backward + + # optional: sequential edge + if sequential_edge: + seq_source, seq_dest = list(range(1, num_word)) + list(range(num_word, 0, -1)), \ + list(range(2, num_word + 1)) + list(range(num_word - 1, -1, -1)) + source_id += seq_source + dest_id += seq_dest + edge_types += [4] * (num_word - 1) + [5] * (num_word - 1) + if consider_bos_for_index: + edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id)) + else: + edges = (torch.LongTensor(source_id) - 1, torch.LongTensor(dest_id) - 1) + dgl_graph = dgl.graph(edges) + assert dgl_graph.num_edges() == len(edge_types) + return dgl_graph, torch.LongTensor(edge_types) + + +def plot_dgl_sentence_graph(dgl_graph, labels): + """ + labels = {idx: word for idx,word in enumerate(sentence.split(" ")) } + """ + import matplotlib.pyplot as plt + nx_graph = dgl_graph.to_networkx() + pos = nx.random_layout(nx_graph) + nx.draw(nx_graph, pos, with_labels=False) + nx.draw_networkx_labels(nx_graph, pos, labels) + plt.show() + +if __name__ == '__main__': + + # Unit Test for Chinese Graph Builder + parser = Sentence2GraphParser("zh") + text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.' + words = ['', '宝', '马', '配', '挂', '跛', '骡', '鞍', ',', '貂', '蝉', '怨', '枕', '董', '翁', '榻', ''] + ph_words = ['', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#', 'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|', + 'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', ''] + graph1, etypes1 = parser.parse(text1, words, ph_words) + plot_dgl_sentence_graph(graph1, {i: w for i, w in enumerate(ph_words)}) + + # Unit Test for English Graph Builder + parser = Sentence2GraphParser("en") + text2 = "I love you . You love me . Mixue ice-scream and tea ." + graph2, etypes2 = parser.parse(text2) + plot_dgl_sentence_graph(graph2, {i: w for i, w in enumerate((" " + text2 + " ").split(" "))}) + diff --git a/NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py b/NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d703ae2c986b231a92ce468500ceb927d2a6ce7c --- /dev/null +++ b/NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py @@ -0,0 +1,193 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import dgl +from dgl.nn.pytorch import GatedGraphConv + +def sequence_mask(lengths, maxlen, dtype=torch.bool): + if maxlen is None: + maxlen = lengths.max() + mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t() + mask.type(dtype) + return mask + + +def group_hidden_by_segs(h, seg_ids, max_len): + """ + :param h: [B, T, H] + :param seg_ids: [B, T] + :return: h_ph: [B, T_ph, H] + """ + B, T, H = h.shape + h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h) + all_ones = h.new_ones(h.shape[:2]) + cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous() + h_gby_segs = h_gby_segs[:, 1:] + cnt_gby_segs = cnt_gby_segs[:, 1:] + h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1) + # assert h_gby_segs.shape[-1] == 192 + return h_gby_segs + +class GraphAuxEnc(nn.Module): + def __init__(self, in_dim, hid_dim, out_dim, n_iterations=5, n_edge_types=6): + super(GraphAuxEnc, self).__init__() + self.in_dim = in_dim + self.hid_dim = hid_dim + self.out_dim = out_dim + self.skip_connect = True + self.dropout_after_gae = False + + self.ggc_1 = GatedGraphConv(in_feats=in_dim, out_feats=hid_dim + , n_steps=n_iterations, n_etypes=n_edge_types) + self.ggc_2 = GatedGraphConv(in_feats=hid_dim, out_feats=out_dim + , n_steps=n_iterations, n_etypes=n_edge_types) + self.dropout = nn.Dropout(p=0.5) + + @staticmethod + def ph_encoding_to_word_encoding(ph_encoding, ph2word, word_len): + """ + ph_encoding: [batch, t_p, hid] + ph2word: tensor [batch, t_w] + word_len: tensor [batch] + """ + word_encoding_for_graph, batch_word_encoding, has_word_row_idx = GraphAuxEnc._process_ph_to_word_encoding( + ph_encoding, + ph2word, + word_len) + # [batch, t_w, hid] + return batch_word_encoding, word_encoding_for_graph + + def pad_word_encoding_to_phoneme(self, word_encoding, ph2word, t_p): + return self._postprocess_word2ph(word_encoding, ph2word, t_p) + + @staticmethod + def _process_ph_to_word_encoding(ph_encoding, ph2word, word_len=None): + """ + ph_encoding: [batch, t_p, hid] + ph2word: tensor [batch, t_w] + word_len: tensor [batch] + """ + word_len = word_len.reshape([-1,]) + max_len = max(word_len) + num_nodes = sum(word_len) + + batch_word_encoding = group_hidden_by_segs(ph_encoding, ph2word, max_len) + bs, t_p, hid = batch_word_encoding.shape + has_word_mask = sequence_mask(word_len, max_len) # [batch, t_p, 1] + word_encoding = batch_word_encoding.reshape([bs * t_p, hid]) + has_word_row_idx = has_word_mask.reshape([-1]) + word_encoding = word_encoding[has_word_row_idx] + assert word_encoding.shape[0] == num_nodes + return word_encoding, batch_word_encoding, has_word_row_idx + + @staticmethod + def _postprocess_word2ph(word_encoding, ph2word, t_p): + word_encoding = F.pad(word_encoding,[0,0,1,0]) + ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]]) + out = torch.gather(word_encoding, 1, ph2word_) # [B, T, H] + return out + + @staticmethod + def _repeat_one_sequence(x, d, T): + """Repeat each frame according to duration.""" + if d.sum() == 0: + d = d.fill_(1) + hid = x.shape[-1] + expanded_lst = [x_.repeat(int(d_), 1) for x_, d_ in zip(x, d) if d_ != 0] + expanded = torch.cat(expanded_lst, dim=0) + if T > expanded.shape[0]: + expanded = torch.cat([expanded, torch.zeros([T - expanded.shape[0], hid]).to(expanded.device)], dim=0) + return expanded + + def word_forward(self, graph_lst, word_encoding, etypes_lst): + """ + word encoding in, word encoding out. + """ + batched_graph = dgl.batch(graph_lst) + inp = word_encoding + batched_etypes = torch.cat(etypes_lst) # [num_edges_in_batch, 1] + assert batched_graph.num_nodes() == inp.shape[0] + + gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes) + if self.dropout_after_gae: + gcc1_out = self.dropout(gcc1_out) + gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes) # [num_nodes_in_batch, hin] + if self.dropout_after_gae: + gcc2_out = self.ggc_2(batched_graph, gcc2_out, batched_etypes) + if self.skip_connect: + assert self.in_dim == self.hid_dim and self.hid_dim == self.out_dim + gcc2_out = inp + gcc1_out + gcc2_out + + word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1]) + max_len = max(word_len) + has_word_mask = sequence_mask(word_len, max_len) # [batch, t_p, 1] + has_word_row_idx = has_word_mask.reshape([-1]) + bs = len(graph_lst) + t_w = max([g.num_nodes() for g in graph_lst]) + hid = word_encoding.shape[-1] + output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device) + output[has_word_row_idx] = gcc2_out + output = output.reshape([bs, t_w, hid]) + word_level_output = output + return torch.transpose(word_level_output, 1, 2) + + def forward(self, graph_lst, ph_encoding, ph2word, etypes_lst, return_word_encoding=False): + """ + graph_lst: [list of dgl_graph] + ph_encoding: [batch, hid, t_p] + ph2word: [list of list[1,2,2,2,3,3,3]] + etypes_lst: [list of etypes]; etypes: torch.LongTensor + """ + t_p = ph_encoding.shape[-1] + ph_encoding = ph_encoding.transpose(1,2) # [batch, t_p, hid] + word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1]) + batched_graph = dgl.batch(graph_lst) + inp, batched_word_encoding, has_word_row_idx = self._process_ph_to_word_encoding(ph_encoding, ph2word, + word_len=word_len) # [num_nodes_in_batch, in_dim] + bs, t_w, hid = batched_word_encoding.shape + batched_etypes = torch.cat(etypes_lst) # [num_edges_in_batch, 1] + gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes) + gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes) # [num_nodes_in_batch, hin] + # skip connection + gcc2_out = inp + gcc1_out + gcc2_out # [n_nodes, hid] + + output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device) + output[has_word_row_idx] = gcc2_out + output = output.reshape([bs, t_w, hid]) + word_level_output = output + output = self._postprocess_word2ph(word_level_output, ph2word, t_p) # [batch, t_p, hid] + output = torch.transpose(output, 1, 2) + + if return_word_encoding: + return output, torch.transpose(word_level_output, 1, 2) + else: + return output + +if __name__ == '__main__': + # Unit Test for batching graphs + from modules.syntaspeech.syntactic_graph_buider import Sentence2GraphParser, plot_dgl_sentence_graph + parser = Sentence2GraphParser("en") + + # Unit Test for English Graph Builder + text1 = "To be or not to be , that 's a question ." + text2 = "I love you . You love me . Mixue ice-scream and tea ." + graph1, etypes1 = parser.parse(text1) + graph2, etypes2 = parser.parse(text2) + batched_text = " " + text1 + " " + " " + " " + text2 + " " + batched_nodes = [graph1.num_nodes(), graph2.num_nodes()] + plot_dgl_sentence_graph(dgl.batch([graph1, graph2]), {i: w for i, w in enumerate(batched_text.split(" "))}) + etypes_lst = [etypes1, etypes2] + + # Unit Test for Graph Encoder forward + in_feats = 4 + out_feats = 4 + enc = GraphAuxEnc(in_dim=in_feats, hid_dim=in_feats, out_dim=out_feats) + ph2word = torch.tensor([ + [1, 2, 3, 3, 3, 4, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 0], + [1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] + ]) + inp = torch.randn([2, in_feats, 17]) # [N_sentence, feat, ph_length] + graph_lst = [graph1, graph2] + out = enc(graph_lst, inp, ph2word, etypes_lst) + print(out.shape) # [N_sentence, feat, ph_length] diff --git a/NeuralSeq/modules/syntaspeech/syntaspeech.py b/NeuralSeq/modules/syntaspeech/syntaspeech.py new file mode 100644 index 0000000000000000000000000000000000000000..c74b6a53765026919ebd3e583861051441c508da --- /dev/null +++ b/NeuralSeq/modules/syntaspeech/syntaspeech.py @@ -0,0 +1,274 @@ +import math +import torch +from torch import nn +from torch.nn import Linear +from utils.hparams import hparams +from modules.commons.conv import ConvBlocks, ConditionalConvBlocks +from modules.commons.common_layers import Embedding +from modules.commons.rel_transformer import RelTransformerEncoder +from modules.commons.transformer import MultiheadAttention, FFTBlocks +from modules.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word +from modules.tts.fastspeech import FS_DECODERS, FastSpeech +from modules.portaspeech.fvae import SyntaFVAE, FVAE +from utils.nn.seq_utils import group_hidden_by_segs +from modules.fastspeech.tts_modules import SyntaDurationPredictor + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + def forward(self, x): + """ + + :param x: [B, T] + :return: [B, T, H] + """ + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, :, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class SyntaSpeech(FastSpeech): + def __init__(self, ph_dict_size, word_dict_size, out_dims=None): + super().__init__(ph_dict_size, out_dims) + # build linguistic encoder + if hparams['num_spk'] > 1: + self.spk_embed_proj = Embedding(hparams['num_spk'], self.hidden_size) + if hparams['use_word_encoder']: + self.word_encoder = RelTransformerEncoder( + word_dict_size, self.hidden_size, self.hidden_size, self.hidden_size, 2, + hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) + if hparams['dur_level'] == 'word': + if hparams['word_encoder_type'] == 'rel_fft': + self.ph2word_encoder = RelTransformerEncoder( + 0, self.hidden_size, self.hidden_size, self.hidden_size, 2, + hparams['word_enc_layers'], hparams['enc_ffn_kernel_size']) + if hparams['word_encoder_type'] == 'fft': + self.ph2word_encoder = FFTBlocks( + self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads']) + self.sin_pos = SinusoidalPosEmb(self.hidden_size) + self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size) + self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False) + self.attn.enable_torch_version = False + if hparams['text_encoder_postnet']: + self.text_encoder_postnet = ConvBlocks( + self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2) + else: + self.sin_pos = SinusoidalPosEmb(self.hidden_size) + + predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size + self.dur_predictor = SyntaDurationPredictor( + self.hidden_size, + n_chans=predictor_hidden, + n_layers=hparams['dur_predictor_layers'], + dropout_rate=hparams['predictor_dropout'], + kernel_size=hparams['dur_predictor_kernel']) + # build VAE decoder + if hparams['use_fvae']: + del self.decoder + del self.mel_out + if hparams.get("use_gae_in_prior", True): + self.fvae = SyntaFVAE( + c_in_out=self.out_dims, + hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'], + kernel_size=hparams['fvae_kernel_size'], + enc_n_layers=hparams['fvae_enc_n_layers'], + dec_n_layers=hparams['fvae_dec_n_layers'], + c_cond=self.hidden_size, + use_prior_flow=hparams['use_prior_flow'], + flow_hidden=hparams['prior_flow_hidden'], + flow_kernel_size=hparams['prior_flow_kernel_size'], + flow_n_steps=hparams['prior_flow_n_blocks'], + strides=[hparams['fvae_strides']], + encoder_type=hparams['fvae_encoder_type'], + decoder_type=hparams['fvae_decoder_type'], + ) + else: + self.fvae = FVAE( + c_in_out=self.out_dims, + hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'], + kernel_size=hparams['fvae_kernel_size'], + enc_n_layers=hparams['fvae_enc_n_layers'], + dec_n_layers=hparams['fvae_dec_n_layers'], + c_cond=self.hidden_size, + use_prior_flow=hparams['use_prior_flow'], + flow_hidden=hparams['prior_flow_hidden'], + flow_kernel_size=hparams['prior_flow_kernel_size'], + flow_n_steps=hparams['prior_flow_n_blocks'], + strides=[hparams['fvae_strides']], + encoder_type=hparams['fvae_encoder_type'], + decoder_type=hparams['fvae_decoder_type'], + ) + else: + self.decoder = FS_DECODERS[hparams['decoder_type']](hparams) + self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True) + if hparams['use_pitch_embed']: + self.pitch_embed = Embedding(300, self.hidden_size, 0) + if hparams['add_word_pos']: + self.word_pos_proj = Linear(self.hidden_size, self.hidden_size) + + def build_embedding(self, dictionary, embed_dim): + num_embeddings = len(dictionary) + emb = Embedding(num_embeddings, embed_dim, self.padding_idx) + return emb + + def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None, + spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None, + global_step=None, graph_lst=None, etypes_lst=None, *args, **kwargs): + + if hparams['use_spk_embed']: + spk_embed = spk_embed + elif hparams['use_spk_id']: + spk_embed = self.spk_embed_proj(spk_id)[:, None, :] + else: + spk_embed = 0 + + ret = {} + style_embed = self.forward_style_embed(spk_embed, spk_id) # speaker embedding, [B, 1, C] + x, tgt_nonpadding = self.run_text_encoder( + txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst=graph_lst, etypes_lst=etypes_lst, **kwargs) + x = x + style_embed # it maybe necessary to achieve multi-speaker + x = x * tgt_nonpadding + ret['nonpadding'] = tgt_nonpadding + if hparams['use_pitch_embed']: + x = x + self.pitch_embed(pitch) + ret['decoder_inp'] = x + if infer and (mel2ph is None or mel2word is None): + mel2word = ret['mel2word'] + ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step, + mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) + return ret + + def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst, etypes_lst, **kwargs): + word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1 # [B, T_mel, T_word] + src_nonpadding = (txt_tokens > 0).float()[:, :, None] + use_bert = hparams.get("use_bert") is True + if use_bert: + ph_encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word, + graph_lst=graph_lst, etypes_lst=etypes_lst, + cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed + else: + ph_encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed + if hparams['use_word_encoder']: + word_encoder_out = self.word_encoder(word_tokens) + style_embed + ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word) + + dur_input = ph_encoder_out * src_nonpadding + if hparams['dur_level'] == 'word': + word_encoder_out = 0 + h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0] + word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word) + if hparams['use_word_encoder']: + word_encoder_out = word_encoder_out + self.word_encoder(word_tokens) + mel2word = self.forward_dur(dur_input, mel2word, ret, ph2word=ph2word, word_len=word_len, graph_lst=graph_lst, etypes_lst=etypes_lst) + mel2word = clip_mel2token_to_multiple(mel2word, hparams['frames_multiple']) + ret['mel2word'] = mel2word + tgt_nonpadding = (mel2word > 0).float()[:, :, None] + enc_pos = self.get_pos_embed(word2word, ph2word) # [B, T_ph, H] + dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] + dec_word_mask = build_word_mask(mel2word, ph2word) # [B, T_mel, T_ph] + x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask) + if hparams['add_word_pos']: + x = x + self.word_pos_proj(dec_pos) + ret['attn'] = weight + else: + mel2ph = self.forward_dur(dur_input, mel2ph, ret) + mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple']) + mel2word = mel2ph_to_mel2word(mel2ph, ph2word) + x = expand_states(ph_encoder_out, mel2ph) + if hparams['add_word_pos']: + dec_pos = self.get_pos_embed(word2word, mel2word) # [B, T_mel, H] + x = x + self.word_pos_proj(dec_pos) + tgt_nonpadding = (mel2ph > 0).float()[:, :, None] + if hparams['use_word_encoder']: + x = x + expand_states(word_encoder_out, mel2word) + return x, tgt_nonpadding + + def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask): + ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1)) + word_enc_out_expend = expand_states(word_encoder_out, mel2word) + word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1) + if hparams['text_encoder_postnet']: + word_enc_out_expend = self.dec_res_proj(word_enc_out_expend) + word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend) + dec_q = x_res = word_enc_out_expend + else: + dec_q = self.dec_query_proj(word_enc_out_expend) + x_res = self.dec_res_proj(word_enc_out_expend) + ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1) + x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9) + x = x.transpose(0, 1) + x = x + x_res + return x, weight + + def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0, + mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None): + if not hparams['use_fvae']: + x = self.decoder(x) + x = self.mel_out(x) + ret['kl'] = 0 + return x * tgt_nonpadding + else: + # x is the phoneme encoding + x = x.transpose(1, 2) # [B, H, T] + tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2) # [B, H, T] + if infer: + z = self.fvae(cond=x, infer=True, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) + else: + tgt_mels = tgt_mels.transpose(1, 2) # [B, 80, T] + z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae( + tgt_mels, tgt_nonpadding_BHT, cond=x, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst) + if global_step < hparams['posterior_start_steps']: + z = torch.randn_like(z) + x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2) + ret['pre_mel_out'] = x_recon + return x_recon + + def forward_dur(self, dur_input, mel2word, ret, **kwargs): + """ + + :param dur_input: [B, T_txt, H] + :param mel2ph: [B, T_mel] + :param txt_tokens: [B, T_txt] + :param ret: + :return: + """ + word_len = kwargs['word_len'] + ph2word = kwargs['ph2word'] + graph_lst = kwargs['graph_lst'] + etypes_lst = kwargs['etypes_lst'] + src_padding = dur_input.data.abs().sum(-1) == 0 + dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach()) + dur = self.dur_predictor(dur_input, src_padding, ph2word, graph_lst, etypes_lst) + + B, T_ph = ph2word.shape + dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur) + dur = dur[:, 1:] + ret['dur'] = dur + if mel2word is None: + mel2word = self.length_regulator(dur).detach() + return mel2word + + def get_pos_embed(self, word2word, x2word): + x_pos = build_word_mask(word2word, x2word).float() # [B, T_word, T_ph] + x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1) + x_pos = self.sin_pos(x_pos.float()) # [B, T_ph, H] + return x_pos + + def store_inverse_all(self): + def remove_weight_norm(m): + try: + if hasattr(m, 'store_inverse'): + m.store_inverse() + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) diff --git a/NeuralSeq/tasks/__pycache__/base_task.cpython-37.pyc b/NeuralSeq/tasks/__pycache__/base_task.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c38ad7f625a014f758733f260eca687c19a59c1a Binary files /dev/null and b/NeuralSeq/tasks/__pycache__/base_task.cpython-37.pyc differ diff --git a/NeuralSeq/tasks/__pycache__/base_task.cpython-38.pyc b/NeuralSeq/tasks/__pycache__/base_task.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3c4bee9e9a48e97b59101c1802551344d20988ca Binary files /dev/null and b/NeuralSeq/tasks/__pycache__/base_task.cpython-38.pyc differ diff --git a/NeuralSeq/tasks/base_task.py b/NeuralSeq/tasks/base_task.py new file mode 100644 index 0000000000000000000000000000000000000000..b74d25c85ce8a86865c5d5a09f3f92579ffb2074 --- /dev/null +++ b/NeuralSeq/tasks/base_task.py @@ -0,0 +1,360 @@ +import glob +import re +import subprocess +from datetime import datetime + +import matplotlib + +matplotlib.use('Agg') + +from utils.hparams import hparams, set_hparams +import random +import sys +import numpy as np +import torch.distributed as dist +from pytorch_lightning.loggers import TensorBoardLogger +from utils.pl_utils import LatestModelCheckpoint, BaseTrainer, data_loader, DDP +from torch import nn +import torch.utils.data +import utils +import logging +import os + +torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system')) + +log_format = '%(asctime)s %(message)s' +logging.basicConfig(stream=sys.stdout, level=logging.INFO, + format=log_format, datefmt='%m/%d %I:%M:%S %p') + + +class BaseDataset(torch.utils.data.Dataset): + def __init__(self, shuffle): + super().__init__() + self.hparams = hparams + self.shuffle = shuffle + self.sort_by_len = hparams['sort_by_len'] + self.sizes = None + + @property + def _sizes(self): + return self.sizes + + def __getitem__(self, index): + raise NotImplementedError + + def collater(self, samples): + raise NotImplementedError + + def __len__(self): + return len(self._sizes) + + def num_tokens(self, index): + return self.size(index) + + def size(self, index): + """Return an example's size as a float or tuple. This value is used when + filtering a dataset with ``--max-positions``.""" + size = min(self._sizes[index], hparams['max_frames']) + return size + + def ordered_indices(self): + """Return an ordered list of indices. Batches will be constructed based + on this order.""" + if self.shuffle: + indices = np.random.permutation(len(self)) + if self.sort_by_len: + indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')] + # 先random, 然后稳定排序, 保证排序后同长度的数据顺序是依照random permutation的 (被其随机打乱). + else: + indices = np.arange(len(self)) + return indices + + @property + def num_workers(self): + return int(os.getenv('NUM_WORKERS', hparams['ds_workers'])) + + +class BaseTask(nn.Module): + def __init__(self, *args, **kwargs): + # dataset configs + super(BaseTask, self).__init__(*args, **kwargs) + self.current_epoch = 0 + self.global_step = 0 + self.loaded_optimizer_states_dict = {} + self.trainer = None + self.logger = None + self.on_gpu = False + self.use_dp = False + self.use_ddp = False + self.example_input_array = None + + self.max_tokens = hparams['max_tokens'] + self.max_sentences = hparams['max_sentences'] + self.max_eval_tokens = hparams['max_eval_tokens'] + if self.max_eval_tokens == -1: + hparams['max_eval_tokens'] = self.max_eval_tokens = self.max_tokens + self.max_eval_sentences = hparams['max_eval_sentences'] + if self.max_eval_sentences == -1: + hparams['max_eval_sentences'] = self.max_eval_sentences = self.max_sentences + + self.model = None + self.training_losses_meter = None + + ########### + # Training, validation and testing + ########### + def build_model(self): + raise NotImplementedError + + def load_ckpt(self, ckpt_base_dir, current_model_name=None, model_name='model', force=True, strict=True): + # This function is updated on 2021.12.13 + if current_model_name is None: + current_model_name = model_name + utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict) + + def on_epoch_start(self): + self.training_losses_meter = {'total_loss': utils.AvgrageMeter()} + + def _training_step(self, sample, batch_idx, optimizer_idx): + """ + + :param sample: + :param batch_idx: + :return: total loss: torch.Tensor, loss_log: dict + """ + raise NotImplementedError + + def training_step(self, sample, batch_idx, optimizer_idx=-1): + loss_ret = self._training_step(sample, batch_idx, optimizer_idx) + self.opt_idx = optimizer_idx + if loss_ret is None: + return {'loss': None} + total_loss, log_outputs = loss_ret + log_outputs = utils.tensors_to_scalars(log_outputs) + for k, v in log_outputs.items(): + if k not in self.training_losses_meter: + self.training_losses_meter[k] = utils.AvgrageMeter() + if not np.isnan(v): + self.training_losses_meter[k].update(v) + self.training_losses_meter['total_loss'].update(total_loss.item()) + + try: + log_outputs['lr'] = self.scheduler.get_lr() + if isinstance(log_outputs['lr'], list): + log_outputs['lr'] = log_outputs['lr'][0] + except: + pass + + # log_outputs['all_loss'] = total_loss.item() + progress_bar_log = log_outputs + tb_log = {f'tr/{k}': v for k, v in log_outputs.items()} + return { + 'loss': total_loss, + 'progress_bar': progress_bar_log, + 'log': tb_log + } + + def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx): + optimizer.step() + optimizer.zero_grad() + if self.scheduler is not None: + self.scheduler.step(self.global_step // hparams['accumulate_grad_batches']) + + def on_epoch_end(self): + loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()} + print(f"\n==============\n " + f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}" + f"\n==============\n") + + def validation_step(self, sample, batch_idx): + """ + + :param sample: + :param batch_idx: + :return: output: dict + """ + raise NotImplementedError + + def _validation_end(self, outputs): + """ + + :param outputs: + :return: loss_output: dict + """ + raise NotImplementedError + + def validation_end(self, outputs): + loss_output = self._validation_end(outputs) + print(f"\n==============\n " + f"valid results: {loss_output}" + f"\n==============\n") + return { + 'log': {f'val/{k}': v for k, v in loss_output.items()}, + 'val_loss': loss_output['total_loss'] + } + + def build_scheduler(self, optimizer): + raise NotImplementedError + + def build_optimizer(self, model): + raise NotImplementedError + + def configure_optimizers(self): + optm = self.build_optimizer(self.model) + self.scheduler = self.build_scheduler(optm) + return [optm] + + def test_start(self): + pass + + def test_step(self, sample, batch_idx): + return self.validation_step(sample, batch_idx) + + def test_end(self, outputs): + return self.validation_end(outputs) + + ########### + # Running configuration + ########### + + @classmethod + def start(cls): + set_hparams() + os.environ['MASTER_PORT'] = str(random.randint(15000, 30000)) + random.seed(hparams['seed']) + np.random.seed(hparams['seed']) + task = cls() + work_dir = hparams['work_dir'] + trainer = BaseTrainer(checkpoint_callback=LatestModelCheckpoint( + filepath=work_dir, + verbose=True, + monitor='val_loss', + mode='min', + num_ckpt_keep=hparams['num_ckpt_keep'], + save_best=hparams['save_best'], + period=1 if hparams['save_ckpt'] else 100000 + ), + logger=TensorBoardLogger( + save_dir=work_dir, + name='lightning_logs', + version='lastest' + ), + gradient_clip_val=hparams['clip_grad_norm'], + val_check_interval=hparams['val_check_interval'], + row_log_interval=hparams['log_interval'], + max_updates=hparams['max_updates'], + num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams[ + 'validate'] else 10000, + accumulate_grad_batches=hparams['accumulate_grad_batches']) + if not hparams['infer']: # train + t = datetime.now().strftime('%Y%m%d%H%M%S') + code_dir = f'{work_dir}/codes/{t}' + subprocess.check_call(f'mkdir -p "{code_dir}"', shell=True) + for c in hparams['save_codes']: + subprocess.check_call(f'cp -r "{c}" "{code_dir}/"', shell=True) + print(f"| Copied codes to {code_dir}.") + trainer.checkpoint_callback.task = task + trainer.fit(task) + else: + trainer.test(task) + + def configure_ddp(self, model, device_ids): + model = DDP( + model, + device_ids=device_ids, + find_unused_parameters=True + ) + if dist.get_rank() != 0 and not hparams['debug']: + sys.stdout = open(os.devnull, "w") + sys.stderr = open(os.devnull, "w") + random.seed(hparams['seed']) + np.random.seed(hparams['seed']) + return model + + def training_end(self, *args, **kwargs): + return None + + def init_ddp_connection(self, proc_rank, world_size): + set_hparams(print_hparams=False) + # guarantees unique ports across jobs from same grid search + default_port = 12910 + # if user gave a port number, use that one instead + try: + default_port = os.environ['MASTER_PORT'] + except Exception: + os.environ['MASTER_PORT'] = str(default_port) + + # figure out the root node addr + root_node = '127.0.0.2' + root_node = self.trainer.resolve_root_node_address(root_node) + os.environ['MASTER_ADDR'] = root_node + dist.init_process_group('nccl', rank=proc_rank, world_size=world_size) + + @data_loader + def train_dataloader(self): + return None + + @data_loader + def test_dataloader(self): + return None + + @data_loader + def val_dataloader(self): + return None + + def on_load_checkpoint(self, checkpoint): + pass + + def on_save_checkpoint(self, checkpoint): + pass + + def on_sanity_check_start(self): + pass + + def on_train_start(self): + pass + + def on_train_end(self): + pass + + def on_batch_start(self, batch): + pass + + def on_batch_end(self): + pass + + def on_pre_performance_check(self): + pass + + def on_post_performance_check(self): + pass + + def on_before_zero_grad(self, optimizer): + pass + + def on_after_backward(self): + pass + + def backward(self, loss, optimizer): + loss.backward() + + def grad_norm(self, norm_type): + results = {} + total_norm = 0 + for name, p in self.named_parameters(): + if p.requires_grad: + try: + param_norm = p.grad.data.norm(norm_type) + total_norm += param_norm ** norm_type + norm = param_norm ** (1 / norm_type) + + grad = round(norm.data.cpu().numpy().flatten()[0], 3) + results['grad_{}_norm_{}'.format(norm_type, name)] = grad + except Exception: + # this param had no grad + pass + + total_norm = total_norm ** (1. / norm_type) + grad = round(total_norm.data.cpu().numpy().flatten()[0], 3) + results['grad_{}_norm_total'.format(norm_type)] = grad + return results diff --git a/NeuralSeq/tasks/run.py b/NeuralSeq/tasks/run.py new file mode 100644 index 0000000000000000000000000000000000000000..82c7559cec873eebf7c2c0ab6554895e21de7e7c --- /dev/null +++ b/NeuralSeq/tasks/run.py @@ -0,0 +1,15 @@ +import importlib +from utils.hparams import set_hparams, hparams + + +def run_task(): + assert hparams['task_cls'] != '' + pkg = ".".join(hparams["task_cls"].split(".")[:-1]) + cls_name = hparams["task_cls"].split(".")[-1] + task_cls = getattr(importlib.import_module(pkg), cls_name) + task_cls.start() + + +if __name__ == '__main__': + set_hparams() + run_task() diff --git a/NeuralSeq/tasks/svs/__init__.py b/NeuralSeq/tasks/svs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/NeuralSeq/tasks/svs/diffsinger_task.py b/NeuralSeq/tasks/svs/diffsinger_task.py new file mode 100644 index 0000000000000000000000000000000000000000..78e65447fe78ca80bd0e67ebe5581794fc3764aa --- /dev/null +++ b/NeuralSeq/tasks/svs/diffsinger_task.py @@ -0,0 +1,490 @@ +import torch + +import utils +from utils.hparams import hparams +from modules.diff.net import DiffNet +from modules.diff.shallow_diffusion_tts import GaussianDiffusion, OfflineGaussianDiffusion +from tasks.svs.diffspeech_task import DiffSpeechTask +from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder +from modules.fastspeech.pe import PitchExtractor +from modules.fastspeech.fs2 import FastSpeech2 +from modules.diffsinger_midi.fs2 import FastSpeech2MIDI +from modules.fastspeech.tts_modules import mel2ph_to_dur + +from modules.diff.candidate_decoder import FFT +from utils.pitch_utils import denorm_f0 +from tasks.tts.fs2_utils import FastSpeechDataset +from tasks.tts.fs2 import FastSpeech2Task + +import numpy as np +import os +import torch.nn.functional as F + +DIFF_DECODERS = { + 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), + 'fft': lambda hp: FFT( + hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']), +} + + +class DiffSingerTask(DiffSpeechTask): + def __init__(self): + super(DiffSingerTask, self).__init__() + self.dataset_cls = FastSpeechDataset + self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + self.pe = PitchExtractor().cuda() + utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) + self.pe.eval() + + def build_tts_model(self): + # import torch + # from tqdm import tqdm + # v_min = torch.ones([80]) * 100 + # v_max = torch.ones([80]) * -100 + # for i, ds in enumerate(tqdm(self.dataset_cls('train'))): + # v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max) + # v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min) + # if i % 100 == 0: + # print(i, v_min, v_max) + # print('final', v_min, v_max) + mel_bins = hparams['audio_num_mel_bins'] + self.model = GaussianDiffusion( + phone_encoder=self.phone_encoder, + out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + K_step=hparams['K_step'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + if hparams['fs2_ckpt'] != '': + utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) + # self.model.fs2.decoder = None + for k, v in self.model.fs2.named_parameters(): + v.requires_grad = False + + def validation_step(self, sample, batch_idx): + outputs = {} + txt_tokens = sample['txt_tokens'] # [B, T_t] + + target = sample['mels'] # [B, T_s, 80] + energy = sample['energy'] + # fs2_mel = sample['fs2_mels'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + mel2ph = sample['mel2ph'] + f0 = sample['f0'] + uv = sample['uv'] + + outputs['losses'] = {} + + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) + + + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + model_out = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) + + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel + pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel + else: + gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) + pred_f0 = model_out.get('f0_denorm') + self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') + self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') + return outputs + + +class ShallowDiffusionOfflineDataset(FastSpeechDataset): + def __getitem__(self, index): + sample = super(ShallowDiffusionOfflineDataset, self).__getitem__(index) + item = self._get_item(index) + + if self.prefix != 'train' and hparams['fs2_ckpt'] != '': + fs2_ckpt = os.path.dirname(hparams['fs2_ckpt']) + item_name = item['item_name'] + fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy')) # ~M generated by FFT-singer. + sample['fs2_mel'] = fs2_mel + return sample + + def collater(self, samples): + batch = super(ShallowDiffusionOfflineDataset, self).collater(samples) + if self.prefix != 'train' and hparams['fs2_ckpt'] != '': + batch['fs2_mels'] = utils.collate_2d([s['fs2_mel'] for s in samples], 0.0) + return batch + + +class DiffSingerOfflineTask(DiffSingerTask): + def __init__(self): + super(DiffSingerOfflineTask, self).__init__() + self.dataset_cls = ShallowDiffusionOfflineDataset + + def build_tts_model(self): + mel_bins = hparams['audio_num_mel_bins'] + self.model = OfflineGaussianDiffusion( + phone_encoder=self.phone_encoder, + out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + K_step=hparams['K_step'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + # if hparams['fs2_ckpt'] != '': + # utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) + # self.model.fs2.decoder = None + + def run_model(self, model, sample, return_output=False, infer=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + fs2_mel = None #sample['fs2_mels'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=[target, fs2_mel], f0=f0, uv=uv, energy=energy, infer=infer) + + losses = {} + if 'diff_loss' in output: + losses['mel'] = output['diff_loss'] + # self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) + # if hparams['use_pitch_embed']: + # self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + + if not return_output: + return losses + else: + return losses, output + + def validation_step(self, sample, batch_idx): + outputs = {} + txt_tokens = sample['txt_tokens'] # [B, T_t] + + target = sample['mels'] # [B, T_s, 80] + energy = sample['energy'] + # fs2_mel = sample['fs2_mels'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + mel2ph = sample['mel2ph'] + f0 = sample['f0'] + uv = sample['uv'] + + outputs['losses'] = {} + + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) + + + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + fs2_mel = sample['fs2_mels'] + model_out = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, + ref_mels=[None, fs2_mel], infer=True) + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel + pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel + else: + gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) + pred_f0 = model_out.get('f0_denorm') + self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') + self.plot_mel(batch_idx, sample['mels'], fs2_mel, name=f'fs2mel_{batch_idx}') + return outputs + + def test_step(self, sample, batch_idx): + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + txt_tokens = sample['txt_tokens'] + energy = sample['energy'] + if hparams['profile_infer']: + pass + else: + mel2ph, uv, f0 = None, None, None + if hparams['use_gt_dur']: + mel2ph = sample['mel2ph'] + if hparams['use_gt_f0']: + f0 = sample['f0'] + uv = sample['uv'] + fs2_mel = sample['fs2_mels'] + outputs = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=[None, fs2_mel], energy=energy, + infer=True) + sample['outputs'] = self.model.out2mel(outputs['mel_out']) + sample['mel2ph_pred'] = outputs['mel2ph'] + + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel + sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel + else: + sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) + sample['f0_pred'] = outputs.get('f0_denorm') + return self.after_infer(sample) + + +class MIDIDataset(FastSpeechDataset): + def __getitem__(self, index): + sample = super(MIDIDataset, self).__getitem__(index) + item = self._get_item(index) + sample['f0_midi'] = torch.FloatTensor(item['f0_midi']) + sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] + + return sample + + def collater(self, samples): + batch = super(MIDIDataset, self).collater(samples) + batch['f0_midi'] = utils.collate_1d([s['f0_midi'] for s in samples], 0.0) + batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) + # print((batch['pitch_midi'] == f0_to_coarse(batch['f0_midi'])).all()) + return batch + + +class OpencpopDataset(FastSpeechDataset): + def __getitem__(self, index): + sample = super(OpencpopDataset, self).__getitem__(index) + item = self._get_item(index) + sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']] + sample['midi_dur'] = torch.FloatTensor(item['midi_dur'])[:hparams['max_frames']] + sample['is_slur'] = torch.LongTensor(item['is_slur'])[:hparams['max_frames']] + sample['word_boundary'] = torch.LongTensor(item['word_boundary'])[:hparams['max_frames']] + return sample + + def collater(self, samples): + batch = super(OpencpopDataset, self).collater(samples) + batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0) + batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0) + batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0) + batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0) + return batch + + +class DiffSingerMIDITask(DiffSingerTask): + def __init__(self): + super(DiffSingerMIDITask, self).__init__() + # self.dataset_cls = MIDIDataset + self.dataset_cls = OpencpopDataset + + def run_model(self, model, sample, return_output=False, infer=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] + mel2ph = sample['mel2ph'] + if hparams.get('switch_midi2f0_step') is not None and self.global_step > hparams['switch_midi2f0_step']: + f0 = None + uv = None + else: + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer, pitch_midi=sample['pitch_midi'], + midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) + + losses = {} + if 'diff_loss' in output: + losses['mel'] = output['diff_loss'] + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + if not return_output: + return losses + else: + return losses, output + + def validation_step(self, sample, batch_idx): + outputs = {} + txt_tokens = sample['txt_tokens'] # [B, T_t] + + target = sample['mels'] # [B, T_s, 80] + energy = sample['energy'] + # fs2_mel = sample['fs2_mels'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + mel2ph = sample['mel2ph'] + + outputs['losses'] = {} + + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) + + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + model_out = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=None, uv=None, energy=energy, ref_mels=None, infer=True, + pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) + + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel + pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel + else: + gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) + pred_f0 = model_out.get('f0_denorm') + self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}') + self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}') + if hparams['use_pitch_embed']: + self.plot_pitch(batch_idx, sample, model_out) + return outputs + + def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): + """ + :param dur_pred: [B, T], float, log scale + :param mel2ph: [B, T] + :param txt_tokens: [B, T] + :param losses: + :return: + """ + B, T = txt_tokens.shape + nonpadding = (txt_tokens != 0).float() + dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding + is_sil = torch.zeros_like(txt_tokens).bool() + for p in self.sil_ph: + is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) + is_sil = is_sil.float() # [B, T_txt] + + # phone duration loss + if hparams['dur_loss'] == 'mse': + losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') + losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() + dur_pred = (dur_pred.exp() - 1).clamp(min=0) + else: + raise NotImplementedError + + # use linear scale for sent and word duration + if hparams['lambda_word_dur'] > 0: + idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] + # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur + word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) + word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) + wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') + word_nonpadding = (word_dur_g > 0).float() + wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() + losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] + if hparams['lambda_sent_dur'] > 0: + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') + losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] + + +class AuxDecoderMIDITask(FastSpeech2Task): + def __init__(self): + super().__init__() + # self.dataset_cls = MIDIDataset + self.dataset_cls = OpencpopDataset + + def build_tts_model(self): + if hparams.get('use_midi') is not None and hparams['use_midi']: + self.model = FastSpeech2MIDI(self.phone_encoder) + else: + self.model = FastSpeech2(self.phone_encoder) + + def run_model(self, model, sample, return_output=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False, pitch_midi=sample['pitch_midi'], + midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) + + losses = {} + self.add_mel_loss(output['mel_out'], target, losses) + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + if not return_output: + return losses + else: + return losses, output + + def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None): + """ + :param dur_pred: [B, T], float, log scale + :param mel2ph: [B, T] + :param txt_tokens: [B, T] + :param losses: + :return: + """ + B, T = txt_tokens.shape + nonpadding = (txt_tokens != 0).float() + dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding + is_sil = torch.zeros_like(txt_tokens).bool() + for p in self.sil_ph: + is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) + is_sil = is_sil.float() # [B, T_txt] + + # phone duration loss + if hparams['dur_loss'] == 'mse': + losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') + losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() + dur_pred = (dur_pred.exp() - 1).clamp(min=0) + else: + raise NotImplementedError + + # use linear scale for sent and word duration + if hparams['lambda_word_dur'] > 0: + idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1] + # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur + word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred) + word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt) + wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') + word_nonpadding = (word_dur_g > 0).float() + wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() + losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] + if hparams['lambda_sent_dur'] > 0: + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') + losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + mel_out = self.model.out2mel(model_out['mel_out']) + outputs = utils.tensors_to_scalars(outputs) + # if sample['mels'].shape[0] == 1: + # self.add_laplace_var(mel_out, sample['mels'], outputs) + if batch_idx < hparams['num_valid_plots']: + self.plot_mel(batch_idx, sample['mels'], mel_out) + self.plot_dur(batch_idx, sample, model_out) + if hparams['use_pitch_embed']: + self.plot_pitch(batch_idx, sample, model_out) + return outputs \ No newline at end of file diff --git a/NeuralSeq/tasks/svs/diffspeech_task.py b/NeuralSeq/tasks/svs/diffspeech_task.py new file mode 100644 index 0000000000000000000000000000000000000000..cf303d54ad882bea00ad02a0191bd0a71e3452fb --- /dev/null +++ b/NeuralSeq/tasks/svs/diffspeech_task.py @@ -0,0 +1,122 @@ +import torch + +import utils +from utils.hparams import hparams +from modules.diff.net import DiffNet +from modules.diff.shallow_diffusion_tts import GaussianDiffusion +from tasks.svs.task import DiffFsTask +from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder +from utils.pitch_utils import denorm_f0 +from tasks.tts.fs2_utils import FastSpeechDataset + +DIFF_DECODERS = { + 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), +} + + +class DiffSpeechTask(DiffFsTask): + def __init__(self): + super(DiffSpeechTask, self).__init__() + self.dataset_cls = FastSpeechDataset + self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() + + def build_tts_model(self): + mel_bins = hparams['audio_num_mel_bins'] + self.model = GaussianDiffusion( + phone_encoder=self.phone_encoder, + out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + K_step=hparams['K_step'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + if hparams['fs2_ckpt'] != '': + utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) + # self.model.fs2.decoder = None + for k, v in self.model.fs2.named_parameters(): + if not 'predictor' in k: + v.requires_grad = False + + def build_optimizer(self, model): + self.optimizer = optimizer = torch.optim.AdamW( + filter(lambda p: p.requires_grad, model.parameters()), + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + return optimizer + + def run_model(self, model, sample, return_output=False, infer=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] + mel2ph = sample['mel2ph'] + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + # fs2_mel = sample['fs2_mels'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) + + losses = {} + if 'diff_loss' in output: + losses['mel'] = output['diff_loss'] + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + if not return_output: + return losses + else: + return losses, output + + def validation_step(self, sample, batch_idx): + outputs = {} + txt_tokens = sample['txt_tokens'] # [B, T_t] + + energy = sample['energy'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + mel2ph = sample['mel2ph'] + f0 = sample['f0'] + uv = sample['uv'] + + outputs['losses'] = {} + + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) + + + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + # model_out = self.model( + # txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, infer=True) + # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}') + model_out = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) + gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) + self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm')) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) + return outputs + + ############ + # validation plots + ############ + def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None): + gt_wav = gt_wav[0].cpu().numpy() + wav_out = wav_out[0].cpu().numpy() + gt_f0 = gt_f0[0].cpu().numpy() + f0 = f0[0].cpu().numpy() + if is_mel: + gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0) + wav_out = self.vocoder.spec2wav(wav_out, f0=f0) + self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) + self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) + diff --git a/NeuralSeq/tasks/svs/task.py b/NeuralSeq/tasks/svs/task.py new file mode 100644 index 0000000000000000000000000000000000000000..896970e5318071d0d406f3d2378462cb77356925 --- /dev/null +++ b/NeuralSeq/tasks/svs/task.py @@ -0,0 +1,84 @@ +import torch + +import utils +from modules.diff.diffusion import GaussianDiffusion +from modules.diff.net import DiffNet +from tasks.tts.fs2 import FastSpeech2Task +from utils.hparams import hparams + + +DIFF_DECODERS = { + 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), +} + + +class DiffFsTask(FastSpeech2Task): + def build_tts_model(self): + mel_bins = hparams['audio_num_mel_bins'] + self.model = GaussianDiffusion( + phone_encoder=self.phone_encoder, + out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), + timesteps=hparams['timesteps'], + loss_type=hparams['diff_loss_type'], + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + ) + + def run_model(self, model, sample, return_output=False, infer=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) + + losses = {} + if 'diff_loss' in output: + losses['mel'] = output['diff_loss'] + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + if not return_output: + return losses + else: + return losses, output + + def _training_step(self, sample, batch_idx, _): + log_outputs = self.run_model(self.model, sample) + total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad]) + log_outputs['batch_size'] = sample['txt_tokens'].size()[0] + log_outputs['lr'] = self.scheduler.get_lr()[0] + return total_loss, log_outputs + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + _, model_out = self.run_model(self.model, sample, return_output=True, infer=True) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) + return outputs + + def build_scheduler(self, optimizer): + return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5) + + def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx): + if optimizer is None: + return + optimizer.step() + optimizer.zero_grad() + if self.scheduler is not None: + self.scheduler.step(self.global_step // hparams['accumulate_grad_batches']) diff --git a/NeuralSeq/tasks/tts/__pycache__/dataset_utils.cpython-38.pyc b/NeuralSeq/tasks/tts/__pycache__/dataset_utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bffa151ec062fbbfd7746f13f195945c2f6522af Binary files /dev/null and b/NeuralSeq/tasks/tts/__pycache__/dataset_utils.cpython-38.pyc differ diff --git a/NeuralSeq/tasks/tts/__pycache__/fs2.cpython-37.pyc b/NeuralSeq/tasks/tts/__pycache__/fs2.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..41d1fe773e827fb7e998e0b5c73eda16e45f4d11 Binary files /dev/null and b/NeuralSeq/tasks/tts/__pycache__/fs2.cpython-37.pyc differ diff --git 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b/NeuralSeq/tasks/tts/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9e31ce3aba637a5c373caf1559310ec029338533 --- /dev/null +++ b/NeuralSeq/tasks/tts/dataset_utils.py @@ -0,0 +1,259 @@ +from utils.cwt import get_lf0_cwt +import torch.optim +import torch.utils.data +import importlib +from utils.indexed_datasets import IndexedDataset +from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse +import numpy as np +from tasks.base_task import BaseDataset +import torch +import torch.optim +import torch.utils.data +import utils +import torch.distributions +from utils.hparams import hparams +from resemblyzer import VoiceEncoder +import json +from data_gen.tts.data_gen_utils import build_phone_encoder + +class BaseTTSDataset(BaseDataset): + def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): + super().__init__(shuffle) + self.data_dir = hparams['binary_data_dir'] if data_dir is None else data_dir + self.prefix = prefix + self.hparams = hparams + self.indexed_ds = None + self.ext_mel2ph = None + + def load_size(): + self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') + + if prefix == 'test': + if test_items is not None: + self.indexed_ds, self.sizes = test_items, test_sizes + else: + load_size() + if hparams['num_test_samples'] > 0: + self.avail_idxs = [x for x in range(hparams['num_test_samples']) \ + if x < len(self.sizes)] + if len(hparams['test_ids']) > 0: + self.avail_idxs = hparams['test_ids'] + self.avail_idxs + else: + self.avail_idxs = list(range(len(self.sizes))) + else: + load_size() + self.avail_idxs = list(range(len(self.sizes))) + + if hparams['min_frames'] > 0: + self.avail_idxs = [ + x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] + self.sizes = [self.sizes[i] for i in self.avail_idxs] + + def _get_item(self, index): + if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: + index = self.avail_idxs[index] + if self.indexed_ds is None: + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + return self.indexed_ds[index] + + def __getitem__(self, index): + hparams = self.hparams + item = self._get_item(index) + assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) + max_frames = hparams['max_frames'] + spec = torch.Tensor(item['mel'])[:max_frames] + max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] + spec = spec[:max_frames] + phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) + sample = { + "id": index, + "item_name": item['item_name'], + "text": item['txt'], + "txt_token": phone, + "mel": spec, + "mel_nonpadding": spec.abs().sum(-1) > 0, + } + if hparams['use_spk_embed']: + sample["spk_embed"] = torch.Tensor(item['spk_embed']) + if hparams['use_spk_id']: + sample["spk_id"] = int(item['spk_id']) + return sample + + def collater(self, samples): + if len(samples) == 0: + return {} + hparams = self.hparams + id = torch.LongTensor([s['id'] for s in samples]) + item_names = [s['item_name'] for s in samples] + text = [s['text'] for s in samples] + txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) + mels = utils.collate_2d([s['mel'] for s in samples], 0.0) + txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) + mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) + + batch = { + 'id': id, + 'item_name': item_names, + 'nsamples': len(samples), + 'text': text, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'mels': mels, + 'mel_lengths': mel_lengths, + } + + if hparams['use_spk_embed']: + spk_embed = torch.stack([s['spk_embed'] for s in samples]) + batch['spk_embed'] = spk_embed + if hparams['use_spk_id']: + spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) + batch['spk_ids'] = spk_ids + return batch + + +class FastSpeechDataset(BaseTTSDataset): + def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): + super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) + self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) + if prefix == 'test' and hparams['test_input_dir'] != '': + self.data_dir = hparams['test_input_dir'] + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + self.indexed_ds = sorted(self.indexed_ds, key=lambda item: item['item_name']) + items = {} + for i in range(len(self.indexed_ds)): + speaker = self.indexed_ds[i]['item_name'].split('_')[0] + if speaker not in items.keys(): + items[speaker] = [i] + else: + items[speaker].append(i) + sort_item = sorted(items.values(), key=lambda item_pre_speaker: len(item_pre_speaker), reverse=True) + self.avail_idxs = [n for a in sort_item for n in a][:hparams['num_test_samples']] + self.indexed_ds, self.sizes = self.load_test_inputs() + self.avail_idxs = [i for i in range(hparams['num_test_samples'])] + + if hparams['pitch_type'] == 'cwt': + _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) + + def __getitem__(self, index): + sample = super(FastSpeechDataset, self).__getitem__(index) + item = self._get_item(index) + hparams = self.hparams + max_frames = hparams['max_frames'] + spec = sample['mel'] + T = spec.shape[0] + phone = sample['txt_token'] + sample['energy'] = (spec.exp() ** 2).sum(-1).sqrt() + sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None + if hparams['use_pitch_embed']: + assert 'f0' in item + if hparams.get('normalize_pitch', False): + f0 = item["f0"] + if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: + f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ + hparams['f0_mean'] + f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) + pitch = f0_to_coarse(f0) + pitch = torch.LongTensor(pitch[:max_frames]) + else: + pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None + f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) + uv = torch.FloatTensor(uv) + f0 = torch.FloatTensor(f0) + if hparams['pitch_type'] == 'cwt': + cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames] + f0_mean = item.get('f0_mean', item.get('cwt_mean')) + f0_std = item.get('f0_std', item.get('cwt_std')) + sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std}) + elif hparams['pitch_type'] == 'ph': + if "f0_ph" in item: + f0 = torch.FloatTensor(item['f0_ph']) + else: + f0 = denorm_f0(f0, None, hparams) + f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0) + f0_phlevel_num = torch.zeros_like(phone).float().scatter_add( + 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1) + f0_ph = f0_phlevel_sum / f0_phlevel_num + f0, uv = norm_interp_f0(f0_ph, hparams) + else: + f0 = uv = torch.zeros_like(mel2ph) + pitch = None + sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch + if hparams['use_spk_embed']: + sample["spk_embed"] = torch.Tensor(item['spk_embed']) + if hparams['use_spk_id']: + sample["spk_id"] = item['spk_id'] + return sample + + def collater(self, samples): + if len(samples) == 0: + return {} + hparams = self.hparams + batch = super(FastSpeechDataset, self).collater(samples) + f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) + pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None + uv = utils.collate_1d([s['uv'] for s in samples]) + energy = utils.collate_1d([s['energy'] for s in samples], 0.0) + mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ + if samples[0]['mel2ph'] is not None else None + batch.update({ + 'mel2ph': mel2ph, + 'energy': energy, + 'pitch': pitch, + 'f0': f0, + 'uv': uv, + }) + if hparams['pitch_type'] == 'cwt': + cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples]) + f0_mean = torch.Tensor([s['f0_mean'] for s in samples]) + f0_std = torch.Tensor([s['f0_std'] for s in samples]) + batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std}) + return batch + + def load_test_inputs(self): + binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" + ph_set = json.load(open(ph_set_fn, 'r')) + print("| phone set: ", ph_set) + phone_encoder = build_phone_encoder(hparams['binary_data_dir']) + word_encoder = None + voice_encoder = VoiceEncoder().cuda() + encoder = [phone_encoder, word_encoder] + sizes = [] + items = [] + for i in range(len(self.avail_idxs)): + item = self._get_item(i) + + item2tgfn = f"{hparams['test_input_dir'].replace('binary', 'processed')}/mfa_outputs/{item['item_name']}.TextGrid" + item = binarizer_cls.process_item(item['item_name'], item['ph'], item['txt'], item2tgfn, + item['wav_fn'], item['spk_id'], encoder, hparams['binarization_args']) + item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ + if hparams['binarization_args']['with_spk_embed'] else None # 判断是否保存embedding文件 + items.append(item) + sizes.append(item['len']) + return items, sizes + +class FastSpeechWordDataset(FastSpeechDataset): + def __getitem__(self, index): + sample = super(FastSpeechWordDataset, self).__getitem__(index) + item = self._get_item(index) + max_frames = hparams['max_frames'] + sample["ph_words"] = item["ph_words"] + sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) + sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] + sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) + return sample + + def collater(self, samples): + batch = super(FastSpeechWordDataset, self).collater(samples) + ph_words = [s['ph_words'] for s in samples] + batch['ph_words'] = ph_words + word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) + batch['word_tokens'] = word_tokens + mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) + batch['mel2word'] = mel2word + ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) + batch['ph2word'] = ph2word + return batch diff --git a/NeuralSeq/tasks/tts/fs2.py b/NeuralSeq/tasks/tts/fs2.py new file mode 100644 index 0000000000000000000000000000000000000000..620d7f3faa53a5326ef97707b9de53506ab059bb --- /dev/null +++ b/NeuralSeq/tasks/tts/fs2.py @@ -0,0 +1,509 @@ +import matplotlib +matplotlib.use('Agg') +from utils import audio +import matplotlib.pyplot as plt +from data_gen.tts.data_gen_utils import get_pitch +from tasks.tts.fs2_utils import FastSpeechDataset +from utils.cwt import cwt2f0 +from utils.pl_utils import data_loader +import os +from multiprocessing.pool import Pool +from tqdm import tqdm +from modules.fastspeech.tts_modules import mel2ph_to_dur +from utils.hparams import hparams +from utils.plot import spec_to_figure, dur_to_figure, f0_to_figure +from utils.pitch_utils import denorm_f0 +from modules.fastspeech.fs2 import FastSpeech2 +from tasks.tts.tts import TtsTask +import torch +import torch.optim +import torch.utils.data +import torch.nn.functional as F +import utils +import torch.distributions +import numpy as np +from modules.commons.ssim import ssim + +class FastSpeech2Task(TtsTask): + def __init__(self): + super(FastSpeech2Task, self).__init__() + self.dataset_cls = FastSpeechDataset + self.mse_loss_fn = torch.nn.MSELoss() + mel_losses = hparams['mel_loss'].split("|") + self.loss_and_lambda = {} + for i, l in enumerate(mel_losses): + if l == '': + continue + if ':' in l: + l, lbd = l.split(":") + lbd = float(lbd) + else: + lbd = 1.0 + self.loss_and_lambda[l] = lbd + print("| Mel losses:", self.loss_and_lambda) + self.sil_ph = self.phone_encoder.sil_phonemes() + + @data_loader + def train_dataloader(self): + train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True) + return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, + endless=hparams['endless_ds']) + + @data_loader + def val_dataloader(self): + valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False) + return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences) + + @data_loader + def test_dataloader(self): + test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False) + return self.build_dataloader(test_dataset, False, self.max_eval_tokens, + self.max_eval_sentences, batch_by_size=False) + + def build_tts_model(self): + self.model = FastSpeech2(self.phone_encoder) + + def build_model(self): + self.build_tts_model() + if hparams['load_ckpt'] != '': + self.load_ckpt(hparams['load_ckpt'], strict=True) + utils.print_arch(self.model) + return self.model + + def _training_step(self, sample, batch_idx, _): + loss_output = self.run_model(self.model, sample) + total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) + loss_output['batch_size'] = sample['txt_tokens'].size()[0] + return total_loss, loss_output + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + mel_out = self.model.out2mel(model_out['mel_out']) + outputs = utils.tensors_to_scalars(outputs) + # if sample['mels'].shape[0] == 1: + # self.add_laplace_var(mel_out, sample['mels'], outputs) + if batch_idx < hparams['num_valid_plots']: + self.plot_mel(batch_idx, sample['mels'], mel_out) + self.plot_dur(batch_idx, sample, model_out) + if hparams['use_pitch_embed']: + self.plot_pitch(batch_idx, sample, model_out) + return outputs + + def _validation_end(self, outputs): + all_losses_meter = { + 'total_loss': utils.AvgrageMeter(), + } + for output in outputs: + n = output['nsamples'] + for k, v in output['losses'].items(): + if k not in all_losses_meter: + all_losses_meter[k] = utils.AvgrageMeter() + all_losses_meter[k].update(v, n) + all_losses_meter['total_loss'].update(output['total_loss'], n) + return {k: round(v.avg, 4) for k, v in all_losses_meter.items()} + + def run_model(self, model, sample, return_output=False): + txt_tokens = sample['txt_tokens'] # [B, T_t] + target = sample['mels'] # [B, T_s, 80] + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + uv = sample['uv'] + energy = sample['energy'] + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) + + output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, + ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False) + + losses = {} + self.add_mel_loss(output['mel_out'], target, losses) + self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) + if hparams['use_pitch_embed']: + self.add_pitch_loss(output, sample, losses) + if hparams['use_energy_embed']: + self.add_energy_loss(output['energy_pred'], energy, losses) + if not return_output: + return losses + else: + return losses, output + + ############ + # losses + ############ + def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None): + if mel_mix_loss is None: + for loss_name, lbd in self.loss_and_lambda.items(): + if 'l1' == loss_name: + l = self.l1_loss(mel_out, target) + elif 'mse' == loss_name: + raise NotImplementedError + elif 'ssim' == loss_name: + l = self.ssim_loss(mel_out, target) + elif 'gdl' == loss_name: + raise NotImplementedError + losses[f'{loss_name}{postfix}'] = l * lbd + else: + raise NotImplementedError + + def l1_loss(self, decoder_output, target): + # decoder_output : B x T x n_mel + # target : B x T x n_mel + l1_loss = F.l1_loss(decoder_output, target, reduction='none') + weights = self.weights_nonzero_speech(target) + l1_loss = (l1_loss * weights).sum() / weights.sum() + return l1_loss + + def ssim_loss(self, decoder_output, target, bias=6.0): + # decoder_output : B x T x n_mel + # target : B x T x n_mel + assert decoder_output.shape == target.shape + weights = self.weights_nonzero_speech(target) + decoder_output = decoder_output[:, None] + bias + target = target[:, None] + bias + ssim_loss = 1 - ssim(decoder_output, target, size_average=False) + ssim_loss = (ssim_loss * weights).sum() / weights.sum() + return ssim_loss + + def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None): + """ + + :param dur_pred: [B, T], float, log scale + :param mel2ph: [B, T] + :param txt_tokens: [B, T] + :param losses: + :return: + """ + B, T = txt_tokens.shape + nonpadding = (txt_tokens != 0).float() + dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding + is_sil = torch.zeros_like(txt_tokens).bool() + for p in self.sil_ph: + is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) + is_sil = is_sil.float() # [B, T_txt] + + # phone duration loss + if hparams['dur_loss'] == 'mse': + losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') + losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() + dur_pred = (dur_pred.exp() - 1).clamp(min=0) + elif hparams['dur_loss'] == 'mog': + return NotImplementedError + elif hparams['dur_loss'] == 'crf': + losses['pdur'] = -self.model.dur_predictor.crf( + dur_pred, dur_gt.long().clamp(min=0, max=31), mask=nonpadding > 0, reduction='mean') + losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur'] + + # use linear scale for sent and word duration + if hparams['lambda_word_dur'] > 0: + word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long() + word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:] + word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:] + wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') + word_nonpadding = (word_dur_g > 0).float() + wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() + losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] + if hparams['lambda_sent_dur'] > 0: + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') + losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] + + def add_pitch_loss(self, output, sample, losses): + if hparams['pitch_type'] == 'ph': + nonpadding = (sample['txt_tokens'] != 0).float() + pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss + losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'], + reduction='none') * nonpadding).sum() \ + / nonpadding.sum() * hparams['lambda_f0'] + return + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + uv = sample['uv'] + nonpadding = (mel2ph != 0).float() + if hparams['pitch_type'] == 'cwt': + cwt_spec = sample[f'cwt_spec'] + f0_mean = sample['f0_mean'] + f0_std = sample['f0_std'] + cwt_pred = output['cwt'][:, :, :10] + f0_mean_pred = output['f0_mean'] + f0_std_pred = output['f0_std'] + losses['C'] = self.cwt_loss(cwt_pred, cwt_spec) * hparams['lambda_f0'] + if hparams['use_uv']: + assert output['cwt'].shape[-1] == 11 + uv_pred = output['cwt'][:, :, -1] + losses['uv'] = (F.binary_cross_entropy_with_logits(uv_pred, uv, reduction='none') * nonpadding) \ + .sum() / nonpadding.sum() * hparams['lambda_uv'] + losses['f0_mean'] = F.l1_loss(f0_mean_pred, f0_mean) * hparams['lambda_f0'] + losses['f0_std'] = F.l1_loss(f0_std_pred, f0_std) * hparams['lambda_f0'] + if hparams['cwt_add_f0_loss']: + f0_cwt_ = self.model.cwt2f0_norm(cwt_pred, f0_mean_pred, f0_std_pred, mel2ph) + self.add_f0_loss(f0_cwt_[:, :, None], f0, uv, losses, nonpadding=nonpadding) + elif hparams['pitch_type'] == 'frame': + self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) + + def add_f0_loss(self, p_pred, f0, uv, losses, nonpadding): + assert p_pred[..., 0].shape == f0.shape + if hparams['use_uv']: + assert p_pred[..., 1].shape == uv.shape + losses['uv'] = (F.binary_cross_entropy_with_logits( + p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \ + / nonpadding.sum() * hparams['lambda_uv'] + nonpadding = nonpadding * (uv == 0).float() + + f0_pred = p_pred[:, :, 0] + if hparams['pitch_loss'] in ['l1', 'l2']: + pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss + losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \ + / nonpadding.sum() * hparams['lambda_f0'] + elif hparams['pitch_loss'] == 'ssim': + return NotImplementedError + + def cwt_loss(self, cwt_p, cwt_g): + if hparams['cwt_loss'] == 'l1': + return F.l1_loss(cwt_p, cwt_g) + if hparams['cwt_loss'] == 'l2': + return F.mse_loss(cwt_p, cwt_g) + if hparams['cwt_loss'] == 'ssim': + return self.ssim_loss(cwt_p, cwt_g, 20) + + def add_energy_loss(self, energy_pred, energy, losses): + nonpadding = (energy != 0).float() + loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum() + loss = loss * hparams['lambda_energy'] + losses['e'] = loss + + + ############ + # validation plots + ############ + def plot_mel(self, batch_idx, spec, spec_out, name=None): + spec_cat = torch.cat([spec, spec_out], -1) + name = f'mel_{batch_idx}' if name is None else name + vmin = hparams['mel_vmin'] + vmax = hparams['mel_vmax'] + self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) + + def plot_dur(self, batch_idx, sample, model_out): + T_txt = sample['txt_tokens'].shape[1] + dur_gt = mel2ph_to_dur(sample['mel2ph'], T_txt)[0] + dur_pred = self.model.dur_predictor.out2dur(model_out['dur']).float() + txt = self.phone_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) + txt = txt.split(" ") + self.logger.experiment.add_figure( + f'dur_{batch_idx}', dur_to_figure(dur_gt, dur_pred, txt), self.global_step) + + def plot_pitch(self, batch_idx, sample, model_out): + f0 = sample['f0'] + if hparams['pitch_type'] == 'ph': + mel2ph = sample['mel2ph'] + f0 = self.expand_f0_ph(f0, mel2ph) + f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph) + self.logger.experiment.add_figure( + f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step) + return + f0 = denorm_f0(f0, sample['uv'], hparams) + if hparams['pitch_type'] == 'cwt': + # cwt + cwt_out = model_out['cwt'] + cwt_spec = cwt_out[:, :, :10] + cwt = torch.cat([cwt_spec, sample['cwt_spec']], -1) + self.logger.experiment.add_figure(f'cwt_{batch_idx}', spec_to_figure(cwt[0]), self.global_step) + # f0 + f0_pred = cwt2f0(cwt_spec, model_out['f0_mean'], model_out['f0_std'], hparams['cwt_scales']) + if hparams['use_uv']: + assert cwt_out.shape[-1] == 11 + uv_pred = cwt_out[:, :, -1] > 0 + f0_pred[uv_pred > 0] = 0 + f0_cwt = denorm_f0(sample['f0_cwt'], sample['uv'], hparams) + self.logger.experiment.add_figure( + f'f0_{batch_idx}', f0_to_figure(f0[0], f0_cwt[0], f0_pred[0]), self.global_step) + elif hparams['pitch_type'] == 'frame': + # f0 + uv_pred = model_out['pitch_pred'][:, :, 1] > 0 + pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], uv_pred, hparams) + self.logger.experiment.add_figure( + f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step) + + ############ + # infer + ############ + def test_step(self, sample, batch_idx): + spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') + txt_tokens = sample['txt_tokens'] + mel2ph, uv, f0 = None, None, None + ref_mels = None + if hparams['profile_infer']: + pass + else: + if hparams['use_gt_dur']: + mel2ph = sample['mel2ph'] + if hparams['use_gt_f0']: + f0 = sample['f0'] + uv = sample['uv'] + print('Here using gt f0!!') + if hparams.get('use_midi') is not None and hparams['use_midi']: + outputs = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True, + pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) + else: + outputs = self.model( + txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True) + sample['outputs'] = self.model.out2mel(outputs['mel_out']) + sample['mel2ph_pred'] = outputs['mel2ph'] + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel + sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel + else: + sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) + sample['f0_pred'] = outputs.get('f0_denorm') + return self.after_infer(sample) + + def after_infer(self, predictions): + if self.saving_result_pool is None and not hparams['profile_infer']: + self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) + self.saving_results_futures = [] + predictions = utils.unpack_dict_to_list(predictions) + t = tqdm(predictions) + for num_predictions, prediction in enumerate(t): + for k, v in prediction.items(): + if type(v) is torch.Tensor: + prediction[k] = v.cpu().numpy() + + item_name = prediction.get('item_name') + text = prediction.get('text').replace(":", "%3A")[:80] + + # remove paddings + mel_gt = prediction["mels"] + mel_gt_mask = np.abs(mel_gt).sum(-1) > 0 + mel_gt = mel_gt[mel_gt_mask] + mel2ph_gt = prediction.get("mel2ph") + mel2ph_gt = mel2ph_gt[mel_gt_mask] if mel2ph_gt is not None else None + mel_pred = prediction["outputs"] + mel_pred_mask = np.abs(mel_pred).sum(-1) > 0 + mel_pred = mel_pred[mel_pred_mask] + mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax']) + mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax']) + + mel2ph_pred = prediction.get("mel2ph_pred") + if mel2ph_pred is not None: + if len(mel2ph_pred) > len(mel_pred_mask): + mel2ph_pred = mel2ph_pred[:len(mel_pred_mask)] + mel2ph_pred = mel2ph_pred[mel_pred_mask] + + f0_gt = prediction.get("f0") + f0_pred = prediction.get("f0_pred") + if f0_pred is not None: + f0_gt = f0_gt[mel_gt_mask] + if len(f0_pred) > len(mel_pred_mask): + f0_pred = f0_pred[:len(mel_pred_mask)] + f0_pred = f0_pred[mel_pred_mask] + + str_phs = None + if self.phone_encoder is not None and 'txt_tokens' in prediction: + str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) + gen_dir = os.path.join(hparams['work_dir'], + f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') + wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) + if not hparams['profile_infer']: + os.makedirs(gen_dir, exist_ok=True) + os.makedirs(f'{gen_dir}/wavs', exist_ok=True) + os.makedirs(f'{gen_dir}/plot', exist_ok=True) + os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True) + os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_pred, mel_pred, 'P', item_name, text, gen_dir, str_phs, mel2ph_pred, f0_gt, f0_pred])) + + if mel_gt is not None and hparams['save_gt']: + wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_gt, mel_gt, 'G', item_name, text, gen_dir, str_phs, mel2ph_gt, f0_gt, f0_pred])) + if hparams['save_f0']: + import matplotlib.pyplot as plt + # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) + f0_pred_ = f0_pred + f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) + fig = plt.figure() + plt.plot(f0_pred_, label=r'$f0_P$') + plt.plot(f0_gt_, label=r'$f0_G$') + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + # f0_midi = prediction.get("f0_midi") + # f0_midi = f0_midi[mel_gt_mask] + # plt.plot(f0_midi, label=r'$f0_M$') + pass + plt.legend() + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') + plt.close(fig) + + t.set_description( + f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") + else: + if 'gen_wav_time' not in self.stats: + self.stats['gen_wav_time'] = 0 + self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate'] + print('gen_wav_time: ', self.stats['gen_wav_time']) + + return {} + + @staticmethod + def save_result(wav_out, mel, prefix, item_name, text, gen_dir, str_phs=None, mel2ph=None, gt_f0=None, pred_f0=None): + item_name = item_name.replace('/', '-') + base_fn = f'[{item_name}][{prefix}]' + + if text is not None: + base_fn += text + base_fn += ('-' + hparams['exp_name']) + np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel) + audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], + norm=hparams['out_wav_norm']) + fig = plt.figure(figsize=(14, 10)) + spec_vmin = hparams['mel_vmin'] + spec_vmax = hparams['mel_vmax'] + heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) + fig.colorbar(heatmap) + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + gt_f0 = (gt_f0 - 100) / (800 - 100) * 80 * (gt_f0 > 0) + pred_f0 = (pred_f0 - 100) / (800 - 100) * 80 * (pred_f0 > 0) + plt.plot(pred_f0, c='white', linewidth=1, alpha=0.6) + plt.plot(gt_f0, c='red', linewidth=1, alpha=0.6) + else: + f0, _ = get_pitch(wav_out, mel, hparams) + f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0) + plt.plot(f0, c='white', linewidth=1, alpha=0.6) + if mel2ph is not None and str_phs is not None: + decoded_txt = str_phs.split(" ") + dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() + dur = [0] + list(np.cumsum(dur)) + for i in range(len(dur) - 1): + shift = (i % 20) + 1 + plt.text(dur[i], shift, decoded_txt[i]) + plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') + plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', + alpha=1, linewidth=1) + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000) + plt.close(fig) + + ############## + # utils + ############## + @staticmethod + def expand_f0_ph(f0, mel2ph): + f0 = denorm_f0(f0, None, hparams) + f0 = F.pad(f0, [1, 0]) + f0 = torch.gather(f0, 1, mel2ph) # [B, T_mel] + return f0 + + +if __name__ == '__main__': + FastSpeech2Task.start() diff --git a/NeuralSeq/tasks/tts/fs2_adv.py b/NeuralSeq/tasks/tts/fs2_adv.py new file mode 100644 index 0000000000000000000000000000000000000000..ebf48a8dd79e14fda3e8039d9ac8c00b58d3bbbb --- /dev/null +++ b/NeuralSeq/tasks/tts/fs2_adv.py @@ -0,0 +1,129 @@ +from tasks.tts.fs2 import FastSpeech2Task +from modules.syntaspeech.multi_window_disc import Discriminator +from utils.hparams import hparams +from torch import nn +import torch +import torch.optim +import torch.utils.data +import utils + + +class FastSpeech2AdvTask(FastSpeech2Task): + def build_model(self): + self.build_tts_model() + if hparams['load_ckpt'] != '': + self.load_ckpt(hparams['load_ckpt'], strict=False) + utils.print_arch(self.model, 'Generator') + self.build_disc_model() + if not hasattr(self, 'gen_params'): + self.gen_params = list(self.model.parameters()) + return self.model + + def build_disc_model(self): + disc_win_num = hparams['disc_win_num'] + h = hparams['mel_disc_hidden_size'] + self.mel_disc = Discriminator( + time_lengths=[32, 64, 128][:disc_win_num], + freq_length=80, hidden_size=h, kernel=(3, 3) + ) + self.disc_params = list(self.mel_disc.parameters()) + utils.print_arch(self.mel_disc, model_name='Mel Disc') + + def _training_step(self, sample, batch_idx, optimizer_idx): + log_outputs = {} + loss_weights = {} + disc_start = hparams['mel_gan'] and self.global_step >= hparams["disc_start_steps"] and \ + hparams['lambda_mel_adv'] > 0 + if optimizer_idx == 0: + ####################### + # Generator # + ####################### + log_outputs, model_out = self.run_model(self.model, sample, return_output=True) + self.model_out = {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)} + if disc_start: + self.disc_cond = disc_cond = self.model_out['decoder_inp'].detach() \ + if hparams['use_cond_disc'] else None + if hparams['mel_loss_no_noise']: + self.add_mel_loss(model_out['mel_out_nonoise'], sample['mels'], log_outputs) + mel_p = model_out['mel_out'] + if hasattr(self.model, 'out2mel'): + mel_p = self.model.out2mel(mel_p) + o_ = self.mel_disc(mel_p, disc_cond) + p_, pc_ = o_['y'], o_['y_c'] + + if p_ is not None: + log_outputs['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size())) + loss_weights['a'] = hparams['lambda_mel_adv'] + if pc_ is not None: + log_outputs['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size())) + loss_weights['ac'] = hparams['lambda_mel_adv'] + else: + ####################### + # Discriminator # + ####################### + if disc_start and self.global_step % hparams['disc_interval'] == 0: + if hparams['rerun_gen']: + with torch.no_grad(): + _, model_out = self.run_model(self.model, sample, return_output=True) + else: + model_out = self.model_out + mel_g = sample['mels'] + mel_p = model_out['mel_out'] + if hasattr(self.model, 'out2mel'): + mel_p = self.model.out2mel(mel_p) + + o = self.mel_disc(mel_g, self.disc_cond) + p, pc = o['y'], o['y_c'] + o_ = self.mel_disc(mel_p, self.disc_cond) + p_, pc_ = o_['y'], o_['y_c'] + + if p_ is not None: + log_outputs["r"] = self.mse_loss_fn(p, p.new_ones(p.size())) + log_outputs["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size())) + + if pc_ is not None: + log_outputs["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size())) + log_outputs["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size())) + + if len(log_outputs) == 0: + return None + total_loss = sum([loss_weights.get(k, 1) * v for k, v in log_outputs.items()]) + + log_outputs['bs'] = sample['mels'].shape[0] + return total_loss, log_outputs + + def configure_optimizers(self): + if not hasattr(self, 'gen_params'): + self.gen_params = list(self.model.parameters()) + optimizer_gen = torch.optim.AdamW( + self.gen_params, + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + optimizer_disc = torch.optim.AdamW( + self.disc_params, + lr=hparams['disc_lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None + self.scheduler = self.build_scheduler({'gen': optimizer_gen, 'disc': optimizer_disc}) + return [optimizer_gen, optimizer_disc] + + def build_scheduler(self, optimizer): + return { + "gen": super().build_scheduler(optimizer['gen']), + "disc": torch.optim.lr_scheduler.StepLR( + optimizer=optimizer["disc"], + **hparams["discriminator_scheduler_params"]) if optimizer["disc"] is not None else None, + } + + def on_before_optimization(self, opt_idx): + if opt_idx == 0: + nn.utils.clip_grad_norm_(self.gen_params, hparams['generator_grad_norm']) + else: + nn.utils.clip_grad_norm_(self.disc_params, hparams["discriminator_grad_norm"]) + + def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx): + if optimizer_idx == 0: + self.scheduler['gen'].step(self.global_step) + else: + self.scheduler['disc'].step(max(self.global_step - hparams["disc_start_steps"], 1)) diff --git a/NeuralSeq/tasks/tts/fs2_utils.py b/NeuralSeq/tasks/tts/fs2_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..092550863d2fd72f008cc790bc6d950340e68182 --- /dev/null +++ b/NeuralSeq/tasks/tts/fs2_utils.py @@ -0,0 +1,173 @@ +import matplotlib + +matplotlib.use('Agg') + +import glob +import importlib +from utils.cwt import get_lf0_cwt +import os +import torch.optim +import torch.utils.data +from utils.indexed_datasets import IndexedDataset +from utils.pitch_utils import norm_interp_f0 +import numpy as np +from tasks.base_task import BaseDataset +import torch +import torch.optim +import torch.utils.data +import utils +import torch.distributions +from utils.hparams import hparams + + +class FastSpeechDataset(BaseDataset): + def __init__(self, prefix, shuffle=False): + super().__init__(shuffle) + self.data_dir = hparams['binary_data_dir'] + self.prefix = prefix + self.hparams = hparams + self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') + self.indexed_ds = None + # self.name2spk_id={} + + # pitch stats + f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy' + if os.path.exists(f0_stats_fn): + hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn) + hparams['f0_mean'] = float(hparams['f0_mean']) + hparams['f0_std'] = float(hparams['f0_std']) + else: + hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None + + if prefix == 'test': + if hparams['test_input_dir'] != '': + self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) + else: + if hparams['num_test_samples'] > 0: + self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids'] + self.sizes = [self.sizes[i] for i in self.avail_idxs] + + if hparams['pitch_type'] == 'cwt': + _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) + + def _get_item(self, index): + if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: + index = self.avail_idxs[index] + if self.indexed_ds is None: + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + return self.indexed_ds[index] + + def __getitem__(self, index): + hparams = self.hparams + item = self._get_item(index) + max_frames = hparams['max_frames'] + spec = torch.Tensor(item['mel'])[:max_frames] + energy = (spec.exp() ** 2).sum(-1).sqrt() + mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None + f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) + phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) + pitch = torch.LongTensor(item.get("pitch"))[:max_frames] + # print(item.keys(), item['mel'].shape, spec.shape) + sample = { + "id": index, + "item_name": item['item_name'], + "text": item['txt'], + "txt_token": phone, + "mel": spec, + "pitch": pitch, + "energy": energy, + "f0": f0, + "uv": uv, + "mel2ph": mel2ph, + "mel_nonpadding": spec.abs().sum(-1) > 0, + } + if self.hparams['use_spk_embed']: + sample["spk_embed"] = torch.Tensor(item['spk_embed']) + if self.hparams['use_spk_id']: + sample["spk_id"] = item['spk_id'] + # sample['spk_id'] = 0 + # for key in self.name2spk_id.keys(): + # if key in item['item_name']: + # sample['spk_id'] = self.name2spk_id[key] + # break + if self.hparams['pitch_type'] == 'cwt': + cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames] + f0_mean = item.get('f0_mean', item.get('cwt_mean')) + f0_std = item.get('f0_std', item.get('cwt_std')) + sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std}) + elif self.hparams['pitch_type'] == 'ph': + f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0) + f0_phlevel_num = torch.zeros_like(phone).float().scatter_add( + 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1) + sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num + return sample + + def collater(self, samples): + if len(samples) == 0: + return {} + id = torch.LongTensor([s['id'] for s in samples]) + item_names = [s['item_name'] for s in samples] + text = [s['text'] for s in samples] + txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) + f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) + pitch = utils.collate_1d([s['pitch'] for s in samples]) + uv = utils.collate_1d([s['uv'] for s in samples]) + energy = utils.collate_1d([s['energy'] for s in samples], 0.0) + mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ + if samples[0]['mel2ph'] is not None else None + mels = utils.collate_2d([s['mel'] for s in samples], 0.0) + txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) + mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) + + batch = { + 'id': id, + 'item_name': item_names, + 'nsamples': len(samples), + 'text': text, + 'txt_tokens': txt_tokens, + 'txt_lengths': txt_lengths, + 'mels': mels, + 'mel_lengths': mel_lengths, + 'mel2ph': mel2ph, + 'energy': energy, + 'pitch': pitch, + 'f0': f0, + 'uv': uv, + } + + if self.hparams['use_spk_embed']: + spk_embed = torch.stack([s['spk_embed'] for s in samples]) + batch['spk_embed'] = spk_embed + if self.hparams['use_spk_id']: + spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) + batch['spk_ids'] = spk_ids + if self.hparams['pitch_type'] == 'cwt': + cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples]) + f0_mean = torch.Tensor([s['f0_mean'] for s in samples]) + f0_std = torch.Tensor([s['f0_std'] for s in samples]) + batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std}) + elif self.hparams['pitch_type'] == 'ph': + batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples]) + + return batch + + def load_test_inputs(self, test_input_dir, spk_id=0): + inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3') + sizes = [] + items = [] + + binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + binarization_args = hparams['binarization_args'] + + for wav_fn in inp_wav_paths: + item_name = os.path.basename(wav_fn) + ph = txt = tg_fn = '' + wav_fn = wav_fn + encoder = None + item = binarizer_cls.process_item(item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args) + items.append(item) + sizes.append(item['len']) + return items, sizes diff --git a/NeuralSeq/tasks/tts/pe.py b/NeuralSeq/tasks/tts/pe.py new file mode 100644 index 0000000000000000000000000000000000000000..3880c80d0820c36e044c00bd38a07fd3cce73323 --- /dev/null +++ b/NeuralSeq/tasks/tts/pe.py @@ -0,0 +1,155 @@ +import matplotlib +matplotlib.use('Agg') + +import torch +import numpy as np +import os + +from tasks.base_task import BaseDataset +from tasks.tts.fs2 import FastSpeech2Task +from modules.fastspeech.pe import PitchExtractor +import utils +from utils.indexed_datasets import IndexedDataset +from utils.hparams import hparams +from utils.plot import f0_to_figure +from utils.pitch_utils import norm_interp_f0, denorm_f0 + + +class PeDataset(BaseDataset): + def __init__(self, prefix, shuffle=False): + super().__init__(shuffle) + self.data_dir = hparams['binary_data_dir'] + self.prefix = prefix + self.hparams = hparams + self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') + self.indexed_ds = None + + # pitch stats + f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy' + if os.path.exists(f0_stats_fn): + hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn) + hparams['f0_mean'] = float(hparams['f0_mean']) + hparams['f0_std'] = float(hparams['f0_std']) + else: + hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None + + if prefix == 'test': + if hparams['num_test_samples'] > 0: + self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids'] + self.sizes = [self.sizes[i] for i in self.avail_idxs] + + def _get_item(self, index): + if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: + index = self.avail_idxs[index] + if self.indexed_ds is None: + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + return self.indexed_ds[index] + + def __getitem__(self, index): + hparams = self.hparams + item = self._get_item(index) + max_frames = hparams['max_frames'] + spec = torch.Tensor(item['mel'])[:max_frames] + # mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None + f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) + pitch = torch.LongTensor(item.get("pitch"))[:max_frames] + # print(item.keys(), item['mel'].shape, spec.shape) + sample = { + "id": index, + "item_name": item['item_name'], + "text": item['txt'], + "mel": spec, + "pitch": pitch, + "f0": f0, + "uv": uv, + # "mel2ph": mel2ph, + # "mel_nonpadding": spec.abs().sum(-1) > 0, + } + return sample + + def collater(self, samples): + if len(samples) == 0: + return {} + id = torch.LongTensor([s['id'] for s in samples]) + item_names = [s['item_name'] for s in samples] + text = [s['text'] for s in samples] + f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) + pitch = utils.collate_1d([s['pitch'] for s in samples]) + uv = utils.collate_1d([s['uv'] for s in samples]) + mels = utils.collate_2d([s['mel'] for s in samples], 0.0) + mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) + # mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ + # if samples[0]['mel2ph'] is not None else None + # mel_nonpaddings = utils.collate_1d([s['mel_nonpadding'].float() for s in samples], 0.0) + + batch = { + 'id': id, + 'item_name': item_names, + 'nsamples': len(samples), + 'text': text, + 'mels': mels, + 'mel_lengths': mel_lengths, + 'pitch': pitch, + # 'mel2ph': mel2ph, + # 'mel_nonpaddings': mel_nonpaddings, + 'f0': f0, + 'uv': uv, + } + return batch + + +class PitchExtractionTask(FastSpeech2Task): + def __init__(self): + super().__init__() + self.dataset_cls = PeDataset + + def build_tts_model(self): + self.model = PitchExtractor(conv_layers=hparams['pitch_extractor_conv_layers']) + + # def build_scheduler(self, optimizer): + # return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5) + def _training_step(self, sample, batch_idx, _): + loss_output = self.run_model(self.model, sample) + total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) + loss_output['batch_size'] = sample['mels'].size()[0] + return total_loss, loss_output + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=True) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = utils.tensors_to_scalars(outputs) + if batch_idx < hparams['num_valid_plots']: + self.plot_pitch(batch_idx, model_out, sample) + return outputs + + def run_model(self, model, sample, return_output=False, infer=False): + f0 = sample['f0'] + uv = sample['uv'] + output = model(sample['mels']) + losses = {} + self.add_pitch_loss(output, sample, losses) + if not return_output: + return losses + else: + return losses, output + + def plot_pitch(self, batch_idx, model_out, sample): + gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) + self.logger.experiment.add_figure( + f'f0_{batch_idx}', + f0_to_figure(gt_f0[0], None, model_out['f0_denorm_pred'][0]), + self.global_step) + + def add_pitch_loss(self, output, sample, losses): + # mel2ph = sample['mel2ph'] # [B, T_s] + mel = sample['mels'] + f0 = sample['f0'] + uv = sample['uv'] + # nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'frame' \ + # else (sample['txt_tokens'] != 0).float() + nonpadding = (mel.abs().sum(-1) > 0).float() # sample['mel_nonpaddings'] + # print(nonpadding[0][-8:], nonpadding.shape) + self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) \ No newline at end of file diff --git a/NeuralSeq/tasks/tts/ps.py b/NeuralSeq/tasks/tts/ps.py new file mode 100644 index 0000000000000000000000000000000000000000..5222c102e6790605545e5adaff8edbc88e80e921 --- /dev/null +++ b/NeuralSeq/tasks/tts/ps.py @@ -0,0 +1,194 @@ +import os +import torch +import torch.nn.functional as F +from torch import nn + +from modules.portaspeech.portaspeech import PortaSpeech +from tasks.tts.fs2 import FastSpeech2Task +from utils.tts_utils import mel2token_to_dur +from utils.hparams import hparams +from utils.tts_utils import get_focus_rate, get_phone_coverage_rate, get_diagonal_focus_rate +from utils import num_params +import numpy as np + +from utils.plot import spec_to_figure +from data_gen.tts.data_gen_utils import build_token_encoder + + +class PortaSpeechTask(FastSpeech2Task): + def __init__(self): + super().__init__() + data_dir = hparams['binary_data_dir'] + self.word_encoder = build_token_encoder(f'{data_dir}/word_set.json') + + def build_tts_model(self): + ph_dict_size = len(self.token_encoder) + word_dict_size = len(self.word_encoder) + self.model = PortaSpeech(ph_dict_size, word_dict_size, hparams) + + def on_train_start(self): + super().on_train_start() + for n, m in self.model.named_children(): + num_params(m, model_name=n) + if hasattr(self.model, 'fvae'): + for n, m in self.model.fvae.named_children(): + num_params(m, model_name=f'fvae.{n}') + + def run_model(self, sample, infer=False, *args, **kwargs): + txt_tokens = sample['txt_tokens'] + word_tokens = sample['word_tokens'] + spk_embed = sample.get('spk_embed') + spk_id = sample.get('spk_ids') + if not infer: + output = self.model(txt_tokens, word_tokens, + ph2word=sample['ph2word'], + mel2word=sample['mel2word'], + mel2ph=sample['mel2ph'], + word_len=sample['word_lengths'].max(), + tgt_mels=sample['mels'], + pitch=sample.get('pitch'), + spk_embed=spk_embed, + spk_id=spk_id, + infer=False, + global_step=self.global_step) + losses = {} + losses['kl_v'] = output['kl'].detach() + losses_kl = output['kl'] + losses_kl = torch.clamp(losses_kl, min=hparams['kl_min']) + losses_kl = min(self.global_step / hparams['kl_start_steps'], 1) * losses_kl + losses_kl = losses_kl * hparams['lambda_kl'] + losses['kl'] = losses_kl + self.add_mel_loss(output['mel_out'], sample['mels'], losses) + if hparams['dur_level'] == 'word': + self.add_dur_loss( + output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) + self.get_attn_stats(output['attn'], sample, losses) + else: + super(PortaSpeechTask, self).add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) + return losses, output + else: + use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) + output = self.model( + txt_tokens, word_tokens, + ph2word=sample['ph2word'], + word_len=sample['word_lengths'].max(), + pitch=sample.get('pitch'), + mel2ph=sample['mel2ph'] if use_gt_dur else None, + mel2word=sample['mel2word'] if use_gt_dur else None, + tgt_mels=sample['mels'], + infer=True, + spk_embed=spk_embed, + spk_id=spk_id, + ) + return output + + def add_dur_loss(self, dur_pred, mel2token, word_len, txt_tokens, losses=None): + T = word_len.max() + dur_gt = mel2token_to_dur(mel2token, T).float() + nonpadding = (torch.arange(T).to(dur_pred.device)[None, :] < word_len[:, None]).float() + dur_pred = dur_pred * nonpadding + dur_gt = dur_gt * nonpadding + wdur = F.l1_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none') + wdur = (wdur * nonpadding).sum() / nonpadding.sum() + if hparams['lambda_word_dur'] > 0: + losses['wdur'] = wdur * hparams['lambda_word_dur'] + if hparams['lambda_sent_dur'] > 0: + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + sdur_loss = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean') + losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] + + def validation_step(self, sample, batch_idx): + return super().validation_step(sample, batch_idx) + + def save_valid_result(self, sample, batch_idx, model_out): + super(PortaSpeechTask, self).save_valid_result(sample, batch_idx, model_out) + if self.global_step > 0 and hparams['dur_level'] == 'word': + self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step) + + def get_attn_stats(self, attn, sample, logging_outputs, prefix=''): + # diagonal_focus_rate + txt_lengths = sample['txt_lengths'].float() + mel_lengths = sample['mel_lengths'].float() + src_padding_mask = sample['txt_tokens'].eq(0) + target_padding_mask = sample['mels'].abs().sum(-1).eq(0) + src_seg_mask = sample['txt_tokens'].eq(self.seg_idx) + attn_ks = txt_lengths.float() / mel_lengths.float() + + focus_rate = get_focus_rate(attn, src_padding_mask, target_padding_mask).mean().data + phone_coverage_rate = get_phone_coverage_rate( + attn, src_padding_mask, src_seg_mask, target_padding_mask).mean() + diagonal_focus_rate, diag_mask = get_diagonal_focus_rate( + attn, attn_ks, mel_lengths, src_padding_mask, target_padding_mask) + logging_outputs[f'{prefix}fr'] = focus_rate.mean().data + logging_outputs[f'{prefix}pcr'] = phone_coverage_rate.mean().data + logging_outputs[f'{prefix}dfr'] = diagonal_focus_rate.mean().data + + def get_plot_dur_info(self, sample, model_out): + if hparams['dur_level'] == 'word': + T_txt = sample['word_lengths'].max() + dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0] + dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt + txt = sample['ph_words'][0].split(" ") + else: + T_txt = sample['txt_tokens'].shape[1] + dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0] + dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt + txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) + txt = txt.split(" ") + return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt} + + def build_optimizer(self, model): + self.optimizer = torch.optim.AdamW( + self.model.parameters(), + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + return self.optimizer + + def build_scheduler(self, optimizer): + return FastSpeechTask.build_scheduler(self, optimizer) + + ############ + # infer + ############ + def test_start(self): + super().test_start() + if hparams.get('save_attn', False): + os.makedirs(f'{self.gen_dir}/attn', exist_ok=True) + self.model.store_inverse_all() + + def test_step(self, sample, batch_idx): + assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference' + outputs = self.run_model(sample, infer=True) + text = sample['text'][0] + item_name = sample['item_name'][0] + tokens = sample['txt_tokens'][0].cpu().numpy() + mel_gt = sample['mels'][0].cpu().numpy() + mel_pred = outputs['mel_out'][0].cpu().numpy() + mel2ph = sample['mel2ph'][0].cpu().numpy() + mel2ph_pred = None + str_phs = self.token_encoder.decode(tokens, strip_padding=True) + base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]' + if text is not None: + base_fn += text.replace(":", "$3A")[:80] + base_fn = base_fn.replace(' ', '_') + gen_dir = self.gen_dir + wav_pred = self.vocoder.spec2wav(mel_pred) + self.saving_result_pool.add_job(self.save_result, args=[ + wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred]) + if hparams['save_gt']: + wav_gt = self.vocoder.spec2wav(mel_gt) + self.saving_result_pool.add_job(self.save_result, args=[ + wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph]) + if hparams.get('save_attn', False): + attn = outputs['attn'][0].cpu().numpy() + np.save(f'{gen_dir}/attn/{item_name}.npy', attn) + print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") + return { + 'item_name': item_name, + 'text': text, + 'ph_tokens': self.token_encoder.decode(tokens.tolist()), + 'wav_fn_pred': base_fn % 'P', + 'wav_fn_gt': base_fn % 'G', + } diff --git a/NeuralSeq/tasks/tts/ps_adv.py b/NeuralSeq/tasks/tts/ps_adv.py new file mode 100644 index 0000000000000000000000000000000000000000..fbc1e5133ddf26f2dfac598028b8e3db01ec638e --- /dev/null +++ b/NeuralSeq/tasks/tts/ps_adv.py @@ -0,0 +1,372 @@ +import os +import torch +import torch.nn.functional as F +import torch.nn as nn +import numpy as np + +from modules.portaspeech.portaspeech import PortaSpeech +from modules.syntaspeech.multi_window_disc import Discriminator +from tasks.tts.fs2 import FastSpeech2Task +from utils.hparams import hparams +from utils.tts_utils import get_focus_rate, get_phone_coverage_rate, get_diagonal_focus_rate, mel2token_to_dur +from utils import num_params, tensors_to_scalars +from utils.pitch_utils import denorm_f0, norm_f0 +from data_gen.tts.data_gen_utils import get_pitch +from utils.dtw import dtw as DTW + +from utils.plot import spec_to_figure +from utils.text.text_encoder import build_token_encoder + + +class PortaSpeechAdvTask(FastSpeech2Task): + def __init__(self): + super().__init__() + data_dir = hparams['binary_data_dir'] + self.word_encoder = build_token_encoder(f'{data_dir}/word_set.json') + self.build_disc_model() + self.mse_loss_fn = torch.nn.MSELoss() + + def build_tts_model(self): + ph_dict_size = len(self.token_encoder) + word_dict_size = len(self.word_encoder) + self.model = PortaSpeech(ph_dict_size, word_dict_size, hparams) + + self.gen_params = [p for p in self.model.parameters() if p.requires_grad] + self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)] + self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)] + self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)] + self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ] + + self.use_bert = True if len(self.bert_params) > 0 else False + + def build_disc_model(self): + disc_win_num = hparams['disc_win_num'] + h = hparams['mel_disc_hidden_size'] + self.mel_disc = Discriminator( + time_lengths=[32, 64, 128][:disc_win_num], + freq_length=80, hidden_size=h, kernel=(3, 3) + ) + self.disc_params = list(self.mel_disc.parameters()) + + def on_train_start(self): + super().on_train_start() + for n, m in self.model.named_children(): + num_params(m, model_name=n) + if hasattr(self.model, 'fvae'): + for n, m in self.model.fvae.named_children(): + num_params(m, model_name=f'fvae.{n}') + + def _training_step(self, sample, batch_idx, optimizer_idx): + loss_output = {} + loss_weights = {} + disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0 + if optimizer_idx == 0: + ####################### + # Generator # + ####################### + loss_output, model_out = self.run_model(sample, infer=False) + self.model_out_gt = self.model_out = \ + {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)} + if disc_start: + mel_p = model_out['mel_out'] + if hasattr(self.model, 'out2mel'): + mel_p = self.model.out2mel(mel_p) + o_ = self.mel_disc(mel_p) + p_, pc_ = o_['y'], o_['y_c'] + if p_ is not None: + loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size())) + loss_weights['a'] = hparams['lambda_mel_adv'] + if pc_ is not None: + loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size())) + loss_weights['ac'] = hparams['lambda_mel_adv'] + else: + ####################### + # Discriminator # + ####################### + if disc_start and self.global_step % hparams['disc_interval'] == 0: + model_out = self.model_out_gt + mel_g = sample['mels'] + mel_p = model_out['mel_out'] + o = self.mel_disc(mel_g) + p, pc = o['y'], o['y_c'] + o_ = self.mel_disc(mel_p) + p_, pc_ = o_['y'], o_['y_c'] + if p_ is not None: + loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size())) + loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size())) + if pc_ is not None: + loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size())) + loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size())) + total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad]) + loss_output['batch_size'] = sample['txt_tokens'].size()[0] + return total_loss, loss_output + + def run_model(self, sample, infer=False, *args, **kwargs): + txt_tokens = sample['txt_tokens'] + word_tokens = sample['word_tokens'] + spk_embed = sample.get('spk_embed') + spk_id = sample.get('spk_ids') + if not infer: + output = self.model(txt_tokens, word_tokens, + ph2word=sample['ph2word'], + mel2word=sample['mel2word'], + mel2ph=sample['mel2ph'], + word_len=sample['word_lengths'].max(), + tgt_mels=sample['mels'], + pitch=sample.get('pitch'), + spk_embed=spk_embed, + spk_id=spk_id, + infer=False, + global_step=self.global_step, + graph_lst=sample['graph_lst'], + etypes_lst=sample['etypes_lst'], + bert_feats=sample.get("bert_feats"), + cl_feats=sample.get("cl_feats") + ) + losses = {} + losses['kl_v'] = output['kl'].detach() + losses_kl = output['kl'] + losses_kl = torch.clamp(losses_kl, min=hparams['kl_min']) + losses_kl = min(self.global_step / hparams['kl_start_steps'], 1) * losses_kl + losses_kl = losses_kl * hparams['lambda_kl'] + losses['kl'] = losses_kl + + self.add_mel_loss(output['mel_out'], sample['mels'], losses) + if hparams['dur_level'] == 'word': + self.add_dur_loss( + output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) + self.get_attn_stats(output['attn'], sample, losses) + else: + super(PortaSpeechAdvTask, self).add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) + return losses, output + else: + use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) + output = self.model( + txt_tokens, word_tokens, + ph2word=sample['ph2word'], + word_len=sample['word_lengths'].max(), + pitch=sample.get('pitch'), + mel2ph=sample['mel2ph'] if use_gt_dur else None, + mel2word=sample['mel2word'] if use_gt_dur else None, + tgt_mels=sample['mels'], + infer=True, + spk_embed=spk_embed, + spk_id=spk_id, + graph_lst=sample['graph_lst'], + etypes_lst=sample['etypes_lst'], + bert_feats=sample.get("bert_feats"), + cl_feats=sample.get("cl_feats") + ) + return output + + def add_dur_loss(self, dur_pred, mel2token, word_len, txt_tokens, losses=None): + T = word_len.max() + dur_gt = mel2token_to_dur(mel2token, T).float() + nonpadding = (torch.arange(T).to(dur_pred.device)[None, :] < word_len[:, None]).float() + dur_pred = dur_pred * nonpadding + dur_gt = dur_gt * nonpadding + wdur = F.l1_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none') + wdur = (wdur * nonpadding).sum() / nonpadding.sum() + + if hparams['lambda_word_dur'] > 0: + losses['wdur'] = wdur * hparams['lambda_word_dur'] + if hparams['lambda_sent_dur'] > 0: + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + sdur_loss = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean') + losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] + + with torch.no_grad(): + # calculate word-level abs_dur_error in micro-second + abs_word_dur_error = F.l1_loss(dur_pred , dur_gt, reduction='none') + abs_word_dur_error = (abs_word_dur_error * nonpadding).sum() / nonpadding.sum() + abs_word_dur_error = abs_word_dur_error * hparams['hop_size'] / hparams['audio_sample_rate'] * 1000 + losses['abs_word_dur_error'] = abs_word_dur_error + # calculate word-level abs_dur_error in second + sent_dur_p = dur_pred.sum(-1) + sent_dur_g = dur_gt.sum(-1) + abs_sent_dur_error = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean').mean() + abs_sent_dur_error = abs_sent_dur_error * hparams['hop_size'] / hparams['audio_sample_rate'] + losses['abs_sent_dur_error'] = abs_sent_dur_error + + def validation_step(self, sample, batch_idx): + outputs = {} + outputs['losses'] = {} + outputs['losses'], model_out = self.run_model(sample) + outputs['total_loss'] = sum(outputs['losses'].values()) + outputs['nsamples'] = sample['nsamples'] + outputs = tensors_to_scalars(outputs) + if self.global_step % hparams['valid_infer_interval'] == 0 \ + and batch_idx < hparams['num_valid_plots']: + valid_results = self.save_valid_result(sample, batch_idx, model_out) + wav_gt = valid_results['wav_gt'] + mel_gt = valid_results['mel_gt'] + wav_pred = valid_results['wav_pred'] + mel_pred = valid_results['mel_pred'] + f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) + f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) + manhattan_distance = lambda x, y: np.abs(x - y) + dist, cost, acc, path = DTW(f0_pred_, f0_gt_, manhattan_distance) + outputs['losses']['f0_dtw'] = dist / len(f0_gt_) + return outputs + + def save_valid_result(self, sample, batch_idx, model_out): + sr = hparams['audio_sample_rate'] + f0_gt = None + mel_out = model_out['mel_out'] + if sample.get('f0') is not None: + f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu()) + self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt) + + # if self.global_step > 0: + wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt) + self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr) + # with gt duration + model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True) + dur_info = self.get_plot_dur_info(sample, model_out) + del dur_info['dur_pred'] + wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) + self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}', + dur_info=dur_info, f0s=f0_gt) + + # with pred duration + if not hparams['use_gt_dur']: + model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False) + dur_info = self.get_plot_dur_info(sample, model_out) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}', + dur_info=dur_info, f0s=f0_gt) + wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) + self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr) + # gt wav + mel_gt = sample['mels'][0].cpu() + wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) + if self.global_step <= hparams['valid_infer_interval']: + self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr) + + # add attn plot + if self.global_step > 0 and hparams['dur_level'] == 'word': + self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step) + + return {'wav_gt': wav_gt, 'wav_pred': wav_pred, 'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()} + + def get_attn_stats(self, attn, sample, logging_outputs, prefix=''): + # diagonal_focus_rate + txt_lengths = sample['txt_lengths'].float() + mel_lengths = sample['mel_lengths'].float() + src_padding_mask = sample['txt_tokens'].eq(0) + target_padding_mask = sample['mels'].abs().sum(-1).eq(0) + src_seg_mask = sample['txt_tokens'].eq(self.seg_idx) + attn_ks = txt_lengths.float() / mel_lengths.float() + + focus_rate = get_focus_rate(attn, src_padding_mask, target_padding_mask).mean().data + phone_coverage_rate = get_phone_coverage_rate( + attn, src_padding_mask, src_seg_mask, target_padding_mask).mean() + diagonal_focus_rate, diag_mask = get_diagonal_focus_rate( + attn, attn_ks, mel_lengths, src_padding_mask, target_padding_mask) + logging_outputs[f'{prefix}fr'] = focus_rate.mean().data + logging_outputs[f'{prefix}pcr'] = phone_coverage_rate.mean().data + logging_outputs[f'{prefix}dfr'] = diagonal_focus_rate.mean().data + + def get_plot_dur_info(self, sample, model_out): + if hparams['dur_level'] == 'word': + T_txt = sample['word_lengths'].max() + dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0] + dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt + txt = sample['ph_words'][0].split(" ") + else: + T_txt = sample['txt_tokens'].shape[1] + dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0] + dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt + txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) + txt = txt.split(" ") + return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt} + + def build_optimizer(self, model): + + optimizer_gen = torch.optim.AdamW( + self.gen_params, + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + + optimizer_disc = torch.optim.AdamW( + self.disc_params, + lr=hparams['disc_lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None + + return [optimizer_gen, optimizer_disc] + + def build_scheduler(self, optimizer): + return [ + FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler + torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler + **hparams["discriminator_scheduler_params"]), + ] + + def on_before_optimization(self, opt_idx): + if opt_idx == 0: + nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm']) + if self.use_bert: + nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm']) + nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm']) + else: + nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm']) + else: + nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"]) + + def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx): + if self.scheduler is not None: + self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches']) + self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches']) + + ############ + # infer + ############ + def test_start(self): + super().test_start() + if hparams.get('save_attn', False): + os.makedirs(f'{self.gen_dir}/attn', exist_ok=True) + self.model.store_inverse_all() + + def test_step(self, sample, batch_idx): + assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference' + outputs = self.run_model(sample, infer=True) + text = sample['text'][0] + item_name = sample['item_name'][0] + tokens = sample['txt_tokens'][0].cpu().numpy() + mel_gt = sample['mels'][0].cpu().numpy() + mel_pred = outputs['mel_out'][0].cpu().numpy() + mel2ph = sample['mel2ph'][0].cpu().numpy() + mel2ph_pred = None + str_phs = self.token_encoder.decode(tokens, strip_padding=True) + base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]' + if text is not None: + base_fn += text.replace(":", "$3A")[:80] + base_fn = base_fn.replace(' ', '_') + gen_dir = self.gen_dir + wav_pred = self.vocoder.spec2wav(mel_pred) + self.saving_result_pool.add_job(self.save_result, args=[ + wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred]) + if hparams['save_gt']: + wav_gt = self.vocoder.spec2wav(mel_gt) + self.saving_result_pool.add_job(self.save_result, args=[ + wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph]) + if hparams.get('save_attn', False): + attn = outputs['attn'][0].cpu().numpy() + np.save(f'{gen_dir}/attn/{item_name}.npy', attn) + # save f0 for pitch dtw + f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) + f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) + np.save(f'{gen_dir}/f0/{item_name}.npy', f0_pred_) + np.save(f'{gen_dir}/f0/{item_name}_gt.npy', f0_gt_) + + print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") + return { + 'item_name': item_name, + 'text': text, + 'ph_tokens': self.token_encoder.decode(tokens.tolist()), + 'wav_fn_pred': base_fn % 'P', + 'wav_fn_gt': base_fn % 'G', + } diff --git a/NeuralSeq/tasks/tts/ps_flow.py b/NeuralSeq/tasks/tts/ps_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..311c1cbf27dd90ac0bd929e2a5d17c928e2dce94 --- /dev/null +++ b/NeuralSeq/tasks/tts/ps_flow.py @@ -0,0 +1,135 @@ +import torch +from modules.portaspeech.portaspeech_flow import PortaSpeechFlow +from tasks.tts.fs2 import FastSpeech2Task +from tasks.tts.ps import PortaSpeechTask +from utils.pitch_utils import denorm_f0 +from utils.hparams import hparams + + +class PortaSpeechFlowTask(PortaSpeechTask): + def __init__(self): + super().__init__() + self.training_post_glow = False + + def build_tts_model(self): + ph_dict_size = len(self.token_encoder) + word_dict_size = len(self.word_encoder) + self.model = PortaSpeechFlow(ph_dict_size, word_dict_size, hparams) + + def _training_step(self, sample, batch_idx, opt_idx): + self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \ + and hparams['use_post_flow'] + if hparams['two_stage'] and \ + ((opt_idx == 0 and self.training_post_glow) or (opt_idx == 1 and not self.training_post_glow)): + return None + loss_output, _ = self.run_model(sample) + total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) + loss_output['batch_size'] = sample['txt_tokens'].size()[0] + if 'postflow' in loss_output and loss_output['postflow'] is None: + return None + return total_loss, loss_output + + def run_model(self, sample, infer=False, *args, **kwargs): + if not infer: + training_post_glow = self.training_post_glow + spk_embed = sample.get('spk_embed') + spk_id = sample.get('spk_ids') + output = self.model(sample['txt_tokens'], + sample['word_tokens'], + ph2word=sample['ph2word'], + mel2word=sample['mel2word'], + mel2ph=sample['mel2ph'], + word_len=sample['word_lengths'].max(), + tgt_mels=sample['mels'], + pitch=sample.get('pitch'), + spk_embed=spk_embed, + spk_id=spk_id, + infer=False, + forward_post_glow=training_post_glow, + two_stage=hparams['two_stage'], + global_step=self.global_step, + bert_feats=sample.get('bert_feats')) + losses = {} + self.add_mel_loss(output['mel_out'], sample['mels'], losses) + if (training_post_glow or not hparams['two_stage']) and hparams['use_post_flow']: + losses['postflow'] = output['postflow'] + losses['l1'] = losses['l1'].detach() + losses['ssim'] = losses['ssim'].detach() + if not training_post_glow or not hparams['two_stage'] or not self.training: + losses['kl'] = output['kl'] + if self.global_step < hparams['kl_start_steps']: + losses['kl'] = losses['kl'].detach() + else: + losses['kl'] = torch.clamp(losses['kl'], min=hparams['kl_min']) + losses['kl'] = losses['kl'] * hparams['lambda_kl'] + if hparams['dur_level'] == 'word': + self.add_dur_loss( + output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses) + self.get_attn_stats(output['attn'], sample, losses) + else: + super().add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses) + return losses, output + else: + use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) + forward_post_glow = self.global_step >= hparams['post_glow_training_start'] + 1000 \ + and hparams['use_post_flow'] + spk_embed = sample.get('spk_embed') + spk_id = sample.get('spk_ids') + output = self.model( + sample['txt_tokens'], + sample['word_tokens'], + ph2word=sample['ph2word'], + word_len=sample['word_lengths'].max(), + pitch=sample.get('pitch'), + mel2ph=sample['mel2ph'] if use_gt_dur else None, + mel2word=sample['mel2word'] if hparams['profile_infer'] or hparams['use_gt_dur'] else None, + infer=True, + forward_post_glow=forward_post_glow, + spk_embed=spk_embed, + spk_id=spk_id, + two_stage=hparams['two_stage'], + bert_feats=sample.get('bert_feats')) + return output + + def validation_step(self, sample, batch_idx): + self.training_post_glow = self.global_step >= hparams['post_glow_training_start'] \ + and hparams['use_post_flow'] + return super().validation_step(sample, batch_idx) + + def save_valid_result(self, sample, batch_idx, model_out): + super(PortaSpeechFlowTask, self).save_valid_result(sample, batch_idx, model_out) + sr = hparams['audio_sample_rate'] + f0_gt = None + if sample.get('f0') is not None: + f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu()) + if self.global_step > 0: + # save FVAE result + if hparams['use_post_flow']: + wav_pred = self.vocoder.spec2wav(model_out['mel_out_fvae'][0].cpu(), f0=f0_gt) + self.logger.add_audio(f'wav_fvae_{batch_idx}', wav_pred, self.global_step, sr) + self.plot_mel(batch_idx, sample['mels'], model_out['mel_out_fvae'][0], + f'mel_fvae_{batch_idx}', f0s=f0_gt) + + def build_optimizer(self, model): + if hparams['two_stage'] and hparams['use_post_flow']: + self.optimizer = torch.optim.AdamW( + [p for name, p in self.model.named_parameters() if 'post_flow' not in name], + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + self.post_flow_optimizer = torch.optim.AdamW( + self.model.post_flow.parameters(), + lr=hparams['post_flow_lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + return [self.optimizer, self.post_flow_optimizer] + else: + self.optimizer = torch.optim.AdamW( + self.model.parameters(), + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + return [self.optimizer] + + def build_scheduler(self, optimizer): + return FastSpeech2Task.build_scheduler(self, optimizer[0]) \ No newline at end of file diff --git a/NeuralSeq/tasks/tts/synta.py b/NeuralSeq/tasks/tts/synta.py new file mode 100644 index 0000000000000000000000000000000000000000..62cf865bc7d17c62b2c0c71f21d5b0ab596ba312 --- /dev/null +++ b/NeuralSeq/tasks/tts/synta.py @@ -0,0 +1,25 @@ +import os +import torch +import torch.nn.functional as F +from torch import nn + +from modules.tts.syntaspeech.syntaspeech import SyntaSpeech +from tasks.tts.ps_adv import PortaSpeechAdvTask +from utils.hparams import hparams + + +class SyntaSpeechTask(PortaSpeechAdvTask): + def build_tts_model(self): + ph_dict_size = len(self.token_encoder) + word_dict_size = len(self.word_encoder) + self.model = SyntaSpeech(ph_dict_size, word_dict_size, hparams) + + self.gen_params = [p for p in self.model.parameters() if p.requires_grad] + self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)] + self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)] + self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)] + self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ] + + self.use_bert = True if len(self.bert_params) > 0 else False + + \ No newline at end of file diff --git a/NeuralSeq/tasks/tts/tts.py b/NeuralSeq/tasks/tts/tts.py new file mode 100644 index 0000000000000000000000000000000000000000..f803c1e738137cb1eca19a1943196abd2884c0a5 --- /dev/null +++ b/NeuralSeq/tasks/tts/tts.py @@ -0,0 +1,131 @@ +from multiprocessing.pool import Pool + +import matplotlib + +from utils.pl_utils import data_loader +from utils.training_utils import RSQRTSchedule +from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder +from modules.fastspeech.pe import PitchExtractor + +matplotlib.use('Agg') +import os +import numpy as np +from tqdm import tqdm +import torch.distributed as dist + +from tasks.base_task import BaseTask +from utils.hparams import hparams +from utils.text_encoder import TokenTextEncoder +import json + +import torch +import torch.optim +import torch.utils.data +import utils + + + +class TtsTask(BaseTask): + def __init__(self, *args, **kwargs): + self.vocoder = None + self.phone_encoder = self.build_phone_encoder(hparams['binary_data_dir']) + self.padding_idx = self.phone_encoder.pad() + self.eos_idx = self.phone_encoder.eos() + self.seg_idx = self.phone_encoder.seg() + self.saving_result_pool = None + self.saving_results_futures = None + self.stats = {} + super().__init__(*args, **kwargs) + + def build_scheduler(self, optimizer): + return RSQRTSchedule(optimizer) + + def build_optimizer(self, model): + self.optimizer = optimizer = torch.optim.AdamW( + model.parameters(), + lr=hparams['lr']) + return optimizer + + def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None, + required_batch_size_multiple=-1, endless=False, batch_by_size=True): + devices_cnt = torch.cuda.device_count() + if devices_cnt == 0: + devices_cnt = 1 + if required_batch_size_multiple == -1: + required_batch_size_multiple = devices_cnt + + def shuffle_batches(batches): + np.random.shuffle(batches) + return batches + + if max_tokens is not None: + max_tokens *= devices_cnt + if max_sentences is not None: + max_sentences *= devices_cnt + indices = dataset.ordered_indices() + if batch_by_size: + batch_sampler = utils.batch_by_size( + indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + else: + batch_sampler = [] + for i in range(0, len(indices), max_sentences): + batch_sampler.append(indices[i:i + max_sentences]) + + if shuffle: + batches = shuffle_batches(list(batch_sampler)) + if endless: + batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))] + else: + batches = batch_sampler + if endless: + batches = [b for _ in range(1000) for b in batches] + num_workers = dataset.num_workers + if self.trainer.use_ddp: + num_replicas = dist.get_world_size() + rank = dist.get_rank() + batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0] + return torch.utils.data.DataLoader(dataset, + collate_fn=dataset.collater, + batch_sampler=batches, + num_workers=num_workers, + pin_memory=False) + + def build_phone_encoder(self, data_dir): + phone_list_file = os.path.join(data_dir, 'phone_set.json') + + phone_list = json.load(open(phone_list_file)) + return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') + + def build_optimizer(self, model): + self.optimizer = optimizer = torch.optim.AdamW( + model.parameters(), + lr=hparams['lr']) + return optimizer + + def test_start(self): + self.saving_result_pool = Pool(8) + self.saving_results_futures = [] + self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() + if hparams.get('pe_enable') is not None and hparams['pe_enable']: + self.pe = PitchExtractor().cuda() + utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True) + self.pe.eval() + def test_end(self, outputs): + self.saving_result_pool.close() + [f.get() for f in tqdm(self.saving_results_futures)] + self.saving_result_pool.join() + return {} + + ########## + # utils + ########## + def weights_nonzero_speech(self, target): + # target : B x T x mel + # Assign weight 1.0 to all labels except for padding (id=0). + dim = target.size(-1) + return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim) + +if __name__ == '__main__': + TtsTask.start() diff --git a/NeuralSeq/tasks/tts/tts_base.py b/NeuralSeq/tasks/tts/tts_base.py new file mode 100644 index 0000000000000000000000000000000000000000..509740b54dbf23db6bafebd6bc46089ee83cf499 --- /dev/null +++ b/NeuralSeq/tasks/tts/tts_base.py @@ -0,0 +1,305 @@ +import filecmp + +import matplotlib + +from utils.plot import spec_to_figure + +matplotlib.use('Agg') + +from data_gen.tts.data_gen_utils import get_pitch +from modules.fastspeech.tts_modules import mel2ph_to_dur +from tasks.tts.dataset_utils import BaseTTSDataset +from utils.tts_utils import sequence_mask +from multiprocessing.pool import Pool +from tasks.base_task import data_loader, BaseConcatDataset +from utils.common_schedulers import RSQRTSchedule, NoneSchedule +from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder +import os +import numpy as np +from tqdm import tqdm +import torch.distributed as dist +from tasks.base_task import BaseTask +from utils.hparams import hparams +from utils.text_encoder import TokenTextEncoder +import json +import matplotlib.pyplot as plt +import torch +import torch.optim +import torch.utils.data +import utils +from utils import audio +import pandas as pd + + +class TTSBaseTask(BaseTask): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.dataset_cls = BaseTTSDataset + self.max_tokens = hparams['max_tokens'] + self.max_sentences = hparams['max_sentences'] + self.max_valid_tokens = hparams['max_valid_tokens'] + if self.max_valid_tokens == -1: + hparams['max_valid_tokens'] = self.max_valid_tokens = self.max_tokens + self.max_valid_sentences = hparams['max_valid_sentences'] + if self.max_valid_sentences == -1: + hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences + self.vocoder = None + self.phone_encoder = self.build_phone_encoder(hparams['binary_data_dir']) + self.padding_idx = self.phone_encoder.pad() + self.eos_idx = self.phone_encoder.eos() + self.seg_idx = self.phone_encoder.seg() + self.saving_result_pool = None + self.saving_results_futures = None + self.stats = {} + + @data_loader + def train_dataloader(self): + if hparams['train_sets'] != '': + train_sets = hparams['train_sets'].split("|") + # check if all train_sets have the same spk map and dictionary + binary_data_dir = hparams['binary_data_dir'] + file_to_cmp = ['phone_set.json'] + if os.path.exists(f'{binary_data_dir}/word_set.json'): + file_to_cmp.append('word_set.json') + if hparams['use_spk_id']: + file_to_cmp.append('spk_map.json') + for f in file_to_cmp: + for ds_name in train_sets: + base_file = os.path.join(binary_data_dir, f) + ds_file = os.path.join(ds_name, f) + assert filecmp.cmp(base_file, ds_file), \ + f'{f} in {ds_name} is not same with that in {binary_data_dir}.' + train_dataset = BaseConcatDataset([ + self.dataset_cls(prefix='train', shuffle=True, data_dir=ds_name) for ds_name in train_sets]) + else: + train_dataset = self.dataset_cls(prefix=hparams['train_set_name'], shuffle=True) + return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, + endless=hparams['endless_ds']) + + @data_loader + def val_dataloader(self): + valid_dataset = self.dataset_cls(prefix=hparams['valid_set_name'], shuffle=False) + return self.build_dataloader(valid_dataset, False, self.max_valid_tokens, self.max_valid_sentences) + + @data_loader + def test_dataloader(self): + test_dataset = self.dataset_cls(prefix=hparams['test_set_name'], shuffle=False) + self.test_dl = self.build_dataloader( + test_dataset, False, self.max_valid_tokens, + self.max_valid_sentences, batch_by_size=False) + return self.test_dl + + def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None, + required_batch_size_multiple=-1, endless=False, batch_by_size=True): + devices_cnt = torch.cuda.device_count() + if devices_cnt == 0: + devices_cnt = 1 + if required_batch_size_multiple == -1: + required_batch_size_multiple = devices_cnt + + def shuffle_batches(batches): + np.random.shuffle(batches) + return batches + + if max_tokens is not None: + max_tokens *= devices_cnt + if max_sentences is not None: + max_sentences *= devices_cnt + indices = dataset.ordered_indices() + if batch_by_size: + batch_sampler = utils.batch_by_size( + indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + else: + batch_sampler = [] + for i in range(0, len(indices), max_sentences): + batch_sampler.append(indices[i:i + max_sentences]) + + if shuffle: + batches = shuffle_batches(list(batch_sampler)) + if endless: + batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))] + else: + batches = batch_sampler + if endless: + batches = [b for _ in range(1000) for b in batches] + num_workers = dataset.num_workers + if self.trainer.use_ddp: + num_replicas = dist.get_world_size() + rank = dist.get_rank() + batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0] + return torch.utils.data.DataLoader(dataset, + collate_fn=dataset.collater, + batch_sampler=batches, + num_workers=num_workers, + pin_memory=False) + + def build_phone_encoder(self, data_dir): + phone_list_file = os.path.join(data_dir, 'phone_set.json') + phone_list = json.load(open(phone_list_file)) + return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') + + def build_scheduler(self, optimizer): + if hparams['scheduler'] == 'rsqrt': + return RSQRTSchedule(optimizer) + else: + return NoneSchedule(optimizer) + + def build_optimizer(self, model): + self.optimizer = optimizer = torch.optim.AdamW( + model.parameters(), + lr=hparams['lr'], + betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), + weight_decay=hparams['weight_decay']) + return optimizer + + def plot_mel(self, batch_idx, spec, spec_out, name=None): + spec_cat = torch.cat([spec, spec_out], -1) + name = f'mel_{batch_idx}' if name is None else name + vmin = hparams['mel_vmin'] + vmax = hparams['mel_vmax'] + self.logger.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) + + def test_start(self): + self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) + self.saving_results_futures = [] + self.results_id = 0 + self.gen_dir = os.path.join( + hparams['work_dir'], + f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') + self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() + + def after_infer(self, predictions, sil_start_frame=0): + predictions = utils.unpack_dict_to_list(predictions) + assert len(predictions) == 1, 'Only support batch_size=1 in inference.' + prediction = predictions[0] + prediction = utils.tensors_to_np(prediction) + item_name = prediction.get('item_name') + text = prediction.get('text') + ph_tokens = prediction.get('txt_tokens') + mel_gt = prediction["mels"] + mel2ph_gt = prediction.get("mel2ph") + mel2ph_gt = mel2ph_gt if mel2ph_gt is not None else None + mel_pred = prediction["outputs"] + mel2ph_pred = prediction.get("mel2ph_pred") + f0_gt = prediction.get("f0") + f0_pred = prediction.get("f0_pred") + + str_phs = None + if self.phone_encoder is not None and 'txt_tokens' in prediction: + str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) + + if 'encdec_attn' in prediction: + encdec_attn = prediction['encdec_attn'] + encdec_attn = encdec_attn[encdec_attn.max(-1).sum(-1).argmax(-1)] + txt_lengths = prediction.get('txt_lengths') + encdec_attn = encdec_attn.T[:txt_lengths, :len(mel_gt)] + else: + encdec_attn = None + + wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) + wav_pred[:sil_start_frame * hparams['hop_size']] = 0 + gen_dir = self.gen_dir + base_fn = f'[{self.results_id:06d}][{item_name}][%s]' + # if text is not None: + # base_fn += text.replace(":", "%3A")[:80] + base_fn = base_fn.replace(' ', '_') + if not hparams['profile_infer']: + os.makedirs(gen_dir, exist_ok=True) + os.makedirs(f'{gen_dir}/wavs', exist_ok=True) + os.makedirs(f'{gen_dir}/plot', exist_ok=True) + if hparams.get('save_mel_npy', False): + os.makedirs(f'{gen_dir}/npy', exist_ok=True) + if 'encdec_attn' in prediction: + os.makedirs(f'{gen_dir}/attn_plot', exist_ok=True) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred, encdec_attn])) + + if mel_gt is not None and hparams['save_gt']: + wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) + self.saving_results_futures.append( + self.saving_result_pool.apply_async(self.save_result, args=[ + wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph_gt])) + if hparams['save_f0']: + import matplotlib.pyplot as plt + f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) + f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) + fig = plt.figure() + plt.plot(f0_pred_, label=r'$\hat{f_0}$') + plt.plot(f0_gt_, label=r'$f_0$') + plt.legend() + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') + plt.close(fig) + print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") + self.results_id += 1 + return { + 'item_name': item_name, + 'text': text, + 'ph_tokens': self.phone_encoder.decode(ph_tokens.tolist()), + 'wav_fn_pred': base_fn % 'P', + 'wav_fn_gt': base_fn % 'G', + } + + @staticmethod + def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None): + audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], + norm=hparams['out_wav_norm']) + fig = plt.figure(figsize=(14, 10)) + spec_vmin = hparams['mel_vmin'] + spec_vmax = hparams['mel_vmax'] + heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) + fig.colorbar(heatmap) + f0, _ = get_pitch(wav_out, mel, hparams) + f0 = f0 / 10 * (f0 > 0) + plt.plot(f0, c='white', linewidth=1, alpha=0.6) + if mel2ph is not None and str_phs is not None: + decoded_txt = str_phs.split(" ") + dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() + dur = [0] + list(np.cumsum(dur)) + for i in range(len(dur) - 1): + shift = (i % 20) + 1 + plt.text(dur[i], shift, decoded_txt[i]) + plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') + plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', + alpha=1, linewidth=1) + plt.tight_layout() + plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png') + plt.close(fig) + if hparams.get('save_mel_npy', False): + np.save(f'{gen_dir}/npy/{base_fn}', mel) + if alignment is not None: + fig, ax = plt.subplots(figsize=(12, 16)) + im = ax.imshow(alignment, aspect='auto', origin='lower', + interpolation='none') + decoded_txt = str_phs.split(" ") + ax.set_yticks(np.arange(len(decoded_txt))) + ax.set_yticklabels(list(decoded_txt), fontsize=6) + fig.colorbar(im, ax=ax) + fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png') + plt.close(fig) + + def test_end(self, outputs): + pd.DataFrame(outputs).to_csv(f'{self.gen_dir}/meta.csv') + self.saving_result_pool.close() + [f.get() for f in tqdm(self.saving_results_futures)] + self.saving_result_pool.join() + return {} + + ########## + # utils + ########## + def weights_nonzero_speech(self, target): + # target : B x T x mel + # Assign weight 1.0 to all labels except for padding (id=0). + dim = target.size(-1) + return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim) + + def make_stop_target(self, target): + # target : B x T x mel + seq_mask = target.abs().sum(-1).ne(0).float() + seq_length = seq_mask.sum(1) + mask_r = 1 - sequence_mask(seq_length - 1, target.size(1)).float() + return seq_mask, mask_r diff --git a/NeuralSeq/tasks/tts/tts_utils.py b/NeuralSeq/tasks/tts/tts_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e13439ee72e4fda220605c5868b3159110d9129b --- /dev/null +++ b/NeuralSeq/tasks/tts/tts_utils.py @@ -0,0 +1,54 @@ +import importlib + +from data_gen.tts.base_binarizer import BaseBinarizer +from data_gen.tts.base_preprocess import BasePreprocessor +from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls +from utils.hparams import hparams + + +def parse_dataset_configs(): + max_tokens = hparams['max_tokens'] + max_sentences = hparams['max_sentences'] + max_valid_tokens = hparams['max_valid_tokens'] + if max_valid_tokens == -1: + hparams['max_valid_tokens'] = max_valid_tokens = max_tokens + max_valid_sentences = hparams['max_valid_sentences'] + if max_valid_sentences == -1: + hparams['max_valid_sentences'] = max_valid_sentences = max_sentences + return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences + + +def parse_mel_losses(): + mel_losses = hparams['mel_losses'].split("|") + loss_and_lambda = {} + for i, l in enumerate(mel_losses): + if l == '': + continue + if ':' in l: + l, lbd = l.split(":") + lbd = float(lbd) + else: + lbd = 1.0 + loss_and_lambda[l] = lbd + print("| Mel losses:", loss_and_lambda) + return loss_and_lambda + + +def load_data_preprocessor(): + preprocess_cls = hparams["preprocess_cls"] + pkg = ".".join(preprocess_cls.split(".")[:-1]) + cls_name = preprocess_cls.split(".")[-1] + preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)() + preprocess_args = {} + preprocess_args.update(hparams['preprocess_args']) + return preprocessor, preprocess_args + + +def load_data_binarizer(): + binarizer_cls = hparams['binarizer_cls'] + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)() + binarization_args = {} + binarization_args.update(hparams['binarization_args']) + return binarizer, binarization_args \ No newline at end of file diff --git a/NeuralSeq/tasks/vocoder/dataset_utils.py b/NeuralSeq/tasks/vocoder/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..05dcdaa524efde31575dd30b57b627d22744b53c --- /dev/null +++ b/NeuralSeq/tasks/vocoder/dataset_utils.py @@ -0,0 +1,204 @@ +import glob +import importlib +import os +from resemblyzer import VoiceEncoder +import numpy as np +import torch +import torch.distributed as dist +from torch.utils.data import DistributedSampler +import utils +from tasks.base_task import BaseDataset +from utils.hparams import hparams +from utils.indexed_datasets import IndexedDataset +from tqdm import tqdm + +class EndlessDistributedSampler(DistributedSampler): + def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.shuffle = shuffle + + g = torch.Generator() + g.manual_seed(self.epoch) + if self.shuffle: + indices = [i for _ in range(1000) for i in torch.randperm( + len(self.dataset), generator=g).tolist()] + else: + indices = [i for _ in range(1000) for i in list(range(len(self.dataset)))] + indices = indices[:len(indices) // self.num_replicas * self.num_replicas] + indices = indices[self.rank::self.num_replicas] + self.indices = indices + + def __iter__(self): + return iter(self.indices) + + def __len__(self): + return len(self.indices) + + +class VocoderDataset(BaseDataset): + def __init__(self, prefix, shuffle=False): + super().__init__(shuffle) + self.hparams = hparams + self.prefix = prefix + self.data_dir = hparams['binary_data_dir'] + self.is_infer = prefix == 'test' + self.batch_max_frames = 0 if self.is_infer else hparams['max_samples'] // hparams['hop_size'] + self.aux_context_window = hparams['aux_context_window'] + self.hop_size = hparams['hop_size'] + if self.is_infer and hparams['test_input_dir'] != '': + self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) + self.avail_idxs = [i for i, _ in enumerate(self.sizes)] + elif self.is_infer and hparams['test_mel_dir'] != '': + self.indexed_ds, self.sizes = self.load_mel_inputs(hparams['test_mel_dir']) + self.avail_idxs = [i for i, _ in enumerate(self.sizes)] + else: + self.indexed_ds = None + self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') + self.avail_idxs = [idx for idx, s in enumerate(self.sizes) if + s - 2 * self.aux_context_window > self.batch_max_frames] + print(f"| {len(self.sizes) - len(self.avail_idxs)} short items are skipped in {prefix} set.") + self.sizes = [s for idx, s in enumerate(self.sizes) if + s - 2 * self.aux_context_window > self.batch_max_frames] + + def _get_item(self, index): + if self.indexed_ds is None: + self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') + item = self.indexed_ds[index] + return item + + def __getitem__(self, index): + index = self.avail_idxs[index] + item = self._get_item(index) + sample = { + "id": index, + "item_name": item['item_name'], + "mel": torch.FloatTensor(item['mel']), + "wav": torch.FloatTensor(item['wav'].astype(np.float32)), + } + if 'pitch' in item: + sample['pitch'] = torch.LongTensor(item['pitch']) + sample['f0'] = torch.FloatTensor(item['f0']) + + if hparams.get('use_spk_embed', False): + sample["spk_embed"] = torch.Tensor(item['spk_embed']) + if hparams.get('use_emo_embed', False): + sample["emo_embed"] = torch.Tensor(item['emo_embed']) + + return sample + + def collater(self, batch): + if len(batch) == 0: + return {} + + y_batch, c_batch, p_batch, f0_batch = [], [], [], [] + item_name = [] + have_pitch = 'pitch' in batch[0] + for idx in range(len(batch)): + item_name.append(batch[idx]['item_name']) + x, c = batch[idx]['wav'] if self.hparams['use_wav'] else None, batch[idx]['mel'].squeeze(0) + if have_pitch: + p = batch[idx]['pitch'] + f0 = batch[idx]['f0'] + if self.hparams['use_wav']:self._assert_ready_for_upsampling(x, c, self.hop_size, 0) + if len(c) - 2 * self.aux_context_window > self.batch_max_frames: + # randomly pickup with the batch_max_steps length of the part + batch_max_frames = self.batch_max_frames if self.batch_max_frames != 0 else len( + c) - 2 * self.aux_context_window - 1 + batch_max_steps = batch_max_frames * self.hop_size + interval_start = self.aux_context_window + interval_end = len(c) - batch_max_frames - self.aux_context_window + start_frame = np.random.randint(interval_start, interval_end) + start_step = start_frame * self.hop_size + if self.hparams['use_wav']:y = x[start_step: start_step + batch_max_steps] + c = c[start_frame - self.aux_context_window: + start_frame + self.aux_context_window + batch_max_frames] + if have_pitch: + p = p[start_frame - self.aux_context_window: + start_frame + self.aux_context_window + batch_max_frames] + f0 = f0[start_frame - self.aux_context_window: + start_frame + self.aux_context_window + batch_max_frames] + if self.hparams['use_wav']:self._assert_ready_for_upsampling(y, c, self.hop_size, self.aux_context_window) + else: + print(f"Removed short sample from batch (length={len(x)}).") + continue + if self.hparams['use_wav']:y_batch += [y.reshape(-1, 1)] # [(T, 1), (T, 1), ...] + c_batch += [c] # [(T' C), (T' C), ...] + if have_pitch: + p_batch += [p] # [(T' C), (T' C), ...] + f0_batch += [f0] # [(T' C), (T' C), ...] + + # convert each batch to tensor, asuume that each item in batch has the same length + if self.hparams['use_wav']:y_batch = utils.collate_2d(y_batch, 0).transpose(2, 1) # (B, 1, T) + c_batch = utils.collate_2d(c_batch, 0).transpose(2, 1) # (B, C, T') + if have_pitch: + p_batch = utils.collate_1d(p_batch, 0) # (B, T') + f0_batch = utils.collate_1d(f0_batch, 0) # (B, T') + else: + p_batch, f0_batch = None, None + + # make input noise signal batch tensor + if self.hparams['use_wav']: z_batch = torch.randn(y_batch.size()) # (B, 1, T) + else: z_batch=[] + return { + 'z': z_batch, + 'mels': c_batch, + 'wavs': y_batch, + 'pitches': p_batch, + 'f0': f0_batch, + 'item_name': item_name + } + + @staticmethod + def _assert_ready_for_upsampling(x, c, hop_size, context_window): + """Assert the audio and feature lengths are correctly adjusted for upsamping.""" + assert len(x) == (len(c) - 2 * context_window) * hop_size + + def load_test_inputs(self, test_input_dir, spk_id=0): + inp_wav_paths = sorted(glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/**/*.mp3')) + sizes = [] + items = [] + + binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + binarization_args = hparams['binarization_args'] + + for wav_fn in inp_wav_paths: + item_name = wav_fn[len(test_input_dir) + 1:].replace("/", "_") + item = binarizer_cls.process_item( + item_name, wav_fn, binarization_args) + items.append(item) + sizes.append(item['len']) + return items, sizes + + def load_mel_inputs(self, test_input_dir, spk_id=0): + inp_mel_paths = sorted(glob.glob(f'{test_input_dir}/*.npy')) + sizes = [] + items = [] + + binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer') + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + binarization_args = hparams['binarization_args'] + + for mel in inp_mel_paths: + mel_input = np.load(mel) + mel_input = torch.FloatTensor(mel_input) + item_name = mel[len(test_input_dir) + 1:].replace("/", "_") + item = binarizer_cls.process_mel_item(item_name, mel_input, None, binarization_args) + items.append(item) + sizes.append(item['len']) + return items, sizes diff --git a/NeuralSeq/tasks/vocoder/vocoder_base.py b/NeuralSeq/tasks/vocoder/vocoder_base.py new file mode 100644 index 0000000000000000000000000000000000000000..04f45af60c8ac1c1f8303d091f8c6031ec8451bf --- /dev/null +++ b/NeuralSeq/tasks/vocoder/vocoder_base.py @@ -0,0 +1,66 @@ +import os + +import torch +import torch.distributed as dist +from torch.utils.data import DistributedSampler + +from tasks.base_task import BaseTask +from tasks.base_task import data_loader +from tasks.vocoder.dataset_utils import VocoderDataset, EndlessDistributedSampler +from utils.hparams import hparams + + +class VocoderBaseTask(BaseTask): + def __init__(self): + super(VocoderBaseTask, self).__init__() + self.max_sentences = hparams['max_sentences'] + self.max_valid_sentences = hparams['max_valid_sentences'] + if self.max_valid_sentences == -1: + hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences + self.dataset_cls = VocoderDataset + + @data_loader + def train_dataloader(self): + train_dataset = self.dataset_cls('train', shuffle=True) + return self.build_dataloader(train_dataset, True, self.max_sentences, hparams['endless_ds']) + + @data_loader + def val_dataloader(self): + valid_dataset = self.dataset_cls('valid', shuffle=False) + return self.build_dataloader(valid_dataset, False, self.max_valid_sentences) + + @data_loader + def test_dataloader(self): + test_dataset = self.dataset_cls('test', shuffle=False) + return self.build_dataloader(test_dataset, False, self.max_valid_sentences) + + def build_dataloader(self, dataset, shuffle, max_sentences, endless=False): + world_size = 1 + rank = 0 + if dist.is_initialized(): + world_size = dist.get_world_size() + rank = dist.get_rank() + sampler_cls = DistributedSampler if not endless else EndlessDistributedSampler + train_sampler = sampler_cls( + dataset=dataset, + num_replicas=world_size, + rank=rank, + shuffle=shuffle, + ) + return torch.utils.data.DataLoader( + dataset=dataset, + shuffle=False, + collate_fn=dataset.collater, + batch_size=max_sentences, + num_workers=dataset.num_workers, + sampler=train_sampler, + pin_memory=True, + ) + + def test_start(self): + self.gen_dir = os.path.join(hparams['work_dir'], + f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') + os.makedirs(self.gen_dir, exist_ok=True) + + def test_end(self, outputs): + return {} diff --git a/NeuralSeq/usr/.gitkeep b/NeuralSeq/usr/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/NeuralSeq/usr/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/usr/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..113de6309e3e63413ebcfe597cc955dbc5df7a0a Binary files /dev/null and b/NeuralSeq/usr/__pycache__/__init__.cpython-37.pyc differ diff --git a/NeuralSeq/usr/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/usr/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a1ab6e3f8f7f54c2091146a5567ca8bbcc75b6cc Binary files /dev/null and b/NeuralSeq/usr/__pycache__/__init__.cpython-38.pyc differ diff --git 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mode 100644 index 0000000000000000000000000000000000000000..4ea5c5a67e038c2213247dfb905942882c090a77 --- /dev/null +++ b/NeuralSeq/utils/__init__.py @@ -0,0 +1,250 @@ +import glob +import logging +import re +import time +from collections import defaultdict +import os +import sys +import shutil +import types +import numpy as np +import torch +import torch.nn.functional as F +import torch.distributed as dist +from torch import nn + + +def tensors_to_scalars(metrics): + new_metrics = {} + for k, v in metrics.items(): + if isinstance(v, torch.Tensor): + v = v.item() + if type(v) is dict: + v = tensors_to_scalars(v) + new_metrics[k] = v + return new_metrics + + +class AvgrageMeter(object): + + def __init__(self): + self.reset() + + def reset(self): + self.avg = 0 + self.sum = 0 + self.cnt = 0 + + def update(self, val, n=1): + self.sum += val * n + self.cnt += n + self.avg = self.sum / self.cnt + + +def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1): + """Convert a list of 1d tensors into a padded 2d tensor.""" + size = max(v.size(0) for v in values) if max_len is None else max_len + res = values[0].new(len(values), size).fill_(pad_idx) + + def copy_tensor(src, dst): + assert dst.numel() == src.numel() + if shift_right: + dst[1:] = src[:-1] + dst[0] = shift_id + else: + dst.copy_(src) + + for i, v in enumerate(values): + copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)]) + return res + + +def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None): + """Convert a list of 2d tensors into a padded 3d tensor.""" + size = max(v.size(0) for v in values) if max_len is None else max_len + res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx) + + def copy_tensor(src, dst): + assert dst.numel() == src.numel() + if shift_right: + dst[1:] = src[:-1] + else: + dst.copy_(src) + + for i, v in enumerate(values): + copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)]) + return res + + +def _is_batch_full(batch, num_tokens, max_tokens, max_sentences): + if len(batch) == 0: + return 0 + if len(batch) == max_sentences: + return 1 + if num_tokens > max_tokens: + return 1 + return 0 + + +def batch_by_size( + indices, num_tokens_fn, max_tokens=None, max_sentences=None, + required_batch_size_multiple=1, distributed=False +): + """ + Yield mini-batches of indices bucketed by size. Batches may contain + sequences of different lengths. + + Args: + indices (List[int]): ordered list of dataset indices + num_tokens_fn (callable): function that returns the number of tokens at + a given index + max_tokens (int, optional): max number of tokens in each batch + (default: None). + max_sentences (int, optional): max number of sentences in each + batch (default: None). + required_batch_size_multiple (int, optional): require batch size to + be a multiple of N (default: 1). + """ + max_tokens = max_tokens if max_tokens is not None else sys.maxsize + max_sentences = max_sentences if max_sentences is not None else sys.maxsize + bsz_mult = required_batch_size_multiple + + if isinstance(indices, types.GeneratorType): + indices = np.fromiter(indices, dtype=np.int64, count=-1) + + sample_len = 0 + sample_lens = [] + batch = [] + batches = [] + for i in range(len(indices)): + idx = indices[i] + num_tokens = num_tokens_fn(idx) + sample_lens.append(num_tokens) + sample_len = max(sample_len, num_tokens) + assert sample_len <= max_tokens, ( + "sentence at index {} of size {} exceeds max_tokens " + "limit of {}!".format(idx, sample_len, max_tokens) + ) + num_tokens = (len(batch) + 1) * sample_len + + if _is_batch_full(batch, num_tokens, max_tokens, max_sentences): + mod_len = max( + bsz_mult * (len(batch) // bsz_mult), + len(batch) % bsz_mult, + ) + batches.append(batch[:mod_len]) + batch = batch[mod_len:] + sample_lens = sample_lens[mod_len:] + sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 + batch.append(idx) + if len(batch) > 0: + batches.append(batch) + return batches + + +def make_positions(tensor, padding_idx): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + # The series of casts and type-conversions here are carefully + # balanced to both work with ONNX export and XLA. In particular XLA + # prefers ints, cumsum defaults to output longs, and ONNX doesn't know + # how to handle the dtype kwarg in cumsum. + mask = tensor.ne(padding_idx).int() + return ( + torch.cumsum(mask, dim=1).type_as(mask) * mask + ).long() + padding_idx + + +def softmax(x, dim): + return F.softmax(x, dim=dim, dtype=torch.float32) + + +def unpack_dict_to_list(samples): + samples_ = [] + bsz = samples.get('outputs').size(0) + for i in range(bsz): + res = {} + for k, v in samples.items(): + try: + res[k] = v[i] + except: + pass + samples_.append(res) + return samples_ + + +def load_ckpt(cur_model, ckpt_base_dir, prefix_in_ckpt='model', force=True, strict=True): + if os.path.isfile(ckpt_base_dir): + base_dir = os.path.dirname(ckpt_base_dir) + checkpoint_path = [ckpt_base_dir] + else: + base_dir = ckpt_base_dir + checkpoint_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= + lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0])) + if len(checkpoint_path) > 0: + checkpoint_path = checkpoint_path[-1] + state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"] + state_dict = {k[len(prefix_in_ckpt) + 1:]: v for k, v in state_dict.items() + if k.startswith(f'{prefix_in_ckpt}.')} + if not strict: + cur_model_state_dict = cur_model.state_dict() + unmatched_keys = [] + for key, param in state_dict.items(): + if key in cur_model_state_dict: + new_param = cur_model_state_dict[key] + if new_param.shape != param.shape: + unmatched_keys.append(key) + print("| Unmatched keys: ", key, new_param.shape, param.shape) + for key in unmatched_keys: + del state_dict[key] + cur_model.load_state_dict(state_dict, strict=strict) + print(f"| load '{prefix_in_ckpt}' from '{checkpoint_path}'.") + else: + e_msg = f"| ckpt not found in {base_dir}." + if force: + assert False, e_msg + else: + print(e_msg) + + +def remove_padding(x, padding_idx=0): + if x is None: + return None + assert len(x.shape) in [1, 2] + if len(x.shape) == 2: # [T, H] + return x[np.abs(x).sum(-1) != padding_idx] + elif len(x.shape) == 1: # [T] + return x[x != padding_idx] + + +class Timer: + timer_map = {} + + def __init__(self, name, print_time=False): + if name not in Timer.timer_map: + Timer.timer_map[name] = 0 + self.name = name + self.print_time = print_time + + def __enter__(self): + self.t = time.time() + + def __exit__(self, exc_type, exc_val, exc_tb): + Timer.timer_map[self.name] += time.time() - self.t + if self.print_time: + print(self.name, Timer.timer_map[self.name]) + + +def print_arch(model, model_name='model'): + print(f"| {model_name} Arch: ", model) + num_params(model, model_name=model_name) + + +def num_params(model, print_out=True, model_name="model"): + parameters = filter(lambda p: p.requires_grad, model.parameters()) + parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 + if print_out: + print(f'| {model_name} Trainable Parameters: %.3fM' % parameters) + return parameters diff --git 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0000000000000000000000000000000000000000..abdec3af193523852bdd2ce4359233d989a2bf91 Binary files /dev/null and b/NeuralSeq/utils/__pycache__/tts_utils.cpython-38.pyc differ diff --git a/NeuralSeq/utils/audio.py b/NeuralSeq/utils/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..30be102e9add71068ebab579927d4166f6f06a54 --- /dev/null +++ b/NeuralSeq/utils/audio.py @@ -0,0 +1,92 @@ +import subprocess +import matplotlib +import os +matplotlib.use('Agg') +import librosa +import librosa.filters +import numpy as np +from scipy import signal +from scipy.io import wavfile + + +def save_wav(wav, path, sr, norm=False): + if norm: + wav = wav / np.abs(wav).max() + wav *= 32767 + # proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + + +def get_hop_size(hparams): + hop_size = hparams['hop_size'] + if hop_size is None: + assert hparams['frame_shift_ms'] is not None + hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate']) + return hop_size + + +########################################################################################### +def _stft(y, hparams): + return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams), + win_length=hparams['win_size'], pad_mode='constant') + + +def _istft(y, hparams): + return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size']) + + +def librosa_pad_lr(x, fsize, fshift, pad_sides=1): + '''compute right padding (final frame) or both sides padding (first and final frames) + ''' + assert pad_sides in (1, 2) + # return int(fsize // 2) + pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0] + if pad_sides == 1: + return 0, pad + else: + return pad // 2, pad // 2 + pad % 2 + + +# Conversions +def amp_to_db(x): + return 20 * np.log10(np.maximum(1e-5, x)) + + +def normalize(S, hparams): + return (S - hparams['min_level_db']) / -hparams['min_level_db'] + +def denormalize(D, hparams): + return (D * -hparams['min_level_db']) + hparams['min_level_db'] +def rnnoise(filename, out_fn=None, verbose=False, out_sample_rate=22050): + assert os.path.exists('./rnnoise/examples/rnnoise_demo'), INSTALL_STR + if out_fn is None: + out_fn = f"{filename[:-4]}.denoised.wav" + out_48k_fn = f"{out_fn}.48000.wav" + tmp0_fn = f"{out_fn}.0.wav" + tmp1_fn = f"{out_fn}.1.wav" + tmp2_fn = f"{out_fn}.2.raw" + tmp3_fn = f"{out_fn}.3.raw" + if verbose: + print("Pre-processing audio...") # wav to pcm raw + subprocess.check_call( + f'sox "{filename}" -G -r48000 "{tmp0_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw + subprocess.check_call( + f'sox -v 0.95 "{tmp0_fn}" "{tmp1_fn}"', shell=True, stdin=subprocess.PIPE) # convert to raw + subprocess.check_call( + f'ffmpeg -y -i "{tmp1_fn}" -loglevel quiet -f s16le -ac 1 -ar 48000 "{tmp2_fn}"', + shell=True, stdin=subprocess.PIPE) # convert to raw + if verbose: + print("Applying rnnoise algorithm to audio...") # rnnoise + subprocess.check_call( + f'./rnnoise/examples/rnnoise_demo "{tmp2_fn}" "{tmp3_fn}"', shell=True) + + if verbose: + print("Post-processing audio...") # pcm raw to wav + if filename == out_fn: + subprocess.check_call(f'rm -f "{out_fn}"', shell=True) + subprocess.check_call( + f'sox -t raw -r 48000 -b 16 -e signed-integer -c 1 "{tmp3_fn}" "{out_48k_fn}"', shell=True) + subprocess.check_call(f'sox "{out_48k_fn}" -G -r{out_sample_rate} "{out_fn}"', shell=True) + subprocess.check_call(f'rm -f "{tmp0_fn}" "{tmp1_fn}" "{tmp2_fn}" "{tmp3_fn}" "{out_48k_fn}"', shell=True) + if verbose: + print("Audio-filtering completed!") \ No newline at end of file diff --git a/NeuralSeq/utils/ckpt_utils.py b/NeuralSeq/utils/ckpt_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fc321f9ba891ffffc374df65871c3085bf898afb --- /dev/null +++ b/NeuralSeq/utils/ckpt_utils.py @@ -0,0 +1,68 @@ +import glob +import logging +import os +import re +import torch + + +def get_last_checkpoint(work_dir, steps=None): + checkpoint = None + last_ckpt_path = None + ckpt_paths = get_all_ckpts(work_dir, steps) + if len(ckpt_paths) > 0: + last_ckpt_path = ckpt_paths[0] + checkpoint = torch.load(last_ckpt_path, map_location='cpu') + logging.info(f'load module from checkpoint: {last_ckpt_path}') + return checkpoint, last_ckpt_path + + +def get_all_ckpts(work_dir, steps=None): + if steps is None: + ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt' + else: + ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt' + return sorted(glob.glob(ckpt_path_pattern), + key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0])) + + +def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True): + if os.path.isfile(ckpt_base_dir): + base_dir = os.path.dirname(ckpt_base_dir) + ckpt_path = ckpt_base_dir + checkpoint = torch.load(ckpt_base_dir, map_location='cpu') + else: + base_dir = ckpt_base_dir + checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir) + if checkpoint is not None: + state_dict = checkpoint["state_dict"] + if len([k for k in state_dict.keys() if '.' in k]) > 0: + state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items() + if k.startswith(f'{model_name}.')} + else: + if '.' not in model_name: + state_dict = state_dict[model_name] + else: + base_model_name = model_name.split('.')[0] + rest_model_name = model_name[len(base_model_name) + 1:] + state_dict = { + k[len(rest_model_name) + 1:]: v for k, v in state_dict[base_model_name].items() + if k.startswith(f'{rest_model_name}.')} + if not strict: + cur_model_state_dict = cur_model.state_dict() + unmatched_keys = [] + for key, param in state_dict.items(): + if key in cur_model_state_dict: + new_param = cur_model_state_dict[key] + if new_param.shape != param.shape: + unmatched_keys.append(key) + print("| Unmatched keys: ", key, new_param.shape, param.shape) + for key in unmatched_keys: + del state_dict[key] + cur_model.load_state_dict(state_dict, strict=strict) + print(f"| load '{model_name}' from '{ckpt_path}'.") + else: + e_msg = f"| ckpt not found in {base_dir}." + if force: + assert False, e_msg + else: + print(e_msg) diff --git a/NeuralSeq/utils/cwt.py b/NeuralSeq/utils/cwt.py new file mode 100644 index 0000000000000000000000000000000000000000..1a08461b9e422aac614438e6240b7355b8e4bb2c --- /dev/null +++ b/NeuralSeq/utils/cwt.py @@ -0,0 +1,146 @@ +import librosa +import numpy as np +from pycwt import wavelet +from scipy.interpolate import interp1d + + +def load_wav(wav_file, sr): + wav, _ = librosa.load(wav_file, sr=sr, mono=True) + return wav + + +def convert_continuos_f0(f0): + '''CONVERT F0 TO CONTINUOUS F0 + Args: + f0 (ndarray): original f0 sequence with the shape (T) + Return: + (ndarray): continuous f0 with the shape (T) + ''' + # get uv information as binary + f0 = np.copy(f0) + uv = np.float32(f0 != 0) + + # get start and end of f0 + if (f0 == 0).all(): + print("| all of the f0 values are 0.") + return uv, f0 + start_f0 = f0[f0 != 0][0] + end_f0 = f0[f0 != 0][-1] + + # padding start and end of f0 sequence + start_idx = np.where(f0 == start_f0)[0][0] + end_idx = np.where(f0 == end_f0)[0][-1] + f0[:start_idx] = start_f0 + f0[end_idx:] = end_f0 + + # get non-zero frame index + nz_frames = np.where(f0 != 0)[0] + + # perform linear interpolation + f = interp1d(nz_frames, f0[nz_frames]) + cont_f0 = f(np.arange(0, f0.shape[0])) + + return uv, cont_f0 + + +def get_cont_lf0(f0, frame_period=5.0): + uv, cont_f0_lpf = convert_continuos_f0(f0) + # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20) + cont_lf0_lpf = np.log(cont_f0_lpf) + return uv, cont_lf0_lpf + + +def get_lf0_cwt(lf0): + ''' + input: + signal of shape (N) + output: + Wavelet_lf0 of shape(10, N), scales of shape(10) + ''' + mother = wavelet.MexicanHat() + dt = 0.005 + dj = 1 + s0 = dt * 2 + J = 9 + + Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother) + # Wavelet.shape => (J + 1, len(lf0)) + Wavelet_lf0 = np.real(Wavelet_lf0).T + return Wavelet_lf0, scales + + +def norm_scale(Wavelet_lf0): + Wavelet_lf0_norm = np.zeros((Wavelet_lf0.shape[0], Wavelet_lf0.shape[1])) + mean = Wavelet_lf0.mean(0)[None, :] + std = Wavelet_lf0.std(0)[None, :] + Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std + return Wavelet_lf0_norm, mean, std + + +def normalize_cwt_lf0(f0, mean, std): + uv, cont_lf0_lpf = get_cont_lf0(f0) + cont_lf0_norm = (cont_lf0_lpf - mean) / std + Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm) + Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0) + + return Wavelet_lf0_norm + + +def get_lf0_cwt_norm(f0s, mean, std): + uvs = list() + cont_lf0_lpfs = list() + cont_lf0_lpf_norms = list() + Wavelet_lf0s = list() + Wavelet_lf0s_norm = list() + scaless = list() + + means = list() + stds = list() + for f0 in f0s: + uv, cont_lf0_lpf = get_cont_lf0(f0) + cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std + + Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) # [560,10] + Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0) # [560,10],[1,10],[1,10] + + Wavelet_lf0s_norm.append(Wavelet_lf0_norm) + uvs.append(uv) + cont_lf0_lpfs.append(cont_lf0_lpf) + cont_lf0_lpf_norms.append(cont_lf0_lpf_norm) + Wavelet_lf0s.append(Wavelet_lf0) + scaless.append(scales) + means.append(mean_scale) + stds.append(std_scale) + + return Wavelet_lf0s_norm, scaless, means, stds + + +def inverse_cwt_torch(Wavelet_lf0, scales): + import torch + b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5)) + lf0_rec = Wavelet_lf0 * b + lf0_rec_sum = lf0_rec.sum(-1) + lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True) + return lf0_rec_sum + + +def inverse_cwt(Wavelet_lf0, scales): + b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5)) + lf0_rec = Wavelet_lf0 * b + lf0_rec_sum = lf0_rec.sum(-1) + lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True) + return lf0_rec_sum + + +def cwt2f0(cwt_spec, mean, std, cwt_scales): + assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3 + import torch + if isinstance(cwt_spec, torch.Tensor): + f0 = inverse_cwt_torch(cwt_spec, cwt_scales) + f0 = f0 * std[:, None] + mean[:, None] + f0 = f0.exp() # [B, T] + else: + f0 = inverse_cwt(cwt_spec, cwt_scales) + f0 = f0 * std[:, None] + mean[:, None] + f0 = np.exp(f0) # [B, T] + return f0 diff --git a/NeuralSeq/utils/dtw.py b/NeuralSeq/utils/dtw.py new file mode 100644 index 0000000000000000000000000000000000000000..464c4b747d792d23cf413675a47c9dddf67da134 --- /dev/null +++ b/NeuralSeq/utils/dtw.py @@ -0,0 +1,162 @@ +from numpy import array, zeros, full, argmin, inf, ndim +from scipy.spatial.distance import cdist +from math import isinf + + +def dtw(x, y, dist, warp=1, w=inf, s=1.0): + """ + Computes Dynamic Time Warping (DTW) of two sequences. + + :param array x: N1*M array + :param array y: N2*M array + :param func dist: distance used as cost measure + :param int warp: how many shifts are computed. + :param int w: window size limiting the maximal distance between indices of matched entries |i,j|. + :param float s: weight applied on off-diagonal moves of the path. As s gets larger, the warping path is increasingly biased towards the diagonal + Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path. + """ + assert len(x) + assert len(y) + assert isinf(w) or (w >= abs(len(x) - len(y))) + assert s > 0 + r, c = len(x), len(y) + if not isinf(w): + D0 = full((r + 1, c + 1), inf) + for i in range(1, r + 1): + D0[i, max(1, i - w):min(c + 1, i + w + 1)] = 0 + D0[0, 0] = 0 + else: + D0 = zeros((r + 1, c + 1)) + D0[0, 1:] = inf + D0[1:, 0] = inf + D1 = D0[1:, 1:] # view + for i in range(r): + for j in range(c): + if (isinf(w) or (max(0, i - w) <= j <= min(c, i + w))): + D1[i, j] = dist(x[i], y[j]) + C = D1.copy() + jrange = range(c) + for i in range(r): + if not isinf(w): + jrange = range(max(0, i - w), min(c, i + w + 1)) + for j in jrange: + min_list = [D0[i, j]] + for k in range(1, warp + 1): + i_k = min(i + k, r) + j_k = min(j + k, c) + min_list += [D0[i_k, j] * s, D0[i, j_k] * s] + D1[i, j] += min(min_list) + if len(x) == 1: + path = zeros(len(y)), range(len(y)) + elif len(y) == 1: + path = range(len(x)), zeros(len(x)) + else: + path = _traceback(D0) + return D1[-1, -1], C, D1, path + + +def accelerated_dtw(x, y, dist, warp=1): + """ + Computes Dynamic Time Warping (DTW) of two sequences in a faster way. + Instead of iterating through each element and calculating each distance, + this uses the cdist function from scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html) + + :param array x: N1*M array + :param array y: N2*M array + :param string or func dist: distance parameter for cdist. When string is given, cdist uses optimized functions for the distance metrics. + If a string is passed, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'. + :param int warp: how many shifts are computed. + Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path. + """ + assert len(x) + assert len(y) + if ndim(x) == 1: + x = x.reshape(-1, 1) + if ndim(y) == 1: + y = y.reshape(-1, 1) + r, c = len(x), len(y) + D0 = zeros((r + 1, c + 1)) + D0[0, 1:] = inf + D0[1:, 0] = inf + D1 = D0[1:, 1:] + D0[1:, 1:] = cdist(x, y, dist) + C = D1.copy() + for i in range(r): + for j in range(c): + min_list = [D0[i, j]] + for k in range(1, warp + 1): + min_list += [D0[min(i + k, r), j], + D0[i, min(j + k, c)]] + D1[i, j] += min(min_list) + if len(x) == 1: + path = zeros(len(y)), range(len(y)) + elif len(y) == 1: + path = range(len(x)), zeros(len(x)) + else: + path = _traceback(D0) + return D1[-1, -1], C, D1, path + + +def _traceback(D): + i, j = array(D.shape) - 2 + p, q = [i], [j] + while (i > 0) or (j > 0): + tb = argmin((D[i, j], D[i, j + 1], D[i + 1, j])) + if tb == 0: + i -= 1 + j -= 1 + elif tb == 1: + i -= 1 + else: # (tb == 2): + j -= 1 + p.insert(0, i) + q.insert(0, j) + return array(p), array(q) + + +if __name__ == '__main__': + w = inf + s = 1.0 + if 1: # 1-D numeric + from sklearn.metrics.pairwise import manhattan_distances + import numpy as np + x = [0, 0, 1, 1, 2, 4, 2, 1, 2, 0] + x = np.array(x).reshape([-1,1,1]) + y = [1, 1, 1, 2, 2, 2, 2, 3, 2, 0] + y = np.array(y).reshape([-1,1,1]) + dist_fun = manhattan_distances + w = 1 + # s = 1.2 + elif 0: # 2-D numeric + from sklearn.metrics.pairwise import euclidean_distances + + x = [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [4, 3], [2, 3], [1, 1], [2, 2], [0, 1]] + y = [[1, 0], [1, 1], [1, 1], [2, 1], [4, 3], [4, 3], [2, 3], [3, 1], [1, 2], [1, 0]] + dist_fun = euclidean_distances + else: # 1-D list of strings + from nltk.metrics.distance import edit_distance + + # x = ['we', 'shelled', 'clams', 'for', 'the', 'chowder'] + # y = ['class', 'too'] + x = ['i', 'soon', 'found', 'myself', 'muttering', 'to', 'the', 'walls'] + y = ['see', 'drown', 'himself'] + # x = 'we talked about the situation'.split() + # y = 'we talked about the situation'.split() + dist_fun = edit_distance + dist, cost, acc, path = dtw(x, y, dist_fun, w=w, s=s) + + # Vizualize + from matplotlib import pyplot as plt + + plt.imshow(cost.T, origin='lower', cmap=plt.cm.Reds, interpolation='nearest') + plt.plot(path[0], path[1], '-o') # relation + plt.xticks(range(len(x)), x) + plt.yticks(range(len(y)), y) + plt.xlabel('x') + plt.ylabel('y') + plt.axis('tight') + if isinf(w): + plt.title('Minimum distance: {}, slope weight: {}'.format(dist, s)) + else: + plt.title('Minimum distance: {}, window widht: {}, slope weight: {}'.format(dist, w, s)) + plt.show() diff --git a/NeuralSeq/utils/hparams.py b/NeuralSeq/utils/hparams.py new file mode 100644 index 0000000000000000000000000000000000000000..c76c5cfc896308d9a84c6254a7ca00b8235e7516 --- /dev/null +++ b/NeuralSeq/utils/hparams.py @@ -0,0 +1,129 @@ +import argparse +import os +import yaml + +global_print_hparams = True +hparams = {} + + +class Args: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + self.__setattr__(k, v) + + +def override_config(old_config: dict, new_config: dict): + for k, v in new_config.items(): + if isinstance(v, dict) and k in old_config: + override_config(old_config[k], new_config[k]) + else: + old_config[k] = v + + +def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True): + if config == '' and exp_name == '': + parser = argparse.ArgumentParser(description='') + parser.add_argument('--config', type=str, default='', + help='location of the data corpus') + parser.add_argument('--exp_name', type=str, default='', help='exp_name') + parser.add_argument('-hp', '--hparams', type=str, default='', + help='location of the data corpus') + parser.add_argument('--infer', action='store_true', help='infer') + parser.add_argument('--validate', action='store_true', help='validate') + parser.add_argument('--reset', action='store_true', help='reset hparams') + parser.add_argument('--remove', action='store_true', help='remove old ckpt') + parser.add_argument('--debug', action='store_true', help='debug') + args, unknown = parser.parse_known_args() + print("| Unknow hparams: ", unknown) + else: + args = Args(config=config, exp_name=exp_name, hparams=hparams_str, + infer=False, validate=False, reset=False, debug=False, remove=False) + global hparams + assert args.config != '' or args.exp_name != '' + if args.config != '': + assert os.path.exists(args.config) + + config_chains = [] + loaded_config = set() + + def load_config(config_fn): + # deep first inheritance and avoid the second visit of one node + if not os.path.exists(config_fn): + return {} + with open(config_fn) as f: + hparams_ = yaml.safe_load(f) + loaded_config.add(config_fn) + if 'base_config' in hparams_: + ret_hparams = {} + if not isinstance(hparams_['base_config'], list): + hparams_['base_config'] = [hparams_['base_config']] + for c in hparams_['base_config']: + if c.startswith('.'): + c = f'{os.path.dirname(config_fn)}/{c}' + c = os.path.normpath(c) + if c not in loaded_config: + override_config(ret_hparams, load_config(c)) + override_config(ret_hparams, hparams_) + else: + ret_hparams = hparams_ + config_chains.append(config_fn) + return ret_hparams + + saved_hparams = {} + args_work_dir = '' + if args.exp_name != '': + args_work_dir = f'{args.exp_name}' # modified + ckpt_config_path = f'{args_work_dir}/config.yaml' + if os.path.exists(ckpt_config_path): + with open(ckpt_config_path) as f: + saved_hparams_ = yaml.safe_load(f) + if saved_hparams_ is not None: + saved_hparams.update(saved_hparams_) + hparams_ = {} + if args.config != '': + hparams_.update(load_config(args.config)) + if not args.reset: + hparams_.update(saved_hparams) + hparams_['work_dir'] = args_work_dir + + # Support config overriding in command line. Support list type config overriding. + # Examples: --hparams="a=1,b.c=2,d=[1 1 1]" + if args.hparams != "": + for new_hparam in args.hparams.split(","): + k, v = new_hparam.split("=") + v = v.strip("\'\" ") + config_node = hparams_ + for k_ in k.split(".")[:-1]: + config_node = config_node[k_] + k = k.split(".")[-1] + if v in ['True', 'False'] or type(config_node[k]) in [bool, list, dict]: + if type(config_node[k]) == list: + v = v.replace(" ", ",") + config_node[k] = eval(v) + else: + config_node[k] = type(config_node[k])(v) + if args_work_dir != '' and args.remove: + answer = input("REMOVE old checkpoint? Y/N [Default: N]: ") + if answer.lower() == "y": + remove_file(args_work_dir) + if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer: + os.makedirs(hparams_['work_dir'], exist_ok=True) + with open(ckpt_config_path, 'w') as f: + yaml.safe_dump(hparams_, f) + + hparams_['infer'] = args.infer + hparams_['debug'] = args.debug + hparams_['validate'] = args.validate + hparams_['exp_name'] = args.exp_name + global global_print_hparams + if global_hparams: + hparams.clear() + hparams.update(hparams_) + if print_hparams and global_print_hparams and global_hparams: + print('| Hparams chains: ', config_chains) + print('| Hparams: ') + for i, (k, v) in enumerate(sorted(hparams_.items())): + print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "") + print("") + global_print_hparams = False + return hparams_ \ No newline at end of file diff --git a/NeuralSeq/utils/indexed_datasets.py b/NeuralSeq/utils/indexed_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..e15632be30d6296a3c9aa80a1f351058003698b3 --- /dev/null +++ b/NeuralSeq/utils/indexed_datasets.py @@ -0,0 +1,71 @@ +import pickle +from copy import deepcopy + +import numpy as np + + +class IndexedDataset: + def __init__(self, path, num_cache=1): + super().__init__() + self.path = path + self.data_file = None + self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets'] + self.data_file = open(f"{path}.data", 'rb', buffering=-1) + self.cache = [] + self.num_cache = num_cache + + def check_index(self, i): + if i < 0 or i >= len(self.data_offsets) - 1: + raise IndexError('index out of range') + + def __del__(self): + if self.data_file: + self.data_file.close() + + def __getitem__(self, i): + self.check_index(i) + if self.num_cache > 0: + for c in self.cache: + if c[0] == i: + return c[1] + self.data_file.seek(self.data_offsets[i]) + b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i]) + item = pickle.loads(b) + if self.num_cache > 0: + self.cache = [(i, deepcopy(item))] + self.cache[:-1] + return item + + def __len__(self): + return len(self.data_offsets) - 1 + +class IndexedDatasetBuilder: + def __init__(self, path): + self.path = path + self.out_file = open(f"{path}.data", 'wb') + self.byte_offsets = [0] + + def add_item(self, item): + s = pickle.dumps(item) + bytes = self.out_file.write(s) + self.byte_offsets.append(self.byte_offsets[-1] + bytes) + + def finalize(self): + self.out_file.close() + np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets}) + + +if __name__ == "__main__": + import random + from tqdm import tqdm + ds_path = '/tmp/indexed_ds_example' + size = 100 + items = [{"a": np.random.normal(size=[10000, 10]), + "b": np.random.normal(size=[10000, 10])} for i in range(size)] + builder = IndexedDatasetBuilder(ds_path) + for i in tqdm(range(size)): + builder.add_item(items[i]) + builder.finalize() + ds = IndexedDataset(ds_path) + for i in tqdm(range(10000)): + idx = random.randint(0, size - 1) + assert (ds[idx]['a'] == items[idx]['a']).all() diff --git a/NeuralSeq/utils/multiprocess_utils.py b/NeuralSeq/utils/multiprocess_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ed3c91aeee1103213fb573806423a9e1aef097 --- /dev/null +++ b/NeuralSeq/utils/multiprocess_utils.py @@ -0,0 +1,54 @@ +import os +import traceback +from multiprocessing import Queue, Process + + +def chunked_worker(worker_id, map_func, args, results_queue=None, init_ctx_func=None): + ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None + for job_idx, arg in args: + try: + if ctx is not None: + res = map_func(*arg, ctx=ctx) + else: + res = map_func(*arg) + results_queue.put((job_idx, res)) + except: + traceback.print_exc() + results_queue.put((job_idx, None)) + +def chunked_multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, q_max_size=1000): + args = zip(range(len(args)), args) + args = list(args) + n_jobs = len(args) + if num_workers is None: + num_workers = int(os.getenv('N_PROC', os.cpu_count())) + results_queues = [] + if ordered: + for i in range(num_workers): + results_queues.append(Queue(maxsize=q_max_size // num_workers)) + else: + results_queue = Queue(maxsize=q_max_size) + for i in range(num_workers): + results_queues.append(results_queue) + workers = [] + for i in range(num_workers): + args_worker = args[i::num_workers] + p = Process(target=chunked_worker, args=( + i, map_func, args_worker, results_queues[i], init_ctx_func), daemon=True) + workers.append(p) + p.start() + for n_finished in range(n_jobs): + results_queue = results_queues[n_finished % num_workers] + job_idx, res = results_queue.get() + assert job_idx == n_finished or not ordered, (job_idx, n_finished) + yield res + for w in workers: + w.join() + w.close() + +def multiprocess_run_tqdm(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, + multithread=False, desc=None): + for i, res in tqdm(enumerate( + multiprocess_run(map_func, args, num_workers, ordered, init_ctx_func, multithread)), + total=len(args), desc=desc): + yield i, res \ No newline at end of file diff --git a/NeuralSeq/utils/os_utils.py b/NeuralSeq/utils/os_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c78a44c04eadc3feb3c35f88c8a074f59ab23778 --- /dev/null +++ b/NeuralSeq/utils/os_utils.py @@ -0,0 +1,20 @@ +import os +import subprocess + + +def link_file(from_file, to_file): + subprocess.check_call( + f'ln -s "`realpath --relative-to="{os.path.dirname(to_file)}" "{from_file}"`" "{to_file}"', shell=True) + + +def move_file(from_file, to_file): + subprocess.check_call(f'mv "{from_file}" "{to_file}"', shell=True) + + +def copy_file(from_file, to_file): + subprocess.check_call(f'cp -r "{from_file}" "{to_file}"', shell=True) + + +def remove_file(*fns): + for f in fns: + subprocess.check_call(f'rm -rf "{f}"', shell=True) \ No newline at end of file diff --git a/NeuralSeq/utils/pitch_utils.py b/NeuralSeq/utils/pitch_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f7fd166abd3a03bac5909e498669b482447435cf --- /dev/null +++ b/NeuralSeq/utils/pitch_utils.py @@ -0,0 +1,76 @@ +######### +# world +########## +import librosa +import numpy as np +import torch + +gamma = 0 +mcepInput = 3 # 0 for dB, 3 for magnitude +alpha = 0.45 +en_floor = 10 ** (-80 / 20) +FFT_SIZE = 2048 + + +f0_bin = 256 +f0_max = 1100.0 +f0_min = 50.0 +f0_mel_min = 1127 * np.log(1 + f0_min / 700) +f0_mel_max = 1127 * np.log(1 + f0_max / 700) + + +def f0_to_coarse(f0): + is_torch = isinstance(f0, torch.Tensor) + f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 + + f0_mel[f0_mel <= 1] = 1 + f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 + f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) + assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) + return f0_coarse + + +def norm_f0(f0, uv, hparams): + is_torch = isinstance(f0, torch.Tensor) + if hparams['pitch_norm'] == 'standard': + f0 = (f0 - hparams['f0_mean']) / hparams['f0_std'] + if hparams['pitch_norm'] == 'log': + f0 = torch.log2(f0) if is_torch else np.log2(f0) + if uv is not None and hparams['use_uv']: + f0[uv > 0] = 0 + return f0 + + +def norm_interp_f0(f0, hparams): + is_torch = isinstance(f0, torch.Tensor) + if is_torch: + device = f0.device + f0 = f0.data.cpu().numpy() + uv = f0 == 0 + f0 = norm_f0(f0, uv, hparams) + if sum(uv) == len(f0): + f0[uv] = 0 + elif sum(uv) > 0: + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + uv = torch.FloatTensor(uv) + f0 = torch.FloatTensor(f0) + if is_torch: + f0 = f0.to(device) + return f0, uv + + +def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None): + if hparams['pitch_norm'] == 'standard': + f0 = f0 * hparams['f0_std'] + hparams['f0_mean'] + if hparams['pitch_norm'] == 'log': + f0 = 2 ** f0 + if min is not None: + f0 = f0.clamp(min=min) + if max is not None: + f0 = f0.clamp(max=max) + if uv is not None and hparams['use_uv']: + f0[uv > 0] = 0 + if pitch_padding is not None: + f0[pitch_padding] = 0 + return f0 diff --git a/NeuralSeq/utils/pl_utils.py b/NeuralSeq/utils/pl_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..76a94ed6abe22e349c51c49afdbf052d52b8d98b --- /dev/null +++ b/NeuralSeq/utils/pl_utils.py @@ -0,0 +1,1618 @@ +import matplotlib +from torch.nn import DataParallel +from torch.nn.parallel import DistributedDataParallel + +matplotlib.use('Agg') +import glob +import itertools +import subprocess +import threading +import traceback + +from pytorch_lightning.callbacks import GradientAccumulationScheduler +from pytorch_lightning.callbacks import ModelCheckpoint + +from functools import wraps +from torch.cuda._utils import _get_device_index +import numpy as np +import torch.optim +import torch.utils.data +import copy +import logging +import os +import re +import sys +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import tqdm +from torch.optim.optimizer import Optimizer + + +def get_a_var(obj): # pragma: no cover + if isinstance(obj, torch.Tensor): + return obj + + if isinstance(obj, list) or isinstance(obj, tuple): + for result in map(get_a_var, obj): + if isinstance(result, torch.Tensor): + return result + if isinstance(obj, dict): + for result in map(get_a_var, obj.items()): + if isinstance(result, torch.Tensor): + return result + return None + + +def data_loader(fn): + """ + Decorator to make any fx with this use the lazy property + :param fn: + :return: + """ + + wraps(fn) + attr_name = '_lazy_' + fn.__name__ + + def _get_data_loader(self): + try: + value = getattr(self, attr_name) + except AttributeError: + try: + value = fn(self) # Lazy evaluation, done only once. + if ( + value is not None and + not isinstance(value, list) and + fn.__name__ in ['test_dataloader', 'val_dataloader'] + ): + value = [value] + except AttributeError as e: + # Guard against AttributeError suppression. (Issue #142) + traceback.print_exc() + error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e) + raise RuntimeError(error) from e + setattr(self, attr_name, value) # Memoize evaluation. + return value + + return _get_data_loader + + +def parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover + r"""Applies each `module` in :attr:`modules` in parallel on arguments + contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword) + on each of :attr:`devices`. + + Args: + modules (Module): modules to be parallelized + inputs (tensor): inputs to the modules + devices (list of int or torch.device): CUDA devices + + :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and + :attr:`devices` (if given) should all have same length. Moreover, each + element of :attr:`inputs` can either be a single object as the only argument + to a module, or a collection of positional arguments. + """ + assert len(modules) == len(inputs) + if kwargs_tup is not None: + assert len(modules) == len(kwargs_tup) + else: + kwargs_tup = ({},) * len(modules) + if devices is not None: + assert len(modules) == len(devices) + else: + devices = [None] * len(modules) + devices = list(map(lambda x: _get_device_index(x, True), devices)) + lock = threading.Lock() + results = {} + grad_enabled = torch.is_grad_enabled() + + def _worker(i, module, input, kwargs, device=None): + torch.set_grad_enabled(grad_enabled) + if device is None: + device = get_a_var(input).get_device() + try: + with torch.cuda.device(device): + # this also avoids accidental slicing of `input` if it is a Tensor + if not isinstance(input, (list, tuple)): + input = (input,) + + # --------------- + # CHANGE + if module.training: + output = module.training_step(*input, **kwargs) + + elif module.testing: + output = module.test_step(*input, **kwargs) + + else: + output = module.validation_step(*input, **kwargs) + # --------------- + + with lock: + results[i] = output + except Exception as e: + with lock: + results[i] = e + + # make sure each module knows what training state it's in... + # fixes weird bug where copies are out of sync + root_m = modules[0] + for m in modules[1:]: + m.training = root_m.training + m.testing = root_m.testing + + if len(modules) > 1: + threads = [threading.Thread(target=_worker, + args=(i, module, input, kwargs, device)) + for i, (module, input, kwargs, device) in + enumerate(zip(modules, inputs, kwargs_tup, devices))] + + for thread in threads: + thread.start() + for thread in threads: + thread.join() + else: + _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0]) + + outputs = [] + for i in range(len(inputs)): + output = results[i] + if isinstance(output, Exception): + raise output + outputs.append(output) + return outputs + + +def _find_tensors(obj): # pragma: no cover + r""" + Recursively find all tensors contained in the specified object. + """ + if isinstance(obj, torch.Tensor): + return [obj] + if isinstance(obj, (list, tuple)): + return itertools.chain(*map(_find_tensors, obj)) + if isinstance(obj, dict): + return itertools.chain(*map(_find_tensors, obj.values())) + return [] + + +class DDP(DistributedDataParallel): + """ + Override the forward call in lightning so it goes to training and validation step respectively + """ + + def parallel_apply(self, replicas, inputs, kwargs): + return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) + + def forward(self, *inputs, **kwargs): # pragma: no cover + self._sync_params() + if self.device_ids: + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + if len(self.device_ids) == 1: + # -------------- + # LIGHTNING MOD + # -------------- + # normal + # output = self.module(*inputs[0], **kwargs[0]) + # lightning + if self.module.training: + output = self.module.training_step(*inputs[0], **kwargs[0]) + elif self.module.testing: + output = self.module.test_step(*inputs[0], **kwargs[0]) + else: + output = self.module.validation_step(*inputs[0], **kwargs[0]) + else: + outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs) + output = self.gather(outputs, self.output_device) + else: + # normal + output = self.module(*inputs, **kwargs) + + if torch.is_grad_enabled(): + # We'll return the output object verbatim since it is a freeform + # object. We need to find any tensors in this object, though, + # because we need to figure out which parameters were used during + # this forward pass, to ensure we short circuit reduction for any + # unused parameters. Only if `find_unused_parameters` is set. + if self.find_unused_parameters: + self.reducer.prepare_for_backward(list(_find_tensors(output))) + else: + self.reducer.prepare_for_backward([]) + return output + + +class DP(DataParallel): + """ + Override the forward call in lightning so it goes to training and validation step respectively + """ + + def forward(self, *inputs, **kwargs): + if not self.device_ids: + return self.module(*inputs, **kwargs) + + for t in itertools.chain(self.module.parameters(), self.module.buffers()): + if t.device != self.src_device_obj: + raise RuntimeError("module must have its parameters and buffers " + "on device {} (device_ids[0]) but found one of " + "them on device: {}".format(self.src_device_obj, t.device)) + + inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) + if len(self.device_ids) == 1: + # lightning + if self.module.training: + return self.module.training_step(*inputs[0], **kwargs[0]) + elif self.module.testing: + return self.module.test_step(*inputs[0], **kwargs[0]) + else: + return self.module.validation_step(*inputs[0], **kwargs[0]) + + replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) + outputs = self.parallel_apply(replicas, inputs, kwargs) + return self.gather(outputs, self.output_device) + + def parallel_apply(self, replicas, inputs, kwargs): + return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) + + +class GradientAccumulationScheduler: + def __init__(self, scheduling: dict): + if scheduling == {}: # empty dict error + raise TypeError("Empty dict cannot be interpreted correct") + + for key in scheduling.keys(): + if not isinstance(key, int) or not isinstance(scheduling[key], int): + raise TypeError("All epoches and accumulation factor must be integers") + + minimal_epoch = min(scheduling.keys()) + if minimal_epoch < 1: + msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct" + raise IndexError(msg) + elif minimal_epoch != 1: # if user didnt define first epoch accumulation factor + scheduling.update({1: 1}) + + self.scheduling = scheduling + self.epochs = sorted(scheduling.keys()) + + def on_epoch_begin(self, epoch, trainer): + epoch += 1 # indexing epochs from 1 + for i in reversed(range(len(self.epochs))): + if epoch >= self.epochs[i]: + trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i]) + break + + +class LatestModelCheckpoint(ModelCheckpoint): + def __init__(self, filepath, monitor='val_loss', verbose=0, num_ckpt_keep=5, + save_weights_only=False, mode='auto', period=1, prefix='model', save_best=True): + super(ModelCheckpoint, self).__init__() + self.monitor = monitor + self.verbose = verbose + self.filepath = filepath + os.makedirs(filepath, exist_ok=True) + self.num_ckpt_keep = num_ckpt_keep + self.save_best = save_best + self.save_weights_only = save_weights_only + self.period = period + self.epochs_since_last_check = 0 + self.prefix = prefix + self.best_k_models = {} + # {filename: monitor} + self.kth_best_model = '' + self.save_top_k = 1 + self.task = None + if mode == 'min': + self.monitor_op = np.less + self.best = np.Inf + self.mode = 'min' + elif mode == 'max': + self.monitor_op = np.greater + self.best = -np.Inf + self.mode = 'max' + else: + if 'acc' in self.monitor or self.monitor.startswith('fmeasure'): + self.monitor_op = np.greater + self.best = -np.Inf + self.mode = 'max' + else: + self.monitor_op = np.less + self.best = np.Inf + self.mode = 'min' + if os.path.exists(f'{self.filepath}/best_valid.npy'): + self.best = np.load(f'{self.filepath}/best_valid.npy')[0] + + def get_all_ckpts(self): + return sorted(glob.glob(f'{self.filepath}/{self.prefix}_ckpt_steps_*.ckpt'), + key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0])) + + def on_epoch_end(self, epoch, logs=None): + logs = logs or {} + self.epochs_since_last_check += 1 + best_filepath = f'{self.filepath}/{self.prefix}_ckpt_best.pt' + if self.epochs_since_last_check >= self.period: + self.epochs_since_last_check = 0 + filepath = f'{self.filepath}/{self.prefix}_ckpt_steps_{self.task.global_step}.ckpt' + if self.verbose > 0: + logging.info(f'Epoch {epoch:05d}@{self.task.global_step}: saving model to {filepath}') + self._save_model(filepath) + for old_ckpt in self.get_all_ckpts()[self.num_ckpt_keep:]: + subprocess.check_call(f'rm -rf "{old_ckpt}"', shell=True) + if self.verbose > 0: + logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}') + current = logs.get(self.monitor) + if current is not None and self.save_best: + if self.monitor_op(current, self.best): + self.best = current + if self.verbose > 0: + logging.info( + f'Epoch {epoch:05d}@{self.task.global_step}: {self.monitor} reached' + f' {current:0.5f} (best {self.best:0.5f}), saving model to' + f' {best_filepath} as top 1') + self._save_model(best_filepath) + np.save(f'{self.filepath}/best_valid.npy', [self.best]) + + +class BaseTrainer: + def __init__( + self, + logger=True, + checkpoint_callback=True, + default_save_path=None, + gradient_clip_val=0, + process_position=0, + gpus=-1, + log_gpu_memory=None, + show_progress_bar=True, + track_grad_norm=-1, + check_val_every_n_epoch=1, + accumulate_grad_batches=1, + max_updates=1000, + min_epochs=1, + val_check_interval=1.0, + log_save_interval=100, + row_log_interval=10, + print_nan_grads=False, + weights_summary='full', + num_sanity_val_steps=5, + resume_from_checkpoint=None, + ): + self.log_gpu_memory = log_gpu_memory + self.gradient_clip_val = gradient_clip_val + self.check_val_every_n_epoch = check_val_every_n_epoch + self.track_grad_norm = track_grad_norm + self.on_gpu = True if (gpus and torch.cuda.is_available()) else False + self.process_position = process_position + self.weights_summary = weights_summary + self.max_updates = max_updates + self.min_epochs = min_epochs + self.num_sanity_val_steps = num_sanity_val_steps + self.print_nan_grads = print_nan_grads + self.resume_from_checkpoint = resume_from_checkpoint + self.default_save_path = default_save_path + + # training bookeeping + self.total_batch_idx = 0 + self.running_loss = [] + self.avg_loss = 0 + self.batch_idx = 0 + self.tqdm_metrics = {} + self.callback_metrics = {} + self.num_val_batches = 0 + self.num_training_batches = 0 + self.num_test_batches = 0 + self.get_train_dataloader = None + self.get_test_dataloaders = None + self.get_val_dataloaders = None + self.is_iterable_train_dataloader = False + + # training state + self.model = None + self.testing = False + self.disable_validation = False + self.lr_schedulers = [] + self.optimizers = None + self.global_step = 0 + self.current_epoch = 0 + self.total_batches = 0 + + # configure checkpoint callback + self.checkpoint_callback = checkpoint_callback + self.checkpoint_callback.save_function = self.save_checkpoint + self.weights_save_path = self.checkpoint_callback.filepath + + # accumulated grads + self.configure_accumulated_gradients(accumulate_grad_batches) + + # allow int, string and gpu list + self.data_parallel_device_ids = [ + int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != ''] + if len(self.data_parallel_device_ids) == 0: + self.root_gpu = None + self.on_gpu = False + else: + self.root_gpu = self.data_parallel_device_ids[0] + self.on_gpu = True + + # distributed backend choice + self.use_ddp = False + self.use_dp = False + self.single_gpu = False + self.distributed_backend = 'ddp' if self.num_gpus > 0 else 'dp' + self.set_distributed_mode(self.distributed_backend) + + self.proc_rank = 0 + self.world_size = 1 + self.node_rank = 0 + + # can't init progress bar here because starting a new process + # means the progress_bar won't survive pickling + self.show_progress_bar = show_progress_bar + + # logging + self.log_save_interval = log_save_interval + self.val_check_interval = val_check_interval + self.logger = logger + self.logger.rank = 0 + self.row_log_interval = row_log_interval + + @property + def num_gpus(self): + gpus = self.data_parallel_device_ids + if gpus is None: + return 0 + else: + return len(gpus) + + @property + def data_parallel(self): + return self.use_dp or self.use_ddp + + def get_model(self): + is_dp_module = isinstance(self.model, (DDP, DP)) + model = self.model.module if is_dp_module else self.model + return model + + # ----------------------------- + # MODEL TRAINING + # ----------------------------- + def fit(self, model): + if self.use_ddp: + mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,)) + else: + model.model = model.build_model() + if not self.testing: + self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers()) + if self.use_dp: + model.cuda(self.root_gpu) + model = DP(model, device_ids=self.data_parallel_device_ids) + elif self.single_gpu: + model.cuda(self.root_gpu) + self.run_pretrain_routine(model) + return 1 + + def init_optimizers(self, optimizers): + + # single optimizer + if isinstance(optimizers, Optimizer): + return [optimizers], [] + + # two lists + elif len(optimizers) == 2 and isinstance(optimizers[0], list): + optimizers, lr_schedulers = optimizers + return optimizers, lr_schedulers + + # single list or tuple + elif isinstance(optimizers, list) or isinstance(optimizers, tuple): + return optimizers, [] + + def run_pretrain_routine(self, model): + """Sanity check a few things before starting actual training. + + :param model: + """ + ref_model = model + if self.data_parallel: + ref_model = model.module + + # give model convenience properties + ref_model.trainer = self + + # set local properties on the model + self.copy_trainer_model_properties(ref_model) + + # link up experiment object + if self.logger is not None: + ref_model.logger = self.logger + self.logger.save() + + if self.use_ddp: + dist.barrier() + + # set up checkpoint callback + # self.configure_checkpoint_callback() + + # transfer data loaders from model + self.get_dataloaders(ref_model) + + # track model now. + # if cluster resets state, the model will update with the saved weights + self.model = model + + # restore training and model before hpc call + self.restore_weights(model) + + # when testing requested only run test and return + if self.testing: + self.run_evaluation(test=True) + return + + # check if we should run validation during training + self.disable_validation = self.num_val_batches == 0 + + # run tiny validation (if validation defined) + # to make sure program won't crash during val + ref_model.on_sanity_check_start() + ref_model.on_train_start() + if not self.disable_validation and self.num_sanity_val_steps > 0: + # init progress bars for validation sanity check + pbar = tqdm.tqdm(desc='Validation sanity check', + total=self.num_sanity_val_steps * len(self.get_val_dataloaders()), + leave=False, position=2 * self.process_position, + disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch') + self.main_progress_bar = pbar + # dummy validation progress bar + self.val_progress_bar = tqdm.tqdm(disable=True) + + self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing) + + # close progress bars + self.main_progress_bar.close() + self.val_progress_bar.close() + + # init progress bar + pbar = tqdm.tqdm(leave=True, position=2 * self.process_position, + disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch', + file=sys.stdout) + self.main_progress_bar = pbar + + # clear cache before training + if self.on_gpu: + torch.cuda.empty_cache() + + # CORE TRAINING LOOP + self.train() + + def test(self, model): + self.testing = True + self.fit(model) + + @property + def training_tqdm_dict(self): + tqdm_dict = { + 'step': '{}'.format(self.global_step), + } + tqdm_dict.update(self.tqdm_metrics) + return tqdm_dict + + # -------------------- + # restore ckpt + # -------------------- + def restore_weights(self, model): + """ + To restore weights we have two cases. + First, attempt to restore hpc weights. If successful, don't restore + other weights. + + Otherwise, try to restore actual weights + :param model: + :return: + """ + # clear cache before restore + if self.on_gpu: + torch.cuda.empty_cache() + + if self.resume_from_checkpoint is not None: + self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu) + else: + # restore weights if same exp version + self.restore_state_if_checkpoint_exists(model) + + # wait for all models to restore weights + if self.use_ddp: + # wait for all processes to catch up + dist.barrier() + + # clear cache after restore + if self.on_gpu: + torch.cuda.empty_cache() + + def restore_state_if_checkpoint_exists(self, model): + did_restore = False + + # do nothing if there's not dir or callback + no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback) + if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath): + return did_restore + + # restore trainer state and model if there is a weight for this experiment + last_steps = -1 + last_ckpt_name = None + + # find last epoch + checkpoints = os.listdir(self.checkpoint_callback.filepath) + for name in checkpoints: + if '.ckpt' in name and not name.endswith('part'): + if 'steps_' in name: + steps = name.split('steps_')[1] + steps = int(re.sub('[^0-9]', '', steps)) + + if steps > last_steps: + last_steps = steps + last_ckpt_name = name + + # restore last checkpoint + if last_ckpt_name is not None: + last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name) + self.restore(last_ckpt_path, self.on_gpu) + logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}') + did_restore = True + + return did_restore + + def restore(self, checkpoint_path, on_gpu): + checkpoint = torch.load(checkpoint_path, map_location='cpu') + + # load model state + model = self.get_model() + + # load the state_dict on the model automatically + model.load_state_dict(checkpoint['state_dict'], strict=False) + if on_gpu: + model.cuda(self.root_gpu) + # load training state (affects trainer only) + self.restore_training_state(checkpoint) + model.global_step = self.global_step + del checkpoint + + try: + if dist.is_initialized() and dist.get_rank() > 0: + return + except Exception as e: + print(e) + return + + def restore_training_state(self, checkpoint): + """ + Restore trainer state. + Model will get its change to update + :param checkpoint: + :return: + """ + if self.checkpoint_callback is not None and self.checkpoint_callback is not False: + self.checkpoint_callback.best = checkpoint['checkpoint_callback_best'] + + self.global_step = checkpoint['global_step'] + self.current_epoch = checkpoint['epoch'] + + if self.testing: + return + + # restore the optimizers + optimizer_states = checkpoint['optimizer_states'] + for optimizer, opt_state in zip(self.optimizers, optimizer_states): + if optimizer is None: + return + optimizer.load_state_dict(opt_state) + + # move optimizer to GPU 1 weight at a time + # avoids OOM + if self.root_gpu is not None: + for state in optimizer.state.values(): + for k, v in state.items(): + if isinstance(v, torch.Tensor): + state[k] = v.cuda(self.root_gpu) + + # restore the lr schedulers + lr_schedulers = checkpoint['lr_schedulers'] + for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers): + scheduler.load_state_dict(lrs_state) + + # -------------------- + # MODEL SAVE CHECKPOINT + # -------------------- + def _atomic_save(self, checkpoint, filepath): + """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints. + + This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once + saving is finished. + + Args: + checkpoint (object): The object to save. + Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save`` + accepts. + filepath (str|pathlib.Path): The path to which the checkpoint will be saved. + This points to the file that the checkpoint will be stored in. + """ + tmp_path = str(filepath) + ".part" + torch.save(checkpoint, tmp_path) + os.replace(tmp_path, filepath) + + def save_checkpoint(self, filepath): + checkpoint = self.dump_checkpoint() + self._atomic_save(checkpoint, filepath) + + def dump_checkpoint(self): + + checkpoint = { + 'epoch': self.current_epoch, + 'global_step': self.global_step + } + + if self.checkpoint_callback is not None and self.checkpoint_callback is not False: + checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best + + # save optimizers + optimizer_states = [] + for i, optimizer in enumerate(self.optimizers): + if optimizer is not None: + optimizer_states.append(optimizer.state_dict()) + + checkpoint['optimizer_states'] = optimizer_states + + # save lr schedulers + lr_schedulers = [] + for i, scheduler in enumerate(self.lr_schedulers): + lr_schedulers.append(scheduler.state_dict()) + + checkpoint['lr_schedulers'] = lr_schedulers + + # add the hparams and state_dict from the model + model = self.get_model() + checkpoint['state_dict'] = model.state_dict() + # give the model a chance to add a few things + model.on_save_checkpoint(checkpoint) + + return checkpoint + + def copy_trainer_model_properties(self, model): + if isinstance(model, DP): + ref_model = model.module + elif isinstance(model, DDP): + ref_model = model.module + else: + ref_model = model + + for m in [model, ref_model]: + m.trainer = self + m.on_gpu = self.on_gpu + m.use_dp = self.use_dp + m.use_ddp = self.use_ddp + m.testing = self.testing + m.single_gpu = self.single_gpu + + def transfer_batch_to_gpu(self, batch, gpu_id): + # base case: object can be directly moved using `cuda` or `to` + if callable(getattr(batch, 'cuda', None)): + return batch.cuda(gpu_id, non_blocking=True) + + elif callable(getattr(batch, 'to', None)): + return batch.to(torch.device('cuda', gpu_id), non_blocking=True) + + # when list + elif isinstance(batch, list): + for i, x in enumerate(batch): + batch[i] = self.transfer_batch_to_gpu(x, gpu_id) + return batch + + # when tuple + elif isinstance(batch, tuple): + batch = list(batch) + for i, x in enumerate(batch): + batch[i] = self.transfer_batch_to_gpu(x, gpu_id) + return tuple(batch) + + # when dict + elif isinstance(batch, dict): + for k, v in batch.items(): + batch[k] = self.transfer_batch_to_gpu(v, gpu_id) + + return batch + + # nothing matches, return the value as is without transform + return batch + + def set_distributed_mode(self, distributed_backend): + # skip for CPU + if self.num_gpus == 0: + return + + # single GPU case + # in single gpu case we allow ddp so we can train on multiple + # nodes, 1 gpu per node + elif self.num_gpus == 1: + self.single_gpu = True + self.use_dp = False + self.use_ddp = False + self.root_gpu = 0 + self.data_parallel_device_ids = [0] + else: + if distributed_backend is not None: + self.use_dp = distributed_backend == 'dp' + self.use_ddp = distributed_backend == 'ddp' + elif distributed_backend is None: + self.use_dp = True + self.use_ddp = False + + logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}') + + def ddp_train(self, gpu_idx, model): + """ + Entry point into a DP thread + :param gpu_idx: + :param model: + :param cluster_obj: + :return: + """ + # otherwise default to node rank 0 + self.node_rank = 0 + + # show progressbar only on progress_rank 0 + self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0 + + # determine which process we are and world size + if self.use_ddp: + self.proc_rank = self.node_rank * self.num_gpus + gpu_idx + self.world_size = self.num_gpus + + # let the exp know the rank to avoid overwriting logs + if self.logger is not None: + self.logger.rank = self.proc_rank + + # set up server using proc 0's ip address + # try to init for 20 times at max in case ports are taken + # where to store ip_table + model.trainer = self + model.init_ddp_connection(self.proc_rank, self.world_size) + + # CHOOSE OPTIMIZER + # allow for lr schedulers as well + model.model = model.build_model() + if not self.testing: + self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers()) + + # MODEL + # copy model to each gpu + if self.distributed_backend == 'ddp': + torch.cuda.set_device(gpu_idx) + model.cuda(gpu_idx) + + # set model properties before going into wrapper + self.copy_trainer_model_properties(model) + + # override root GPU + self.root_gpu = gpu_idx + + if self.distributed_backend == 'ddp': + device_ids = [gpu_idx] + else: + device_ids = None + + # allow user to configure ddp + model = model.configure_ddp(model, device_ids) + + # continue training routine + self.run_pretrain_routine(model) + + def resolve_root_node_address(self, root_node): + if '[' in root_node: + name = root_node.split('[')[0] + number = root_node.split(',')[0] + if '-' in number: + number = number.split('-')[0] + + number = re.sub('[^0-9]', '', number) + root_node = name + number + + return root_node + + def log_metrics(self, metrics, grad_norm_dic, step=None): + """Logs the metric dict passed in. + + :param metrics: + :param grad_norm_dic: + """ + # added metrics by Lightning for convenience + metrics['epoch'] = self.current_epoch + + # add norms + metrics.update(grad_norm_dic) + + # turn all tensors to scalars + scalar_metrics = self.metrics_to_scalars(metrics) + + step = step if step is not None else self.global_step + # log actual metrics + if self.proc_rank == 0 and self.logger is not None: + self.logger.log_metrics(scalar_metrics, step=step) + self.logger.save() + + def add_tqdm_metrics(self, metrics): + for k, v in metrics.items(): + if type(v) is torch.Tensor: + v = v.item() + + self.tqdm_metrics[k] = v + + def metrics_to_scalars(self, metrics): + new_metrics = {} + for k, v in metrics.items(): + if isinstance(v, torch.Tensor): + v = v.item() + + if type(v) is dict: + v = self.metrics_to_scalars(v) + + new_metrics[k] = v + + return new_metrics + + def process_output(self, output, train=False): + """Reduces output according to the training mode. + + Separates loss from logging and tqdm metrics + :param output: + :return: + """ + # --------------- + # EXTRACT CALLBACK KEYS + # --------------- + # all keys not progress_bar or log are candidates for callbacks + callback_metrics = {} + for k, v in output.items(): + if k not in ['progress_bar', 'log', 'hiddens']: + callback_metrics[k] = v + + if train and self.use_dp: + num_gpus = self.num_gpus + callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus) + + for k, v in callback_metrics.items(): + if isinstance(v, torch.Tensor): + callback_metrics[k] = v.item() + + # --------------- + # EXTRACT PROGRESS BAR KEYS + # --------------- + try: + progress_output = output['progress_bar'] + + # reduce progress metrics for tqdm when using dp + if train and self.use_dp: + num_gpus = self.num_gpus + progress_output = self.reduce_distributed_output(progress_output, num_gpus) + + progress_bar_metrics = progress_output + except Exception: + progress_bar_metrics = {} + + # --------------- + # EXTRACT LOGGING KEYS + # --------------- + # extract metrics to log to experiment + try: + log_output = output['log'] + + # reduce progress metrics for tqdm when using dp + if train and self.use_dp: + num_gpus = self.num_gpus + log_output = self.reduce_distributed_output(log_output, num_gpus) + + log_metrics = log_output + except Exception: + log_metrics = {} + + # --------------- + # EXTRACT LOSS + # --------------- + # if output dict doesn't have the keyword loss + # then assume the output=loss if scalar + loss = None + if train: + try: + loss = output['loss'] + except Exception: + if type(output) is torch.Tensor: + loss = output + else: + raise RuntimeError( + 'No `loss` value in the dictionary returned from `model.training_step()`.' + ) + + # when using dp need to reduce the loss + if self.use_dp: + loss = self.reduce_distributed_output(loss, self.num_gpus) + + # --------------- + # EXTRACT HIDDEN + # --------------- + hiddens = output.get('hiddens') + + # use every metric passed in as a candidate for callback + callback_metrics.update(progress_bar_metrics) + callback_metrics.update(log_metrics) + + # convert tensors to numpy + for k, v in callback_metrics.items(): + if isinstance(v, torch.Tensor): + callback_metrics[k] = v.item() + + return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens + + def reduce_distributed_output(self, output, num_gpus): + if num_gpus <= 1: + return output + + # when using DP, we get one output per gpu + # average outputs and return + if type(output) is torch.Tensor: + return output.mean() + + for k, v in output.items(): + # recurse on nested dics + if isinstance(output[k], dict): + output[k] = self.reduce_distributed_output(output[k], num_gpus) + + # do nothing when there's a scalar + elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0: + pass + + # reduce only metrics that have the same number of gpus + elif output[k].size(0) == num_gpus: + reduced = torch.mean(output[k]) + output[k] = reduced + return output + + def clip_gradients(self): + if self.gradient_clip_val > 0: + model = self.get_model() + torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip_val) + + def print_nan_gradients(self): + model = self.get_model() + for param in model.parameters(): + if (param.grad is not None) and torch.isnan(param.grad.float()).any(): + logging.info(param, param.grad) + + def configure_accumulated_gradients(self, accumulate_grad_batches): + self.accumulate_grad_batches = None + + if isinstance(accumulate_grad_batches, dict): + self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches) + elif isinstance(accumulate_grad_batches, int): + schedule = {1: accumulate_grad_batches} + self.accumulation_scheduler = GradientAccumulationScheduler(schedule) + else: + raise TypeError("Gradient accumulation supports only int and dict types") + + def get_dataloaders(self, model): + if not self.testing: + self.init_train_dataloader(model) + self.init_val_dataloader(model) + else: + self.init_test_dataloader(model) + + if self.use_ddp: + dist.barrier() + if not self.testing: + self.get_train_dataloader() + self.get_val_dataloaders() + else: + self.get_test_dataloaders() + + def init_train_dataloader(self, model): + self.fisrt_epoch = True + self.get_train_dataloader = model.train_dataloader + if isinstance(self.get_train_dataloader(), torch.utils.data.DataLoader): + self.num_training_batches = len(self.get_train_dataloader()) + self.num_training_batches = int(self.num_training_batches) + else: + self.num_training_batches = float('inf') + self.is_iterable_train_dataloader = True + if isinstance(self.val_check_interval, int): + self.val_check_batch = self.val_check_interval + else: + self._percent_range_check('val_check_interval') + self.val_check_batch = int(self.num_training_batches * self.val_check_interval) + self.val_check_batch = max(1, self.val_check_batch) + + def init_val_dataloader(self, model): + self.get_val_dataloaders = model.val_dataloader + self.num_val_batches = 0 + if self.get_val_dataloaders() is not None: + if isinstance(self.get_val_dataloaders()[0], torch.utils.data.DataLoader): + self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders()) + self.num_val_batches = int(self.num_val_batches) + else: + self.num_val_batches = float('inf') + + def init_test_dataloader(self, model): + self.get_test_dataloaders = model.test_dataloader + if self.get_test_dataloaders() is not None: + if isinstance(self.get_test_dataloaders()[0], torch.utils.data.DataLoader): + self.num_test_batches = sum(len(dataloader) for dataloader in self.get_test_dataloaders()) + self.num_test_batches = int(self.num_test_batches) + else: + self.num_test_batches = float('inf') + + def evaluate(self, model, dataloaders, max_batches, test=False): + """Run evaluation code. + + :param model: PT model + :param dataloaders: list of PT dataloaders + :param max_batches: Scalar + :param test: boolean + :return: + """ + # enable eval mode + model.zero_grad() + model.eval() + + # copy properties for forward overrides + self.copy_trainer_model_properties(model) + + # disable gradients to save memory + torch.set_grad_enabled(False) + + if test: + self.get_model().test_start() + # bookkeeping + outputs = [] + + # run training + for dataloader_idx, dataloader in enumerate(dataloaders): + dl_outputs = [] + for batch_idx, batch in enumerate(dataloader): + + if batch is None: # pragma: no cover + continue + + # stop short when on fast_dev_run (sets max_batch=1) + if batch_idx >= max_batches: + break + + # ----------------- + # RUN EVALUATION STEP + # ----------------- + output = self.evaluation_forward(model, + batch, + batch_idx, + dataloader_idx, + test) + + # track outputs for collation + dl_outputs.append(output) + + # batch done + if test: + self.test_progress_bar.update(1) + else: + self.val_progress_bar.update(1) + outputs.append(dl_outputs) + + # with a single dataloader don't pass an array + if len(dataloaders) == 1: + outputs = outputs[0] + + # give model a chance to do something with the outputs (and method defined) + model = self.get_model() + if test: + eval_results_ = model.test_end(outputs) + else: + eval_results_ = model.validation_end(outputs) + eval_results = eval_results_ + + # enable train mode again + model.train() + + # enable gradients to save memory + torch.set_grad_enabled(True) + + return eval_results + + def run_evaluation(self, test=False): + # when testing make sure user defined a test step + model = self.get_model() + model.on_pre_performance_check() + + # select dataloaders + if test: + dataloaders = self.get_test_dataloaders() + max_batches = self.num_test_batches + else: + # val + dataloaders = self.get_val_dataloaders() + max_batches = self.num_val_batches + + # init validation or test progress bar + # main progress bar will already be closed when testing so initial position is free + position = 2 * self.process_position + (not test) + desc = 'Testing' if test else 'Validating' + pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position, + disable=not self.show_progress_bar, dynamic_ncols=True, + unit='batch', file=sys.stdout) + setattr(self, f'{"test" if test else "val"}_progress_bar', pbar) + + # run evaluation + eval_results = self.evaluate(self.model, + dataloaders, + max_batches, + test) + if eval_results is not None: + _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output( + eval_results) + + # add metrics to prog bar + self.add_tqdm_metrics(prog_bar_metrics) + + # log metrics + self.log_metrics(log_metrics, {}) + + # track metrics for callbacks + self.callback_metrics.update(callback_metrics) + + # hook + model.on_post_performance_check() + + # add model specific metrics + tqdm_metrics = self.training_tqdm_dict + if not test: + self.main_progress_bar.set_postfix(**tqdm_metrics) + + # close progress bar + if test: + self.test_progress_bar.close() + else: + self.val_progress_bar.close() + + # model checkpointing + if self.proc_rank == 0 and self.checkpoint_callback is not None and not test: + self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, + logs=self.callback_metrics) + + def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False): + # make dataloader_idx arg in validation_step optional + args = [batch, batch_idx] + + if test and len(self.get_test_dataloaders()) > 1: + args.append(dataloader_idx) + + elif not test and len(self.get_val_dataloaders()) > 1: + args.append(dataloader_idx) + + # handle DP, DDP forward + if self.use_ddp or self.use_dp: + output = model(*args) + return output + + # single GPU + if self.single_gpu: + # for single GPU put inputs on gpu manually + root_gpu = 0 + if isinstance(self.data_parallel_device_ids, list): + root_gpu = self.data_parallel_device_ids[0] + batch = self.transfer_batch_to_gpu(batch, root_gpu) + args[0] = batch + + # CPU + if test: + output = model.test_step(*args) + else: + output = model.validation_step(*args) + + return output + + def train(self): + model = self.get_model() + # run all epochs + for epoch in range(self.current_epoch, 1000000): + # set seed for distributed sampler (enables shuffling for each epoch) + if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'): + self.get_train_dataloader().sampler.set_epoch(epoch) + + # get model + model = self.get_model() + + # update training progress in trainer and model + model.current_epoch = epoch + self.current_epoch = epoch + + total_val_batches = 0 + if not self.disable_validation: + # val can be checked multiple times in epoch + is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0 + val_checks_per_epoch = self.num_training_batches // self.val_check_batch + val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0 + total_val_batches = self.num_val_batches * val_checks_per_epoch + + # total batches includes multiple val checks + self.total_batches = self.num_training_batches + total_val_batches + self.batch_loss_value = 0 # accumulated grads + + if self.is_iterable_train_dataloader: + # for iterable train loader, the progress bar never ends + num_iterations = None + else: + num_iterations = self.total_batches + + # reset progress bar + # .reset() doesn't work on disabled progress bar so we should check + desc = f'Epoch {epoch + 1}' if not self.is_iterable_train_dataloader else '' + self.main_progress_bar.set_description(desc) + + # changing gradient according accumulation_scheduler + self.accumulation_scheduler.on_epoch_begin(epoch, self) + + # ----------------- + # RUN TNG EPOCH + # ----------------- + self.run_training_epoch() + + # update LR schedulers + if self.lr_schedulers is not None: + for lr_scheduler in self.lr_schedulers: + lr_scheduler.step(epoch=self.current_epoch) + + self.main_progress_bar.close() + + model.on_train_end() + + if self.logger is not None: + self.logger.finalize("success") + + def run_training_epoch(self): + # before epoch hook + if self.is_function_implemented('on_epoch_start'): + model = self.get_model() + model.on_epoch_start() + + # run epoch + for batch_idx, batch in enumerate(self.get_train_dataloader()): + # stop epoch if we limited the number of training batches + if batch_idx >= self.num_training_batches: + break + + self.batch_idx = batch_idx + + model = self.get_model() + model.global_step = self.global_step + + # --------------- + # RUN TRAIN STEP + # --------------- + output = self.run_training_batch(batch, batch_idx) + batch_result, grad_norm_dic, batch_step_metrics = output + + # when returning -1 from train_step, we end epoch early + early_stop_epoch = batch_result == -1 + + # --------------- + # RUN VAL STEP + # --------------- + should_check_val = ( + not self.disable_validation and self.global_step % self.val_check_batch == 0 and not self.fisrt_epoch) + self.fisrt_epoch = False + + if should_check_val: + self.run_evaluation(test=self.testing) + + # when logs should be saved + should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch + if should_save_log: + if self.proc_rank == 0 and self.logger is not None: + self.logger.save() + + # when metrics should be logged + should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch + if should_log_metrics: + # logs user requested information to logger + self.log_metrics(batch_step_metrics, grad_norm_dic) + + self.global_step += 1 + self.total_batch_idx += 1 + + # end epoch early + # stop when the flag is changed or we've gone past the amount + # requested in the batches + if early_stop_epoch: + break + if self.global_step > self.max_updates: + print("| Training end..") + exit() + + # epoch end hook + if self.is_function_implemented('on_epoch_end'): + model = self.get_model() + model.on_epoch_end() + + def run_training_batch(self, batch, batch_idx): + # track grad norms + grad_norm_dic = {} + + # track all metrics for callbacks + all_callback_metrics = [] + + # track metrics to log + all_log_metrics = [] + + if batch is None: + return 0, grad_norm_dic, {} + + # hook + if self.is_function_implemented('on_batch_start'): + model_ref = self.get_model() + response = model_ref.on_batch_start(batch) + + if response == -1: + return -1, grad_norm_dic, {} + + splits = [batch] + self.hiddens = None + for split_idx, split_batch in enumerate(splits): + self.split_idx = split_idx + + # call training_step once per optimizer + for opt_idx, optimizer in enumerate(self.optimizers): + if optimizer is None: + continue + # make sure only the gradients of the current optimizer's paramaters are calculated + # in the training step to prevent dangling gradients in multiple-optimizer setup. + if len(self.optimizers) > 1: + for param in self.get_model().parameters(): + param.requires_grad = False + for group in optimizer.param_groups: + for param in group['params']: + param.requires_grad = True + + # wrap the forward step in a closure so second order methods work + def optimizer_closure(): + # forward pass + output = self.training_forward( + split_batch, batch_idx, opt_idx, self.hiddens) + + closure_loss = output[0] + progress_bar_metrics = output[1] + log_metrics = output[2] + callback_metrics = output[3] + self.hiddens = output[4] + if closure_loss is None: + return None + + # accumulate loss + # (if accumulate_grad_batches = 1 no effect) + closure_loss = closure_loss / self.accumulate_grad_batches + + # backward pass + model_ref = self.get_model() + if closure_loss.requires_grad: + model_ref.backward(closure_loss, optimizer) + + # track metrics for callbacks + all_callback_metrics.append(callback_metrics) + + # track progress bar metrics + self.add_tqdm_metrics(progress_bar_metrics) + all_log_metrics.append(log_metrics) + + # insert after step hook + if self.is_function_implemented('on_after_backward'): + model_ref = self.get_model() + model_ref.on_after_backward() + + return closure_loss + + # calculate loss + loss = optimizer_closure() + if loss is None: + continue + + # nan grads + if self.print_nan_grads: + self.print_nan_gradients() + + # track total loss for logging (avoid mem leaks) + self.batch_loss_value += loss.item() + + # gradient update with accumulated gradients + if (self.batch_idx + 1) % self.accumulate_grad_batches == 0: + + # track gradient norms when requested + if batch_idx % self.row_log_interval == 0: + if self.track_grad_norm > 0: + model = self.get_model() + grad_norm_dic = model.grad_norm( + self.track_grad_norm) + + # clip gradients + self.clip_gradients() + + # calls .step(), .zero_grad() + # override function to modify this behavior + model = self.get_model() + model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx) + + # calculate running loss for display + self.running_loss.append(self.batch_loss_value) + self.batch_loss_value = 0 + self.avg_loss = np.mean(self.running_loss[-100:]) + + # activate batch end hook + if self.is_function_implemented('on_batch_end'): + model = self.get_model() + model.on_batch_end() + + # update progress bar + self.main_progress_bar.update(1) + self.main_progress_bar.set_postfix(**self.training_tqdm_dict) + + # collapse all metrics into one dict + all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()} + + # track all metrics for callbacks + self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()}) + + return 0, grad_norm_dic, all_log_metrics + + def training_forward(self, batch, batch_idx, opt_idx, hiddens): + """ + Handle forward for each training case (distributed, single gpu, etc...) + :param batch: + :param batch_idx: + :return: + """ + # --------------- + # FORWARD + # --------------- + # enable not needing to add opt_idx to training_step + args = [batch, batch_idx, opt_idx] + + # distributed forward + if self.use_ddp or self.use_dp: + output = self.model(*args) + # single GPU forward + elif self.single_gpu: + gpu_id = 0 + if isinstance(self.data_parallel_device_ids, list): + gpu_id = self.data_parallel_device_ids[0] + batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id) + args[0] = batch + output = self.model.training_step(*args) + # CPU forward + else: + output = self.model.training_step(*args) + + # allow any mode to define training_end + model_ref = self.get_model() + output_ = model_ref.training_end(output) + if output_ is not None: + output = output_ + + # format and reduce outputs accordingly + output = self.process_output(output, train=True) + + return output + + # --------------- + # Utils + # --------------- + def is_function_implemented(self, f_name): + model = self.get_model() + f_op = getattr(model, f_name, None) + return callable(f_op) + + def _percent_range_check(self, name): + value = getattr(self, name) + msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}." + if name == "val_check_interval": + msg += " If you want to disable validation set `val_percent_check` to 0.0 instead." + + if not 0. <= value <= 1.: + raise ValueError(msg) diff --git a/NeuralSeq/utils/plot.py b/NeuralSeq/utils/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..bdca62a8cd80869c707890cd9febd39966cd3658 --- /dev/null +++ b/NeuralSeq/utils/plot.py @@ -0,0 +1,56 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch + +LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime'] + + +def spec_to_figure(spec, vmin=None, vmax=None): + if isinstance(spec, torch.Tensor): + spec = spec.cpu().numpy() + fig = plt.figure(figsize=(12, 6)) + plt.pcolor(spec.T, vmin=vmin, vmax=vmax) + return fig + + +def spec_f0_to_figure(spec, f0s, figsize=None): + max_y = spec.shape[1] + if isinstance(spec, torch.Tensor): + spec = spec.detach().cpu().numpy() + f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()} + f0s = {k: f0 / 10 for k, f0 in f0s.items()} + fig = plt.figure(figsize=(12, 6) if figsize is None else figsize) + plt.pcolor(spec.T) + for i, (k, f0) in enumerate(f0s.items()): + plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8) + plt.legend() + return fig + + +def dur_to_figure(dur_gt, dur_pred, txt): + dur_gt = dur_gt.long().cpu().numpy() + dur_pred = dur_pred.long().cpu().numpy() + dur_gt = np.cumsum(dur_gt) + dur_pred = np.cumsum(dur_pred) + fig = plt.figure(figsize=(12, 6)) + for i in range(len(dur_gt)): + shift = (i % 8) + 1 + plt.text(dur_gt[i], shift, txt[i]) + plt.text(dur_pred[i], 10 + shift, txt[i]) + plt.vlines(dur_gt[i], 0, 10, colors='b') # blue is gt + plt.vlines(dur_pred[i], 10, 20, colors='r') # red is pred + return fig + + +def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None): + fig = plt.figure() + f0_gt = f0_gt.cpu().numpy() + plt.plot(f0_gt, color='r', label='gt') + if f0_cwt is not None: + f0_cwt = f0_cwt.cpu().numpy() + plt.plot(f0_cwt, color='b', label='cwt') + if f0_pred is not None: + f0_pred = f0_pred.cpu().numpy() + plt.plot(f0_pred, color='green', label='pred') + plt.legend() + return fig diff --git a/NeuralSeq/utils/text_encoder.py b/NeuralSeq/utils/text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d9e0758abc7b4e1f452481cba9715df08ceab543 --- /dev/null +++ b/NeuralSeq/utils/text_encoder.py @@ -0,0 +1,304 @@ +import re +import six +from six.moves import range # pylint: disable=redefined-builtin + +PAD = "" +EOS = "" +UNK = "" +SEG = "|" +RESERVED_TOKENS = [PAD, EOS, UNK] +NUM_RESERVED_TOKENS = len(RESERVED_TOKENS) +PAD_ID = RESERVED_TOKENS.index(PAD) # Normally 0 +EOS_ID = RESERVED_TOKENS.index(EOS) # Normally 1 +UNK_ID = RESERVED_TOKENS.index(UNK) # Normally 2 + +if six.PY2: + RESERVED_TOKENS_BYTES = RESERVED_TOKENS +else: + RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")] + +# Regular expression for unescaping token strings. +# '\u' is converted to '_' +# '\\' is converted to '\' +# '\213;' is converted to unichr(213) +_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);") +_ESCAPE_CHARS = set(u"\\_u;0123456789") + + +def strip_ids(ids, ids_to_strip): + """Strip ids_to_strip from the end ids.""" + ids = list(ids) + while ids and ids[-1] in ids_to_strip: + ids.pop() + return ids + + +class TextEncoder(object): + """Base class for converting from ints to/from human readable strings.""" + + def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS): + self._num_reserved_ids = num_reserved_ids + + @property + def num_reserved_ids(self): + return self._num_reserved_ids + + def encode(self, s): + """Transform a human-readable string into a sequence of int ids. + + The ids should be in the range [num_reserved_ids, vocab_size). Ids [0, + num_reserved_ids) are reserved. + + EOS is not appended. + + Args: + s: human-readable string to be converted. + + Returns: + ids: list of integers + """ + return [int(w) + self._num_reserved_ids for w in s.split()] + + def decode(self, ids, strip_extraneous=False): + """Transform a sequence of int ids into a human-readable string. + + EOS is not expected in ids. + + Args: + ids: list of integers to be converted. + strip_extraneous: bool, whether to strip off extraneous tokens + (EOS and PAD). + + Returns: + s: human-readable string. + """ + if strip_extraneous: + ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) + return " ".join(self.decode_list(ids)) + + def decode_list(self, ids): + """Transform a sequence of int ids into a their string versions. + + This method supports transforming individual input/output ids to their + string versions so that sequence to/from text conversions can be visualized + in a human readable format. + + Args: + ids: list of integers to be converted. + + Returns: + strs: list of human-readable string. + """ + decoded_ids = [] + for id_ in ids: + if 0 <= id_ < self._num_reserved_ids: + decoded_ids.append(RESERVED_TOKENS[int(id_)]) + else: + decoded_ids.append(id_ - self._num_reserved_ids) + return [str(d) for d in decoded_ids] + + @property + def vocab_size(self): + raise NotImplementedError() + + +class ByteTextEncoder(TextEncoder): + """Encodes each byte to an id. For 8-bit strings only.""" + + def encode(self, s): + numres = self._num_reserved_ids + if six.PY2: + if isinstance(s, unicode): + s = s.encode("utf-8") + return [ord(c) + numres for c in s] + # Python3: explicitly convert to UTF-8 + return [c + numres for c in s.encode("utf-8")] + + def decode(self, ids, strip_extraneous=False): + if strip_extraneous: + ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) + numres = self._num_reserved_ids + decoded_ids = [] + int2byte = six.int2byte + for id_ in ids: + if 0 <= id_ < numres: + decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)]) + else: + decoded_ids.append(int2byte(id_ - numres)) + if six.PY2: + return "".join(decoded_ids) + # Python3: join byte arrays and then decode string + return b"".join(decoded_ids).decode("utf-8", "replace") + + def decode_list(self, ids): + numres = self._num_reserved_ids + decoded_ids = [] + int2byte = six.int2byte + for id_ in ids: + if 0 <= id_ < numres: + decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)]) + else: + decoded_ids.append(int2byte(id_ - numres)) + # Python3: join byte arrays and then decode string + return decoded_ids + + @property + def vocab_size(self): + return 2**8 + self._num_reserved_ids + + +class ByteTextEncoderWithEos(ByteTextEncoder): + """Encodes each byte to an id and appends the EOS token.""" + + def encode(self, s): + return super(ByteTextEncoderWithEos, self).encode(s) + [EOS_ID] + + +class TokenTextEncoder(TextEncoder): + """Encoder based on a user-supplied vocabulary (file or list).""" + + def __init__(self, + vocab_filename, + reverse=False, + vocab_list=None, + replace_oov=None, + num_reserved_ids=NUM_RESERVED_TOKENS): + """Initialize from a file or list, one token per line. + + Handling of reserved tokens works as follows: + - When initializing from a list, we add reserved tokens to the vocab. + - When initializing from a file, we do not add reserved tokens to the vocab. + - When saving vocab files, we save reserved tokens to the file. + + Args: + vocab_filename: If not None, the full filename to read vocab from. If this + is not None, then vocab_list should be None. + reverse: Boolean indicating if tokens should be reversed during encoding + and decoding. + vocab_list: If not None, a list of elements of the vocabulary. If this is + not None, then vocab_filename should be None. + replace_oov: If not None, every out-of-vocabulary token seen when + encoding will be replaced by this string (which must be in vocab). + num_reserved_ids: Number of IDs to save for reserved tokens like . + """ + super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids) + self._reverse = reverse + self._replace_oov = replace_oov + if vocab_filename: + self._init_vocab_from_file(vocab_filename) + else: + assert vocab_list is not None + self._init_vocab_from_list(vocab_list) + self.pad_index = self._token_to_id[PAD] + self.eos_index = self._token_to_id[EOS] + self.unk_index = self._token_to_id[UNK] + self.seg_index = self._token_to_id[SEG] if SEG in self._token_to_id else self.eos_index + + def encode(self, s): + """Converts a space-separated string of tokens to a list of ids.""" + sentence = s + tokens = sentence.strip().split() + if self._replace_oov is not None: + tokens = [t if t in self._token_to_id else self._replace_oov + for t in tokens] + ret = [self._token_to_id[tok] for tok in tokens] + return ret[::-1] if self._reverse else ret + + def decode(self, ids, strip_eos=False, strip_padding=False): + if strip_padding and self.pad() in list(ids): + pad_pos = list(ids).index(self.pad()) + ids = ids[:pad_pos] + if strip_eos and self.eos() in list(ids): + eos_pos = list(ids).index(self.eos()) + ids = ids[:eos_pos] + return " ".join(self.decode_list(ids)) + + def decode_list(self, ids): + seq = reversed(ids) if self._reverse else ids + return [self._safe_id_to_token(i) for i in seq] + + @property + def vocab_size(self): + return len(self._id_to_token) + + def __len__(self): + return self.vocab_size + + def _safe_id_to_token(self, idx): + return self._id_to_token.get(idx, "ID_%d" % idx) + + def _init_vocab_from_file(self, filename): + """Load vocab from a file. + + Args: + filename: The file to load vocabulary from. + """ + with open(filename) as f: + tokens = [token.strip() for token in f.readlines()] + + def token_gen(): + for token in tokens: + yield token + + self._init_vocab(token_gen(), add_reserved_tokens=False) + + def _init_vocab_from_list(self, vocab_list): + """Initialize tokens from a list of tokens. + + It is ok if reserved tokens appear in the vocab list. They will be + removed. The set of tokens in vocab_list should be unique. + + Args: + vocab_list: A list of tokens. + """ + def token_gen(): + for token in vocab_list: + if token not in RESERVED_TOKENS: + yield token + + self._init_vocab(token_gen()) + + def _init_vocab(self, token_generator, add_reserved_tokens=True): + """Initialize vocabulary with tokens from token_generator.""" + + self._id_to_token = {} + non_reserved_start_index = 0 + + if add_reserved_tokens: + self._id_to_token.update(enumerate(RESERVED_TOKENS)) + non_reserved_start_index = len(RESERVED_TOKENS) + + self._id_to_token.update( + enumerate(token_generator, start=non_reserved_start_index)) + + # _token_to_id is the reverse of _id_to_token + self._token_to_id = dict((v, k) + for k, v in six.iteritems(self._id_to_token)) + + def pad(self): + return self.pad_index + + def eos(self): + return self.eos_index + + def unk(self): + return self.unk_index + + def seg(self): + return self.seg_index + + def store_to_file(self, filename): + """Write vocab file to disk. + + Vocab files have one token per line. The file ends in a newline. Reserved + tokens are written to the vocab file as well. + + Args: + filename: Full path of the file to store the vocab to. + """ + with open(filename, "w") as f: + for i in range(len(self._id_to_token)): + f.write(self._id_to_token[i] + "\n") + + def sil_phonemes(self): + return [p for p in self._id_to_token.values() if not p[0].isalpha()] diff --git a/NeuralSeq/utils/text_norm.py b/NeuralSeq/utils/text_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..d0973cebc91e0525aeb6657e70012a1d37b5e6ff --- /dev/null +++ b/NeuralSeq/utils/text_norm.py @@ -0,0 +1,790 @@ +# coding=utf-8 +# Authors: +# 2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git) +# 2019.9 Jiayu DU +# +# requirements: +# - python 3.X +# notes: python 2.X WILL fail or produce misleading results + +import sys, os, argparse, codecs, string, re + +# ================================================================================ # +# basic constant +# ================================================================================ # +CHINESE_DIGIS = u'零一二三四五六七八九' +BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖' +BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖' +SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万' +SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬' +LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载' +LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載' +SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万' +SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬' + +ZERO_ALT = u'〇' +ONE_ALT = u'幺' +TWO_ALTS = [u'两', u'兩'] + +POSITIVE = [u'正', u'正'] +NEGATIVE = [u'负', u'負'] +POINT = [u'点', u'點'] +# PLUS = [u'加', u'加'] +# SIL = [u'杠', u'槓'] + +# 中文数字系统类型 +NUMBERING_TYPES = ['low', 'mid', 'high'] + +CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \ + '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)' +CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)' +COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \ + '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \ + '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \ + '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \ + '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \ + '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)' + +# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git) +CHINESE_PUNC_STOP = '！？｡。' +CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏' +CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP + + +# ================================================================================ # +# basic class +# ================================================================================ # +class ChineseChar(object): + """ + 中文字符 + 每个字符对应简体和繁体, + e.g. 简体 = '负', 繁体 = '負' + 转换时可转换为简体或繁体 + """ + + def __init__(self, simplified, traditional): + self.simplified = simplified + self.traditional = traditional + # self.__repr__ = self.__str__ + + def __str__(self): + return self.simplified or self.traditional or None + + def __repr__(self): + return self.__str__() + + +class ChineseNumberUnit(ChineseChar): + """ + 中文数字/数位字符 + 每个字符除繁简体外还有一个额外的大写字符 + e.g. '陆' 和 '陸' + """ + + def __init__(self, power, simplified, traditional, big_s, big_t): + super(ChineseNumberUnit, self).__init__(simplified, traditional) + self.power = power + self.big_s = big_s + self.big_t = big_t + + def __str__(self): + return '10^{}'.format(self.power) + + @classmethod + def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False): + + if small_unit: + return ChineseNumberUnit(power=index + 1, + simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1]) + elif numbering_type == NUMBERING_TYPES[0]: + return ChineseNumberUnit(power=index + 8, + simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + elif numbering_type == NUMBERING_TYPES[1]: + return ChineseNumberUnit(power=(index + 2) * 4, + simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + elif numbering_type == NUMBERING_TYPES[2]: + return ChineseNumberUnit(power=pow(2, index + 3), + simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + else: + raise ValueError( + 'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type)) + + +class ChineseNumberDigit(ChineseChar): + """ + 中文数字字符 + """ + + def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None): + super(ChineseNumberDigit, self).__init__(simplified, traditional) + self.value = value + self.big_s = big_s + self.big_t = big_t + self.alt_s = alt_s + self.alt_t = alt_t + + def __str__(self): + return str(self.value) + + @classmethod + def create(cls, i, v): + return ChineseNumberDigit(i, v[0], v[1], v[2], v[3]) + + +class ChineseMath(ChineseChar): + """ + 中文数位字符 + """ + + def __init__(self, simplified, traditional, symbol, expression=None): + super(ChineseMath, self).__init__(simplified, traditional) + self.symbol = symbol + self.expression = expression + self.big_s = simplified + self.big_t = traditional + + +CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath + + +class NumberSystem(object): + """ + 中文数字系统 + """ + pass + + +class MathSymbol(object): + """ + 用于中文数字系统的数学符号 (繁/简体), e.g. + positive = ['正', '正'] + negative = ['负', '負'] + point = ['点', '點'] + """ + + def __init__(self, positive, negative, point): + self.positive = positive + self.negative = negative + self.point = point + + def __iter__(self): + for v in self.__dict__.values(): + yield v + + +# class OtherSymbol(object): +# """ +# 其他符号 +# """ +# +# def __init__(self, sil): +# self.sil = sil +# +# def __iter__(self): +# for v in self.__dict__.values(): +# yield v + + +# ================================================================================ # +# basic utils +# ================================================================================ # +def create_system(numbering_type=NUMBERING_TYPES[1]): + """ + 根据数字系统类型返回创建相应的数字系统，默认为 mid + NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型 + low: '兆' = '亿' * '十' = $10^{9}$, '京' = '兆' * '十', etc. + mid: '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc. + high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc. + 返回对应的数字系统 + """ + + # chinese number units of '亿' and larger + all_larger_units = zip( + LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL) + larger_units = [CNU.create(i, v, numbering_type, False) + for i, v in enumerate(all_larger_units)] + # chinese number units of '十, 百, 千, 万' + all_smaller_units = zip( + SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL) + smaller_units = [CNU.create(i, v, small_unit=True) + for i, v in enumerate(all_smaller_units)] + # digis + chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS, + BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL) + digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)] + digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT + digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT + digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1] + + # symbols + positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x) + negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x) + point_cn = CM(POINT[0], POINT[1], '.', lambda x, + y: float(str(x) + '.' + str(y))) + # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y))) + system = NumberSystem() + system.units = smaller_units + larger_units + system.digits = digits + system.math = MathSymbol(positive_cn, negative_cn, point_cn) + # system.symbols = OtherSymbol(sil_cn) + return system + + +def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]): + def get_symbol(char, system): + for u in system.units: + if char in [u.traditional, u.simplified, u.big_s, u.big_t]: + return u + for d in system.digits: + if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]: + return d + for m in system.math: + if char in [m.traditional, m.simplified]: + return m + + def string2symbols(chinese_string, system): + int_string, dec_string = chinese_string, '' + for p in [system.math.point.simplified, system.math.point.traditional]: + if p in chinese_string: + int_string, dec_string = chinese_string.split(p) + break + return [get_symbol(c, system) for c in int_string], \ + [get_symbol(c, system) for c in dec_string] + + def correct_symbols(integer_symbols, system): + """ + 一百八 to 一百八十 + 一亿一千三百万 to 一亿 一千万 三百万 + """ + + if integer_symbols and isinstance(integer_symbols[0], CNU): + if integer_symbols[0].power == 1: + integer_symbols = [system.digits[1]] + integer_symbols + + if len(integer_symbols) > 1: + if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU): + integer_symbols.append( + CNU(integer_symbols[-2].power - 1, None, None, None, None)) + + result = [] + unit_count = 0 + for s in integer_symbols: + if isinstance(s, CND): + result.append(s) + unit_count = 0 + elif isinstance(s, CNU): + current_unit = CNU(s.power, None, None, None, None) + unit_count += 1 + + if unit_count == 1: + result.append(current_unit) + elif unit_count > 1: + for i in range(len(result)): + if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power: + result[-i - 1] = CNU(result[-i - 1].power + + current_unit.power, None, None, None, None) + return result + + def compute_value(integer_symbols): + """ + Compute the value. + When current unit is larger than previous unit, current unit * all previous units will be used as all previous units. + e.g. '两千万' = 2000 * 10000 not 2000 + 10000 + """ + value = [0] + last_power = 0 + for s in integer_symbols: + if isinstance(s, CND): + value[-1] = s.value + elif isinstance(s, CNU): + value[-1] *= pow(10, s.power) + if s.power > last_power: + value[:-1] = list(map(lambda v: v * + pow(10, s.power), value[:-1])) + last_power = s.power + value.append(0) + return sum(value) + + system = create_system(numbering_type) + int_part, dec_part = string2symbols(chinese_string, system) + int_part = correct_symbols(int_part, system) + int_str = str(compute_value(int_part)) + dec_str = ''.join([str(d.value) for d in dec_part]) + if dec_part: + return '{0}.{1}'.format(int_str, dec_str) + else: + return int_str + + +def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False, + traditional=False, alt_zero=False, alt_one=False, alt_two=True, + use_zeros=True, use_units=True): + def get_value(value_string, use_zeros=True): + + striped_string = value_string.lstrip('0') + + # record nothing if all zeros + if not striped_string: + return [] + + # record one digits + elif len(striped_string) == 1: + if use_zeros and len(value_string) != len(striped_string): + return [system.digits[0], system.digits[int(striped_string)]] + else: + return [system.digits[int(striped_string)]] + + # recursively record multiple digits + else: + result_unit = next(u for u in reversed( + system.units) if u.power < len(striped_string)) + result_string = value_string[:-result_unit.power] + return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:]) + + system = create_system(numbering_type) + + int_dec = number_string.split('.') + if len(int_dec) == 1: + int_string = int_dec[0] + dec_string = "" + elif len(int_dec) == 2: + int_string = int_dec[0] + dec_string = int_dec[1] + else: + raise ValueError( + "invalid input num string with more than one dot: {}".format(number_string)) + + if use_units and len(int_string) > 1: + result_symbols = get_value(int_string) + else: + result_symbols = [system.digits[int(c)] for c in int_string] + dec_symbols = [system.digits[int(c)] for c in dec_string] + if dec_string: + result_symbols += [system.math.point] + dec_symbols + + if alt_two: + liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t, + system.digits[2].big_s, system.digits[2].big_t) + for i, v in enumerate(result_symbols): + if isinstance(v, CND) and v.value == 2: + next_symbol = result_symbols[i + + 1] if i < len(result_symbols) - 1 else None + previous_symbol = result_symbols[i - 1] if i > 0 else None + if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))): + if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)): + result_symbols[i] = liang + + # if big is True, '两' will not be used and `alt_two` has no impact on output + if big: + attr_name = 'big_' + if traditional: + attr_name += 't' + else: + attr_name += 's' + else: + if traditional: + attr_name = 'traditional' + else: + attr_name = 'simplified' + + result = ''.join([getattr(s, attr_name) for s in result_symbols]) + + # if not use_zeros: + # result = result.strip(getattr(system.digits[0], attr_name)) + + if alt_zero: + result = result.replace( + getattr(system.digits[0], attr_name), system.digits[0].alt_s) + + if alt_one: + result = result.replace( + getattr(system.digits[1], attr_name), system.digits[1].alt_s) + + for i, p in enumerate(POINT): + if result.startswith(p): + return CHINESE_DIGIS[0] + result + + # ^10, 11, .., 19 + if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0], + SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \ + result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]: + result = result[1:] + + return result + + +# ================================================================================ # +# different types of rewriters +# ================================================================================ # +class Cardinal: + """ + CARDINAL类 + """ + + def __init__(self, cardinal=None, chntext=None): + self.cardinal = cardinal + self.chntext = chntext + + def chntext2cardinal(self): + return chn2num(self.chntext) + + def cardinal2chntext(self): + return num2chn(self.cardinal) + + +class Digit: + """ + DIGIT类 + """ + + def __init__(self, digit=None, chntext=None): + self.digit = digit + self.chntext = chntext + + # def chntext2digit(self): + # return chn2num(self.chntext) + + def digit2chntext(self): + return num2chn(self.digit, alt_two=False, use_units=False) + + +class TelePhone: + """ + TELEPHONE类 + """ + + def __init__(self, telephone=None, raw_chntext=None, chntext=None): + self.telephone = telephone + self.raw_chntext = raw_chntext + self.chntext = chntext + + # def chntext2telephone(self): + # sil_parts = self.raw_chntext.split('') + # self.telephone = '-'.join([ + # str(chn2num(p)) for p in sil_parts + # ]) + # return self.telephone + + def telephone2chntext(self, fixed=False): + + if fixed: + sil_parts = self.telephone.split('-') + self.raw_chntext = ''.join([ + num2chn(part, alt_two=False, use_units=False) for part in sil_parts + ]) + self.chntext = self.raw_chntext.replace('', '') + else: + sp_parts = self.telephone.strip('+').split() + self.raw_chntext = ''.join([ + num2chn(part, alt_two=False, use_units=False) for part in sp_parts + ]) + self.chntext = self.raw_chntext.replace('', '') + return self.chntext + + +class Fraction: + """ + FRACTION类 + """ + + def __init__(self, fraction=None, chntext=None): + self.fraction = fraction + self.chntext = chntext + + def chntext2fraction(self): + denominator, numerator = self.chntext.split('分之') + return chn2num(numerator) + '/' + chn2num(denominator) + + def fraction2chntext(self): + numerator, denominator = self.fraction.split('/') + return num2chn(denominator) + '分之' + num2chn(numerator) + + +class Date: + """ + DATE类 + """ + + def __init__(self, date=None, chntext=None): + self.date = date + self.chntext = chntext + + # def chntext2date(self): + # chntext = self.chntext + # try: + # year, other = chntext.strip().split('年', maxsplit=1) + # year = Digit(chntext=year).digit2chntext() + '年' + # except ValueError: + # other = chntext + # year = '' + # if other: + # try: + # month, day = other.strip().split('月', maxsplit=1) + # month = Cardinal(chntext=month).chntext2cardinal() + '月' + # except ValueError: + # day = chntext + # month = '' + # if day: + # day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1] + # else: + # month = '' + # day = '' + # date = year + month + day + # self.date = date + # return self.date + + def date2chntext(self): + date = self.date + try: + year, other = date.strip().split('年', 1) + year = Digit(digit=year).digit2chntext() + '年' + except ValueError: + other = date + year = '' + if other: + try: + month, day = other.strip().split('月', 1) + month = Cardinal(cardinal=month).cardinal2chntext() + '月' + except ValueError: + day = date + month = '' + if day: + day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1] + else: + month = '' + day = '' + chntext = year + month + day + self.chntext = chntext + return self.chntext + + +class Money: + """ + MONEY类 + """ + + def __init__(self, money=None, chntext=None): + self.money = money + self.chntext = chntext + + # def chntext2money(self): + # return self.money + + def money2chntext(self): + money = self.money + pattern = re.compile(r'(\d+(\.\d+)?)') + matchers = pattern.findall(money) + if matchers: + for matcher in matchers: + money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext()) + self.chntext = money + return self.chntext + + +class Percentage: + """ + PERCENTAGE类 + """ + + def __init__(self, percentage=None, chntext=None): + self.percentage = percentage + self.chntext = chntext + + def chntext2percentage(self): + return chn2num(self.chntext.strip().strip('百分之')) + '%' + + def percentage2chntext(self): + return '百分之' + num2chn(self.percentage.strip().strip('%')) + + +# ================================================================================ # +# NSW Normalizer +# ================================================================================ # +class NSWNormalizer: + def __init__(self, raw_text): + self.raw_text = '^' + raw_text + '$' + self.norm_text = '' + + def _particular(self): + text = self.norm_text + pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))") + matchers = pattern.findall(text) + if matchers: + # print('particular') + for matcher in matchers: + text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1) + self.norm_text = text + return self.norm_text + + def normalize(self, remove_punc=True): + text = self.raw_text + + # 规范化日期 + pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)") + matchers = pattern.findall(text) + if matchers: + # print('date') + for matcher in matchers: + text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1) + + # 规范化金钱 + pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)") + matchers = pattern.findall(text) + if matchers: + # print('money') + for matcher in matchers: + text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1) + + # 规范化固话/手机号码 + # 手机 + # http://www.jihaoba.com/news/show/13680 + # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198 + # 联通：130、131、132、156、155、186、185、176 + # 电信：133、153、189、180、181、177 + pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D") + matchers = pattern.findall(text) + if matchers: + # print('telephone') + for matcher in matchers: + text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1) + # 固话 + pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D") + matchers = pattern.findall(text) + if matchers: + # print('fixed telephone') + for matcher in matchers: + text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1) + + # 规范化分数 + pattern = re.compile(r"(\d+/\d+)") + matchers = pattern.findall(text) + if matchers: + # print('fraction') + for matcher in matchers: + text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1) + + # 规范化百分数 + text = text.replace('％', '%') + pattern = re.compile(r"(\d+(\.\d+)?%)") + matchers = pattern.findall(text) + if matchers: + # print('percentage') + for matcher in matchers: + text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1) + + # 规范化纯数+量词 + pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS) + matchers = pattern.findall(text) + if matchers: + # print('cardinal+quantifier') + for matcher in matchers: + text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) + + # 规范化数字编号 + pattern = re.compile(r"(\d{4,32})") + matchers = pattern.findall(text) + if matchers: + # print('digit') + for matcher in matchers: + text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1) + + # 规范化纯数 + pattern = re.compile(r"(\d+(\.\d+)?)") + matchers = pattern.findall(text) + if matchers: + # print('cardinal') + for matcher in matchers: + text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) + + self.norm_text = text + self._particular() + + text = self.norm_text.lstrip('^').rstrip('$') + if remove_punc: + # Punctuations removal + old_chars = CHINESE_PUNC_LIST + string.punctuation # includes all CN and EN punctuations + new_chars = ' ' * len(old_chars) + del_chars = '' + text = text.translate(str.maketrans(old_chars, new_chars, del_chars)) + return text + + +def nsw_test_case(raw_text): + print('I:' + raw_text) + print('O:' + NSWNormalizer(raw_text).normalize()) + print('') + + +def nsw_test(): + nsw_test_case('固话：0595-23865596或23880880。') + nsw_test_case('固话：0595-23865596或23880880。') + nsw_test_case('手机：+86 19859213959或15659451527。') + nsw_test_case('分数：32477/76391。') + nsw_test_case('百分数：80.03%。') + nsw_test_case('编号：31520181154418。') + nsw_test_case('纯数：2983.07克或12345.60米。') + nsw_test_case('日期：1999年2月20日或09年3月15号。') + nsw_test_case('金钱：12块5，34.5元，20.1万') + nsw_test_case('特殊：O2O或B2C。') + nsw_test_case('3456万吨') + nsw_test_case('2938个') + nsw_test_case('938') + nsw_test_case('今天吃了115个小笼包231个馒头') + nsw_test_case('有62％的概率') + + +if __name__ == '__main__': + # nsw_test() + + p = argparse.ArgumentParser() + p.add_argument('ifile', help='input filename, assume utf-8 encoding') + p.add_argument('ofile', help='output filename') + p.add_argument('--to_upper', action='store_true', help='convert to upper case') + p.add_argument('--to_lower', action='store_true', help='convert to lower case') + p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.") + p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines') + args = p.parse_args() + + ifile = codecs.open(args.ifile, 'r', 'utf8') + ofile = codecs.open(args.ofile, 'w+', 'utf8') + + n = 0 + for l in ifile: + key = '' + text = '' + if args.has_key: + cols = l.split(maxsplit=1) + key = cols[0] + if len(cols) == 2: + text = cols[1] + else: + text = '' + else: + text = l + + # cases + if args.to_upper and args.to_lower: + sys.stderr.write('text norm: to_upper OR to_lower?') + exit(1) + if args.to_upper: + text = text.upper() + if args.to_lower: + text = text.lower() + + # NSW(Non-Standard-Word) normalization + text = NSWNormalizer(text).normalize() + + # + if args.has_key: + ofile.write(key + '\t' + text) + else: + ofile.write(text) + + n += 1 + if n % args.log_interval == 0: + sys.stderr.write("text norm: {} lines done.\n".format(n)) + + sys.stderr.write("text norm: {} lines done in total.\n".format(n)) + + ifile.close() + ofile.close() diff --git a/NeuralSeq/utils/training_utils.py b/NeuralSeq/utils/training_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..409b15388790b1aadb24632313bdd1f41b4b06ac --- /dev/null +++ b/NeuralSeq/utils/training_utils.py @@ -0,0 +1,27 @@ +from utils.hparams import hparams + + +class RSQRTSchedule(object): + def __init__(self, optimizer): + super().__init__() + self.optimizer = optimizer + self.constant_lr = hparams['lr'] + self.warmup_updates = hparams['warmup_updates'] + self.hidden_size = hparams['hidden_size'] + self.lr = hparams['lr'] + for param_group in optimizer.param_groups: + param_group['lr'] = self.lr + self.step(0) + + def step(self, num_updates): + constant_lr = self.constant_lr + warmup = min(num_updates / self.warmup_updates, 1.0) + rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5 + rsqrt_hidden = self.hidden_size ** -0.5 + self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7) + for param_group in self.optimizer.param_groups: + param_group['lr'] = self.lr + return self.lr + + def get_lr(self): + return self.optimizer.param_groups[0]['lr'] diff --git a/NeuralSeq/utils/tts_utils.py b/NeuralSeq/utils/tts_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..869b8b4bc495f119a4f09fa8436b6b6aec02a81d --- /dev/null +++ b/NeuralSeq/utils/tts_utils.py @@ -0,0 +1,398 @@ +from collections import defaultdict +import torch +import torch.nn.functional as F + + +def make_positions(tensor, padding_idx): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + # The series of casts and type-conversions here are carefully + # balanced to both work with ONNX export and XLA. In particular XLA + # prefers ints, cumsum defaults to output longs, and ONNX doesn't know + # how to handle the dtype kwarg in cumsum. + mask = tensor.ne(padding_idx).int() + return ( + torch.cumsum(mask, dim=1).type_as(mask) * mask + ).long() + padding_idx + + +def softmax(x, dim): + return F.softmax(x, dim=dim, dtype=torch.float32) + + +def sequence_mask(lengths, maxlen, dtype=torch.bool): + if maxlen is None: + maxlen = lengths.max() + mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t() + mask.type(dtype) + return mask + + +INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0) + + +def _get_full_incremental_state_key(module_instance, key): + module_name = module_instance.__class__.__name__ + + # assign a unique ID to each module instance, so that incremental state is + # not shared across module instances + if not hasattr(module_instance, '_instance_id'): + INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1 + module_instance._instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name] + + return '{}.{}.{}'.format(module_name, module_instance._instance_id, key) + + +def get_incremental_state(module, incremental_state, key): + """Helper for getting incremental state for an nn.Module.""" + full_key = _get_full_incremental_state_key(module, key) + if incremental_state is None or full_key not in incremental_state: + return None + return incremental_state[full_key] + + +def set_incremental_state(module, incremental_state, key, value): + """Helper for setting incremental state for an nn.Module.""" + if incremental_state is not None: + full_key = _get_full_incremental_state_key(module, key) + incremental_state[full_key] = value + + +def fill_with_neg_inf(t): + """FP16-compatible function that fills a tensor with -inf.""" + return t.float().fill_(float('-inf')).type_as(t) + + +def fill_with_neg_inf2(t): + """FP16-compatible function that fills a tensor with -inf.""" + return t.float().fill_(-1e8).type_as(t) + + +def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None): + ''' + attn: bs x L_t x L_s + ''' + if src_padding_mask is not None: + attn = attn * (1 - src_padding_mask.float())[:, None, :] + + if tgt_padding_mask is not None: + attn = attn * (1 - tgt_padding_mask.float())[:, :, None] + + focus_rate = attn.max(-1).values.sum(-1) + focus_rate = focus_rate / attn.sum(-1).sum(-1) + return focus_rate + + +def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None): + ''' + attn: bs x L_t x L_s + ''' + src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False) + if src_padding_mask is not None: + src_mask |= src_padding_mask + if src_seg_mask is not None: + src_mask |= src_seg_mask + + attn = attn * (1 - src_mask.float())[:, None, :] + if tgt_padding_mask is not None: + attn = attn * (1 - tgt_padding_mask.float())[:, :, None] + + phone_coverage_rate = attn.max(1).values.sum(-1) + # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1) + phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1) + return phone_coverage_rate + + +def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None, + band_mask_factor=5, band_width=50): + ''' + attn: bx x L_t x L_s + attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens + + diagonal: y=k*x (k=attn_ks, x:output, y:input) + 1 0 0 + 0 1 0 + 0 0 1 + y>=k*(x-width) and y<=k*(x+width):1 + else:0 + ''' + # width = min(target_len/band_mask_factor, 50) + width1 = target_len / band_mask_factor + width2 = target_len.new(target_len.size()).fill_(band_width) + width = torch.where(width1 < width2, width1, width2).float() + base = torch.ones(attn.size()).to(attn.device) + zero = torch.zeros(attn.size()).to(attn.device) + x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base + y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base + cond = (y - attn_ks[:, None, None] * x) + cond1 = cond + attn_ks[:, None, None] * width[:, None, None] + cond2 = cond - attn_ks[:, None, None] * width[:, None, None] + mask1 = torch.where(cond1 < 0, zero, base) + mask2 = torch.where(cond2 > 0, zero, base) + mask = mask1 * mask2 + + if src_padding_mask is not None: + attn = attn * (1 - src_padding_mask.float())[:, None, :] + if tgt_padding_mask is not None: + attn = attn * (1 - tgt_padding_mask.float())[:, :, None] + + diagonal_attn = attn * mask + diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1) + return diagonal_focus_rate, mask + + +def select_attn(attn_logits, type='best'): + """ + + :param attn_logits: [n_layers, B, n_head, T_sp, T_txt] + :return: + """ + encdec_attn = torch.stack(attn_logits, 0).transpose(1, 2) + # [n_layers * n_head, B, T_sp, T_txt] + encdec_attn = (encdec_attn.reshape([-1, *encdec_attn.shape[2:]])).softmax(-1) + if type == 'best': + indices = encdec_attn.max(-1).values.sum(-1).argmax(0) + encdec_attn = encdec_attn.gather( + 0, indices[None, :, None, None].repeat(1, 1, encdec_attn.size(-2), encdec_attn.size(-1)))[0] + return encdec_attn + elif type == 'mean': + return encdec_attn.mean(0) + + +def make_pad_mask(lengths, xs=None, length_dim=-1): + """Make mask tensor containing indices of padded part. + Args: + lengths (LongTensor or List): Batch of lengths (B,). + xs (Tensor, optional): The reference tensor. + If set, masks will be the same shape as this tensor. + length_dim (int, optional): Dimension indicator of the above tensor. + See the example. + Returns: + Tensor: Mask tensor containing indices of padded part. + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (including 1.2) + Examples: + With only lengths. + >>> lengths = [5, 3, 2] + >>> make_non_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + With the reference tensor. + >>> xs = torch.zeros((3, 2, 4)) + >>> make_pad_mask(lengths, xs) + tensor([[[0, 0, 0, 0], + [0, 0, 0, 0]], + [[0, 0, 0, 1], + [0, 0, 0, 1]], + [[0, 0, 1, 1], + [0, 0, 1, 1]]], dtype=torch.uint8) + >>> xs = torch.zeros((3, 2, 6)) + >>> make_pad_mask(lengths, xs) + tensor([[[0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1]], + [[0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1]], + [[0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) + With the reference tensor and dimension indicator. + >>> xs = torch.zeros((3, 6, 6)) + >>> make_pad_mask(lengths, xs, 1) + tensor([[[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1]], + [[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1]], + [[0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8) + >>> make_pad_mask(lengths, xs, 2) + tensor([[[0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 1]], + [[0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1], + [0, 0, 0, 1, 1, 1]], + [[0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8) + """ + if length_dim == 0: + raise ValueError("length_dim cannot be 0: {}".format(length_dim)) + + if not isinstance(lengths, list): + lengths = lengths.tolist() + bs = int(len(lengths)) + if xs is None: + maxlen = int(max(lengths)) + else: + maxlen = xs.size(length_dim) + + seq_range = torch.arange(0, maxlen, dtype=torch.int64) + seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen) + seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + + if xs is not None: + assert xs.size(0) == bs, (xs.size(0), bs) + + if length_dim < 0: + length_dim = xs.dim() + length_dim + # ind = (:, None, ..., None, :, , None, ..., None) + ind = tuple( + slice(None) if i in (0, length_dim) else None for i in range(xs.dim()) + ) + mask = mask[ind].expand_as(xs).to(xs.device) + return mask + + +def make_non_pad_mask(lengths, xs=None, length_dim=-1): + """Make mask tensor containing indices of non-padded part. + Args: + lengths (LongTensor or List): Batch of lengths (B,). + xs (Tensor, optional): The reference tensor. + If set, masks will be the same shape as this tensor. + length_dim (int, optional): Dimension indicator of the above tensor. + See the example. + Returns: + ByteTensor: mask tensor containing indices of padded part. + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (including 1.2) + Examples: + With only lengths. + >>> lengths = [5, 3, 2] + >>> make_non_pad_mask(lengths) + masks = [[1, 1, 1, 1 ,1], + [1, 1, 1, 0, 0], + [1, 1, 0, 0, 0]] + With the reference tensor. + >>> xs = torch.zeros((3, 2, 4)) + >>> make_non_pad_mask(lengths, xs) + tensor([[[1, 1, 1, 1], + [1, 1, 1, 1]], + [[1, 1, 1, 0], + [1, 1, 1, 0]], + [[1, 1, 0, 0], + [1, 1, 0, 0]]], dtype=torch.uint8) + >>> xs = torch.zeros((3, 2, 6)) + >>> make_non_pad_mask(lengths, xs) + tensor([[[1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0]], + [[1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0]], + [[1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) + With the reference tensor and dimension indicator. + >>> xs = torch.zeros((3, 6, 6)) + >>> make_non_pad_mask(lengths, xs, 1) + tensor([[[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0]], + [[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]], + [[1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8) + >>> make_non_pad_mask(lengths, xs, 2) + tensor([[[1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0], + [1, 1, 1, 1, 1, 0]], + [[1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0], + [1, 1, 1, 0, 0, 0]], + [[1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0], + [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8) + """ + return ~make_pad_mask(lengths, xs, length_dim) + + +def get_mask_from_lengths(lengths): + max_len = torch.max(lengths).item() + ids = torch.arange(0, max_len).to(lengths.device) + mask = (ids < lengths.unsqueeze(1)).bool() + return mask + + +def group_hidden_by_segs(h, seg_ids, max_len): + """ + + :param h: [B, T, H] + :param seg_ids: [B, T] + :return: h_ph: [B, T_ph, H] + """ + B, T, H = h.shape + h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h) + all_ones = h.new_ones(h.shape[:2]) + cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous() + h_gby_segs = h_gby_segs[:, 1:] + cnt_gby_segs = cnt_gby_segs[:, 1:] + h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1) + return h_gby_segs, cnt_gby_segs + +def mel2token_to_dur(mel2token, T_txt=None, max_dur=None): + is_torch = isinstance(mel2token, torch.Tensor) + has_batch_dim = True + if not is_torch: + mel2token = torch.LongTensor(mel2token) + if T_txt is None: + T_txt = mel2token.max() + if len(mel2token.shape) == 1: + mel2token = mel2token[None, ...] + has_batch_dim = False + B, _ = mel2token.shape + dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token)) + dur = dur[:, 1:] + if max_dur is not None: + dur = dur.clamp(max=max_dur) + if not is_torch: + dur = dur.numpy() + if not has_batch_dim: + dur = dur[0] + return dur + +def expand_word2ph(word_encoding, ph2word): + word_encoding = F.pad(word_encoding,[0,0,1,0]) + ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]]) + out = torch.gather(word_encoding, 1, ph2word_) # [B, T, H] + return out \ No newline at end of file diff --git a/NeuralSeq/vocoders/__init__.py b/NeuralSeq/vocoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..66c318857ce48048437dede7072901ad6471b8fc --- /dev/null +++ b/NeuralSeq/vocoders/__init__.py @@ -0,0 +1 @@ +from vocoders import hifigan diff --git a/NeuralSeq/vocoders/__pycache__/__init__.cpython-37.pyc b/NeuralSeq/vocoders/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b34d2a2f64b654b1e587e559d17e9f1840d426f Binary files /dev/null and b/NeuralSeq/vocoders/__pycache__/__init__.cpython-37.pyc differ diff --git a/NeuralSeq/vocoders/__pycache__/__init__.cpython-38.pyc b/NeuralSeq/vocoders/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ba12c81b89681c8ba2df8a8dc99244a763cfd916 Binary files /dev/null and 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differ diff --git a/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-37.pyc b/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cc3da72d7f0ffcb038c393c9c9a86e5e493e53f0 Binary files /dev/null and b/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-37.pyc differ diff --git a/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-38.pyc b/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1cd36ebb7a39cbdf6fed59c74cbdd37aea64c2cc Binary files /dev/null and b/NeuralSeq/vocoders/__pycache__/vocoder_utils.cpython-38.pyc differ diff --git a/NeuralSeq/vocoders/base_vocoder.py b/NeuralSeq/vocoders/base_vocoder.py new file mode 100644 index 0000000000000000000000000000000000000000..fe49a9e4f790ecdc5e76d60a23f96602b59fc48d --- /dev/null +++ b/NeuralSeq/vocoders/base_vocoder.py @@ -0,0 +1,39 @@ +import importlib +VOCODERS = {} + + +def register_vocoder(cls): + VOCODERS[cls.__name__.lower()] = cls + VOCODERS[cls.__name__] = cls + return cls + + +def get_vocoder_cls(hparams): + if hparams['vocoder'] in VOCODERS: + return VOCODERS[hparams['vocoder']] + else: + vocoder_cls = hparams['vocoder'] + pkg = ".".join(vocoder_cls.split(".")[:-1]) + cls_name = vocoder_cls.split(".")[-1] + vocoder_cls = getattr(importlib.import_module(pkg), cls_name) + return vocoder_cls + + +class BaseVocoder: + def spec2wav(self, mel): + """ + + :param mel: [T, 80] + :return: wav: [T'] + """ + + raise NotImplementedError + + @staticmethod + def wav2spec(wav_fn): + """ + + :param wav_fn: str + :return: wav, mel: [T, 80] + """ + raise NotImplementedError diff --git a/NeuralSeq/vocoders/hifigan.py b/NeuralSeq/vocoders/hifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..810d3c931b556387f8a2e85537f4964add1e76b0 --- /dev/null +++ b/NeuralSeq/vocoders/hifigan.py @@ -0,0 +1,76 @@ +import glob +import json +import os +import re + +import librosa +import torch + +import utils +from modules.hifigan.hifigan import HifiGanGenerator +from utils.hparams import hparams, set_hparams +from vocoders.base_vocoder import register_vocoder +from vocoders.pwg import PWG +from vocoders.vocoder_utils import denoise + + +def load_model(config_path, checkpoint_path): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + ckpt_dict = torch.load(checkpoint_path, map_location="cpu") + if '.yaml' in config_path: + config = set_hparams(config_path, global_hparams=False) + state = ckpt_dict["state_dict"]["model_gen"] + elif '.json' in config_path: + config = json.load(open(config_path, 'r')) + state = ckpt_dict["generator"] + + model = HifiGanGenerator(config) + model.load_state_dict(state, strict=True) + model.remove_weight_norm() + model = model.eval().to(device) + print(f"| Loaded model parameters from {checkpoint_path}.") + print(f"| HifiGAN device: {device}.") + return model, config, device + + +total_time = 0 + + +@register_vocoder +class HifiGAN(PWG): + def __init__(self): + base_dir = hparams['vocoder_ckpt'] + config_path = f'{base_dir}/config.yaml' + if os.path.exists(config_path): + ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= + lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] + print('| load HifiGAN: ', ckpt) + self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) + else: + config_path = f'{base_dir}/config.json' + ckpt = f'{base_dir}/generator_v1' + if os.path.exists(config_path): + self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt) + + def spec2wav(self, mel, **kwargs): + device = self.device + with torch.no_grad(): + c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device) + with utils.Timer('hifigan', print_time=hparams['profile_infer']): + f0 = kwargs.get('f0') + if f0 is not None and hparams.get('use_nsf'): + f0 = torch.FloatTensor(f0[None, :]).to(device) + y = self.model(c, f0).view(-1) + else: + y = self.model(c).view(-1) + wav_out = y.cpu().numpy() + if hparams.get('vocoder_denoise_c', 0.0) > 0: + wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c']) + return wav_out + + # @staticmethod + # def wav2spec(wav_fn, **kwargs): + # wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate']) + # wav_torch = torch.FloatTensor(wav)[None, :] + # mel = mel_spectrogram(wav_torch, hparams).numpy()[0] + # return wav, mel.T diff --git a/NeuralSeq/vocoders/pwg.py b/NeuralSeq/vocoders/pwg.py new file mode 100644 index 0000000000000000000000000000000000000000..ca9b6891ab2ba5cb413eeca97a41534e5db129d5 --- /dev/null +++ b/NeuralSeq/vocoders/pwg.py @@ -0,0 +1,137 @@ +import glob +import re +import librosa +import torch +import yaml +from sklearn.preprocessing import StandardScaler +from torch import nn +from modules.parallel_wavegan.models import ParallelWaveGANGenerator +from modules.parallel_wavegan.utils import read_hdf5 +from utils.hparams import hparams +from utils.pitch_utils import f0_to_coarse +from vocoders.base_vocoder import BaseVocoder, register_vocoder +import numpy as np + + +def load_pwg_model(config_path, checkpoint_path, stats_path): + # load config + with open(config_path) as f: + config = yaml.load(f, Loader=yaml.Loader) + + # setup + if torch.cuda.is_available(): + device = torch.device("cuda") + else: + device = torch.device("cpu") + model = ParallelWaveGANGenerator(**config["generator_params"]) + + ckpt_dict = torch.load(checkpoint_path, map_location="cpu") + if 'state_dict' not in ckpt_dict: # official vocoder + model.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["model"]["generator"]) + scaler = StandardScaler() + if config["format"] == "hdf5": + scaler.mean_ = read_hdf5(stats_path, "mean") + scaler.scale_ = read_hdf5(stats_path, "scale") + elif config["format"] == "npy": + scaler.mean_ = np.load(stats_path)[0] + scaler.scale_ = np.load(stats_path)[1] + else: + raise ValueError("support only hdf5 or npy format.") + else: # custom PWG vocoder + fake_task = nn.Module() + fake_task.model_gen = model + fake_task.load_state_dict(torch.load(checkpoint_path, map_location="cpu")["state_dict"], strict=False) + scaler = None + + model.remove_weight_norm() + model = model.eval().to(device) + print(f"| Loaded model parameters from {checkpoint_path}.") + print(f"| PWG device: {device}.") + return model, scaler, config, device + + +@register_vocoder +class PWG(BaseVocoder): + def __init__(self): + if hparams['vocoder_ckpt'] == '': # load LJSpeech PWG pretrained model + base_dir = 'wavegan_pretrained' + ckpts = glob.glob(f'{base_dir}/checkpoint-*steps.pkl') + ckpt = sorted(ckpts, key= + lambda x: int(re.findall(f'{base_dir}/checkpoint-(\d+)steps.pkl', x)[0]))[-1] + config_path = f'{base_dir}/config.yaml' + print('| load PWG: ', ckpt) + self.model, self.scaler, self.config, self.device = load_pwg_model( + config_path=config_path, + checkpoint_path=ckpt, + stats_path=f'{base_dir}/stats.h5', + ) + else: + base_dir = hparams['vocoder_ckpt'] + print(base_dir) + config_path = f'{base_dir}/config.yaml' + ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key= + lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1] + print('| load PWG: ', ckpt) + self.scaler = None + self.model, _, self.config, self.device = load_pwg_model( + config_path=config_path, + checkpoint_path=ckpt, + stats_path=f'{base_dir}/stats.h5', + ) + + def spec2wav(self, mel, **kwargs): + # start generation + config = self.config + device = self.device + pad_size = (config["generator_params"]["aux_context_window"], + config["generator_params"]["aux_context_window"]) + c = mel + if self.scaler is not None: + c = self.scaler.transform(c) + + with torch.no_grad(): + z = torch.randn(1, 1, c.shape[0] * config["hop_size"]).to(device) + c = np.pad(c, (pad_size, (0, 0)), "edge") + c = torch.FloatTensor(c).unsqueeze(0).transpose(2, 1).to(device) + p = kwargs.get('f0') + if p is not None: + p = f0_to_coarse(p) + p = np.pad(p, (pad_size,), "edge") + p = torch.LongTensor(p[None, :]).to(device) + y = self.model(z, c, p).view(-1) + wav_out = y.cpu().numpy() + return wav_out + + @staticmethod + def wav2spec(wav_fn, return_linear=False): + from data_gen.tts.data_gen_utils import process_utterance + res = process_utterance( + wav_fn, fft_size=hparams['fft_size'], + hop_size=hparams['hop_size'], + win_length=hparams['win_size'], + num_mels=hparams['audio_num_mel_bins'], + fmin=hparams['fmin'], + fmax=hparams['fmax'], + sample_rate=hparams['audio_sample_rate'], + loud_norm=hparams['loud_norm'], + min_level_db=hparams['min_level_db'], + return_linear=return_linear, vocoder='pwg', eps=float(hparams.get('wav2spec_eps', 1e-10))) + if return_linear: + return res[0], res[1].T, res[2].T # [T, 80], [T, n_fft] + else: + return res[0], res[1].T + + @staticmethod + def wav2mfcc(wav_fn): + fft_size = hparams['fft_size'] + hop_size = hparams['hop_size'] + win_length = hparams['win_size'] + sample_rate = hparams['audio_sample_rate'] + wav, _ = librosa.core.load(wav_fn, sr=sample_rate) + mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13, + n_fft=fft_size, hop_length=hop_size, + win_length=win_length, pad_mode="constant", power=1.0) + mfcc_delta = librosa.feature.delta(mfcc, order=1) + mfcc_delta_delta = librosa.feature.delta(mfcc, order=2) + mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T + return mfcc diff --git a/NeuralSeq/vocoders/vocoder_utils.py b/NeuralSeq/vocoders/vocoder_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..db5d5ca1765928e4b047db04435a8a39b52592ca --- /dev/null +++ b/NeuralSeq/vocoders/vocoder_utils.py @@ -0,0 +1,15 @@ +import librosa + +from utils.hparams import hparams +import numpy as np + + +def denoise(wav, v=0.1): + spec = librosa.stft(y=wav, n_fft=hparams['fft_size'], hop_length=hparams['hop_size'], + win_length=hparams['win_size'], pad_mode='constant') + spec_m = np.abs(spec) + spec_m = np.clip(spec_m - v, a_min=0, a_max=None) + spec_a = np.angle(spec) + + return librosa.istft(spec_m * np.exp(1j * spec_a), hop_length=hparams['hop_size'], + win_length=hparams['win_size']) diff --git a/data/binary/training_set/mfa_dict.txt b/data/binary/training_set/mfa_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d0a876417d7997277620147ed2274696f419a5a --- /dev/null +++ b/data/binary/training_set/mfa_dict.txt @@ -0,0 +1,47532 @@ +AA0_B_JH_EH1_K_T_IH0_V_L_IY0 AA0 B JH EH1 K T IH0 V L IY0 +AA0_B_N_AA1_K_SH_AH0_S AA0 B N AA1 K SH AH0 S +AA0_B_S_IY1_N AA0 B S IY1 N +AA0_B_S_IY1_N_AH0_L_IY0 AA0 B S IY1 N AH0 L IY0 +AA0_B_T_UW1_S AA0 B T UW1 S +AA0_D_AA1_S_AH0 AA0 D AA1 S AH0 +AA0_D_AE1_S_AH0_T_IY0 AA0 D AE1 S AH0 T IY0 +AA0_D_AE1_S_AH0_T_IY0_Z AA0 D AE1 S AH0 T IY0 Z +AA0_D_EY1_SH_AH0_S AA0 D EY1 SH AH0 S +AA0_D_IH0_B_IH1_L_IH0_T_IY0 AA0 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"chew", "leaves", "quickly", "said", "rabbit", "pay", "half", "crown", "week", "extra", "daisy", "creams", "pink", "edges"] \ No newline at end of file diff --git a/mono2binaural/src/__pycache__/models.cpython-38.pyc b/mono2binaural/src/__pycache__/models.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ba1e35f43750279ddbb60c407b16d9935838f8e9 Binary files /dev/null and b/mono2binaural/src/__pycache__/models.cpython-38.pyc differ diff --git a/mono2binaural/src/__pycache__/utils.cpython-38.pyc b/mono2binaural/src/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a93896e213139a6d381670a222252f8cd65719bd Binary files /dev/null and b/mono2binaural/src/__pycache__/utils.cpython-38.pyc differ diff --git a/mono2binaural/src/__pycache__/warping.cpython-38.pyc b/mono2binaural/src/__pycache__/warping.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..907a3041b92eadbf7790029deec3bcdff72f8345 Binary files /dev/null and b/mono2binaural/src/__pycache__/warping.cpython-38.pyc differ diff --git a/mono2binaural/src/models.py b/mono2binaural/src/models.py new file mode 100644 index 0000000000000000000000000000000000000000..0d4052784170e4fdc90c342860981ec7c03a25bc --- /dev/null +++ b/mono2binaural/src/models.py @@ -0,0 +1,110 @@ +import numpy as np +import scipy.linalg +from scipy.spatial.transform import Rotation as R +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from src.warping import GeometricTimeWarper, MonotoneTimeWarper +from src.utils import Net + + +class GeometricWarper(nn.Module): + def __init__(self, sampling_rate=48000): + super().__init__() + self.warper = GeometricTimeWarper(sampling_rate=sampling_rate) + + def _transmitter_mouth(self, view): + # offset between tracking markers and real mouth position in the dataset + mouth_offset = np.array([0.09, 0, -0.20]) + quat = view[:, 3:, :].transpose(2, 1).contiguous().detach().cpu().view(-1, 4).numpy() + # make sure zero-padded values are set to non-zero values (else scipy raises an exception) + norms = scipy.linalg.norm(quat, axis=1) + eps_val = (norms == 0).astype(np.float32) + quat = quat + eps_val[:, None] + transmitter_rot_mat = R.from_quat(quat) + transmitter_mouth = transmitter_rot_mat.apply(mouth_offset, inverse=True) + transmitter_mouth = th.Tensor(transmitter_mouth).view(view.shape[0], -1, 3).transpose(2, 1).contiguous() + if view.is_cuda: + transmitter_mouth = transmitter_mouth.cuda() + return transmitter_mouth + + def _3d_displacements(self, view): + transmitter_mouth = self._transmitter_mouth(view) + # offset between tracking markers and ears in the dataset + left_ear_offset = th.Tensor([0, -0.08, -0.22]).cuda() if view.is_cuda else th.Tensor([0, -0.08, -0.22]) + right_ear_offset = th.Tensor([0, 0.08, -0.22]).cuda() if view.is_cuda else th.Tensor([0, 0.08, -0.22]) + # compute displacements between transmitter mouth and receiver left/right ear + displacement_left = view[:, 0:3, :] + transmitter_mouth - left_ear_offset[None, :, None] + displacement_right = view[:, 0:3, :] + transmitter_mouth - right_ear_offset[None, :, None] + displacement = th.stack([displacement_left, displacement_right], dim=1) + return displacement + + def _warpfield(self, view, seq_length): + return self.warper.displacements2warpfield(self._3d_displacements(view), seq_length) + + def forward(self, mono, view): + ''' + :param mono: input signal as tensor of shape B x 1 x T + :param view: rx/tx position/orientation as tensor of shape B x 7 x K (K = T / 400) + :return: warped: warped left/right ear signal as tensor of shape B x 2 x T + ''' + return self.warper(th.cat([mono, mono], dim=1), self._3d_displacements(view)) + + +class Warpnet(nn.Module): + def __init__(self, layers=4, channels=64, view_dim=7): + super().__init__() + self.layers = [nn.Conv1d(view_dim if l == 0 else channels, channels, kernel_size=2) for l in range(layers)] + self.layers = nn.ModuleList(self.layers) + self.linear = nn.Conv1d(channels, 2, kernel_size=1) + self.neural_warper = MonotoneTimeWarper() + self.geometric_warper = GeometricWarper() + + def neural_warpfield(self, view, seq_length): + warpfield = view + for layer in self.layers: + warpfield = F.pad(warpfield, pad=[1, 0]) + warpfield = F.relu(layer(warpfield)) + warpfield = self.linear(warpfield) + warpfield = F.interpolate(warpfield, size=seq_length) + return warpfield + + def forward(self, mono, view): + ''' + :param mono: input signal as tensor of shape B x 1 x T + :param view: rx/tx position/orientation as tensor of shape B x 7 x K (K = T / 400) + :return: warped: warped left/right ear signal as tensor of shape B x 2 x T + ''' + geometric_warpfield = self.geometric_warper._warpfield(view, mono.shape[-1]) + neural_warpfield = self.neural_warpfield(view, mono.shape[-1]) + warpfield = geometric_warpfield + neural_warpfield + # ensure causality + warpfield = -F.relu(-warpfield) # the predicted warp + warped = self.neural_warper(th.cat([mono, mono], dim=1), warpfield) + return warped + +class BinauralNetwork(Net): + def __init__(self, + view_dim=7, + warpnet_layers=4, + warpnet_channels=64, + model_name='binaural_network', + use_cuda=True): + super().__init__(model_name, use_cuda) + self.warper = Warpnet(warpnet_layers, warpnet_channels) + if self.use_cuda: + self.cuda() + + def forward(self, mono, view): + ''' + :param mono: the input signal as a B x 1 x T tensor + :param view: the receiver/transmitter position as a B x 7 x T tensor + :return: out: the binaural output produced by the network + intermediate: a two-channel audio signal obtained from the output of each intermediate layer + as a list of B x 2 x T tensors + ''' + # print('mono ', mono.shape) + # print('view ', view.shape) + warped = self.warper(mono, view) + # print('warped ', warped.shape) + return warped diff --git a/mono2binaural/src/utils.py b/mono2binaural/src/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..074dd84bcb700650a615f75b37c6c54f6f211443 --- /dev/null +++ b/mono2binaural/src/utils.py @@ -0,0 +1,251 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +All rights reserved. + +This source code is licensed under the license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch as th +#import torchaudio as ta + + +class Net(th.nn.Module): + + def __init__(self, model_name="network", use_cuda=True): + super().__init__() + self.use_cuda = use_cuda + self.model_name = model_name + + def save(self, model_dir, suffix=''): + ''' + save the network to model_dir/model_name.suffix.net + :param model_dir: directory to save the model to + :param suffix: suffix to append after model name + ''' + if self.use_cuda: + self.cpu() + + if suffix == "": + fname = f"{model_dir}/{self.model_name}.net" + else: + fname = f"{model_dir}/{self.model_name}.{suffix}.net" + + th.save(self.state_dict(), fname) + if self.use_cuda: + self.cuda() + + def load_from_file(self, model_file): + ''' + load network parameters from model_file + :param model_file: file containing the model parameters + ''' + if self.use_cuda: + self.cpu() + + states = th.load(model_file) + self.load_state_dict(states) + + if self.use_cuda: + self.cuda() + print(f"Loaded: {model_file}") + + def load(self, model_dir, suffix=''): + ''' + load network parameters from model_dir/model_name.suffix.net + :param model_dir: directory to load the model from + :param suffix: suffix to append after model name + ''' + if suffix == "": + fname = f"{model_dir}/{self.model_name}.net" + else: + fname = f"{model_dir}/{self.model_name}.{suffix}.net" + self.load_from_file(fname) + + def num_trainable_parameters(self): + ''' + :return: the number of trainable parameters in the model + ''' + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + +# class NewbobAdam(th.optim.Adam): + +# def __init__(self, +# weights, +# net, +# artifacts_dir, +# initial_learning_rate=0.001, +# decay=0.5, +# max_decay=0.01 +# ): +# ''' +# Newbob learning rate scheduler +# :param weights: weights to optimize +# :param net: the network, must be an instance of type src.utils.Net +# :param artifacts_dir: (str) directory to save/restore models to/from +# :param initial_learning_rate: (float) initial learning rate +# :param decay: (float) value to decrease learning rate by when loss doesn't improve further +# :param max_decay: (float) maximum decay of learning rate +# ''' +# super().__init__(weights, lr=initial_learning_rate) +# self.last_epoch_loss = np.inf +# self.total_decay = 1 +# self.net = net +# self.decay = decay +# self.max_decay = max_decay +# self.artifacts_dir = artifacts_dir +# # store initial state as backup +# if decay < 1.0: +# net.save(artifacts_dir, suffix="newbob") + +# def update_lr(self, loss): +# ''' +# update the learning rate based on the current loss value and historic loss values +# :param loss: the loss after the current iteration +# ''' +# if loss > self.last_epoch_loss and self.decay < 1.0 and self.total_decay > self.max_decay: +# self.total_decay = self.total_decay * self.decay +# print(f"NewbobAdam: Decay learning rate (loss degraded from {self.last_epoch_loss} to {loss})." +# f"Total decay: {self.total_decay}") +# # restore previous network state +# self.net.load(self.artifacts_dir, suffix="newbob") +# # decrease learning rate +# for param_group in self.param_groups: +# param_group['lr'] = param_group['lr'] * self.decay +# else: +# self.last_epoch_loss = loss +# # save last snapshot to restore it in case of lr decrease +# if self.decay < 1.0 and self.total_decay > self.max_decay: +# self.net.save(self.artifacts_dir, suffix="newbob") + + +# class FourierTransform: +# def __init__(self, +# fft_bins=2048, +# win_length_ms=40, +# frame_rate_hz=100, +# causal=False, +# preemphasis=0.0, +# sample_rate=48000, +# normalized=False): +# self.sample_rate = sample_rate +# self.frame_rate_hz = frame_rate_hz +# self.preemphasis = preemphasis +# self.fft_bins = fft_bins +# self.win_length = int(sample_rate * win_length_ms / 1000) +# self.hop_length = int(sample_rate / frame_rate_hz) +# self.causal = causal +# self.normalized = normalized +# if self.win_length > self.fft_bins: +# print('FourierTransform Warning: fft_bins should be larger than win_length') + +# def _convert_format(self, data, expected_dims): +# if not type(data) == th.Tensor: +# data = th.Tensor(data) +# if len(data.shape) < expected_dims: +# data = data.unsqueeze(0) +# if not len(data.shape) == expected_dims: +# raise Exception(f"FourierTransform: data needs to be a Tensor with {expected_dims} dimensions but got shape {data.shape}") +# return data + +# def _preemphasis(self, audio): +# if self.preemphasis > 0: +# return th.cat((audio[:, 0:1], audio[:, 1:] - self.preemphasis * audio[:, :-1]), dim=1) +# return audio + +# def _revert_preemphasis(self, audio): +# if self.preemphasis > 0: +# for i in range(1, audio.shape[1]): +# audio[:, i] = audio[:, i] + self.preemphasis * audio[:, i-1] +# return audio + +# def _magphase(self, complex_stft): +# mag, phase = ta.functional.magphase(complex_stft, 1.0) +# return mag, phase + +# def stft(self, audio): +# ''' +# wrapper around th.stft +# audio: wave signal as th.Tensor +# ''' +# hann = th.hann_window(self.win_length) +# hann = hann.cuda() if audio.is_cuda else hann +# spec = th.stft(audio, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, +# window=hann, center=not self.causal, normalized=self.normalized) +# return spec.contiguous() + +# def complex_spectrogram(self, audio): +# ''' +# audio: wave signal as th.Tensor +# return: th.Tensor of size channels x frequencies x time_steps (channels x y_axis x x_axis) +# ''' +# self._convert_format(audio, expected_dims=2) +# audio = self._preemphasis(audio) +# return self.stft(audio) + +# def magnitude_phase(self, audio): +# ''' +# audio: wave signal as th.Tensor +# return: tuple containing two th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum +# ''' +# stft = self.complex_spectrogram(audio) +# return self._magphase(stft) + +# def mag_spectrogram(self, audio): +# ''' +# audio: wave signal as th.Tensor +# return: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum +# ''' +# return self.magnitude_phase(audio)[0] + +# def power_spectrogram(self, audio): +# ''' +# audio: wave signal as th.Tensor +# return: power spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum +# ''' +# return th.pow(self.mag_spectrogram(audio), 2.0) + +# def phase_spectrogram(self, audio): +# ''' +# audio: wave signal as th.Tensor +# return: phase spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum +# ''' +# return self.magnitude_phase(audio)[1] + +# def mel_spectrogram(self, audio, n_mels): +# ''' +# audio: wave signal as th.Tensor +# n_mels: number of bins used for mel scale warping +# return: mel spectrogram as th.Tensor of size channels x n_mels x time_steps for magnitude and phase spectrum +# ''' +# spec = self.power_spectrogram(audio) +# mel_warping = ta.transforms.MelScale(n_mels, self.sample_rate) +# return mel_warping(spec) + +# def complex_spec2wav(self, complex_spec, length): +# ''' +# inverse stft +# complex_spec: complex spectrum as th.Tensor of size channels x frequencies x time_steps x 2 (real part/imaginary part) +# length: length of the audio to be reconstructed (in frames) +# ''' +# complex_spec = self._convert_format(complex_spec, expected_dims=4) +# hann = th.hann_window(self.win_length) +# hann = hann.cuda() if complex_spec.is_cuda else hann +# wav = ta.functional.istft(complex_spec, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, window=hann, length=length, center=not self.causal) +# wav = self._revert_preemphasis(wav) +# return wav + +# def magphase2wav(self, mag_spec, phase_spec, length): +# ''' +# reconstruction of wav signal from magnitude and phase spectrum +# mag_spec: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps +# phase_spec: phase spectrum as th.Tensor of size channels x frequencies x time_steps +# length: length of the audio to be reconstructed (in frames) +# ''' +# mag_spec = self._convert_format(mag_spec, expected_dims=3) +# phase_spec = self._convert_format(phase_spec, expected_dims=3) +# complex_spec = th.stack([mag_spec * th.cos(phase_spec), mag_spec * th.sin(phase_spec)], dim=-1) +# return self.complex_spec2wav(complex_spec, length) + diff --git a/mono2binaural/src/warping.py b/mono2binaural/src/warping.py new file mode 100644 index 0000000000000000000000000000000000000000..9d7c4ed30c48c8feec112f67b1e6a320605f4c08 --- /dev/null +++ b/mono2binaural/src/warping.py @@ -0,0 +1,113 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +All rights reserved. + +This source code is licensed under the license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class TimeWarperFunction(th.autograd.Function): + + @staticmethod + def forward(ctx, input, warpfield): + ''' + :param ctx: autograd context + :param input: input signal (B x 2 x T) + :param warpfield: the corresponding warpfield (B x 2 x T) + :return: the warped signal (B x 2 x T) + ''' + ctx.save_for_backward(input, warpfield) + # compute index list to lookup warped input values + idx_left = warpfield.floor().type(th.long) + idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1) + # compute weight for linear interpolation + alpha = warpfield - warpfield.floor() + # linear interpolation + output = (1 - alpha) * th.gather(input, 2, idx_left) + alpha * th.gather(input, 2, idx_right) + return output + + @staticmethod + def backward(ctx, grad_output): + input, warpfield = ctx.saved_tensors + # compute index list to lookup warped input values + idx_left = warpfield.floor().type(th.long) + idx_right = th.clamp(warpfield.ceil().type(th.long), max=input.shape[-1]-1) + # warpfield gradient + grad_warpfield = th.gather(input, 2, idx_right) - th.gather(input, 2, idx_left) + grad_warpfield = grad_output * grad_warpfield + # input gradient + grad_input = th.zeros(input.shape, device=input.device) + alpha = warpfield - warpfield.floor() + grad_input = grad_input.scatter_add(2, idx_left, grad_output * (1 - alpha)) + \ + grad_input.scatter_add(2, idx_right, grad_output * alpha) + return grad_input, grad_warpfield + + +class TimeWarper(nn.Module): + + def __init__(self): + super().__init__() + self.warper = TimeWarperFunction().apply + + def _to_absolute_positions(self, warpfield, seq_length): + # translate warpfield from relative warp indices to absolute indices ([1...T] + warpfield) + temp_range = th.arange(seq_length, dtype=th.float) + temp_range = temp_range.cuda() if warpfield.is_cuda else temp_range + return th.clamp(warpfield + temp_range[None, None, :], min=0, max=seq_length-1) + + def forward(self, input, warpfield): + ''' + :param input: audio signal to be warped (B x 2 x T) + :param warpfield: the corresponding warpfield (B x 2 x T) + :return: the warped signal (B x 2 x T) + ''' + warpfield = self._to_absolute_positions(warpfield, input.shape[-1]) + warped = self.warper(input, warpfield) + return warped + + +class MonotoneTimeWarper(TimeWarper): + + def forward(self, input, warpfield): + ''' + :param input: audio signal to be warped (B x 2 x T) + :param warpfield: the corresponding warpfield (B x 2 x T) + :return: the warped signal (B x 2 x T), ensured to be monotonous + ''' + warpfield = self._to_absolute_positions(warpfield, input.shape[-1]) + # ensure monotonicity: each warp must be at least as big as previous_warp-1 + warpfield = th.cummax(warpfield, dim=-1)[0] + # print('warpfield ',warpfield.shape) + # warp + warped = self.warper(input, warpfield) + return warped + + +class GeometricTimeWarper(TimeWarper): + + def __init__(self, sampling_rate=48000): + super().__init__() + self.sampling_rate = sampling_rate + + def displacements2warpfield(self, displacements, seq_length): + distance = th.sum(displacements**2, dim=2) ** 0.5 + distance = F.interpolate(distance, size=seq_length) + warpfield = -distance / 343.0 * self.sampling_rate + return warpfield + + def forward(self, input, displacements): + ''' + :param input: audio signal to be warped (B x 2 x T) + :param displacements: sequence of 3D displacement vectors for geometric warping (B x 3 x T) + :return: the warped signal (B x 2 x T) + ''' + warpfield = self.displacements2warpfield(displacements, input.shape[-1]) + # print('Ge warpfield ', warpfield.shape) + # assert 1==2 + warped = super().forward(input, warpfield) + return warped diff --git a/mono2binaural/useful_ckpts/m2b/binaural_network.net b/mono2binaural/useful_ckpts/m2b/binaural_network.net new file mode 100644 index 0000000000000000000000000000000000000000..2780d5434cd67fc1946934db25bf39c3873d6f94 Binary files /dev/null and b/mono2binaural/useful_ckpts/m2b/binaural_network.net differ diff --git a/mono2binaural/useful_ckpts/m2b/tx_positions.txt b/mono2binaural/useful_ckpts/m2b/tx_positions.txt new file mode 100644 index 0000000000000000000000000000000000000000..f28d435ba85e8e8adf80903d82170562443880b6 --- /dev/null +++ b/mono2binaural/useful_ckpts/m2b/tx_positions.txt @@ -0,0 +1,95649 @@ +-0.0829160 -0.9228290 -0.0446180 0.0614880 -0.0260480 0.0898050 -0.9937180 +-0.0800600 -0.9274320 -0.0452590 0.0638160 -0.0230050 0.0908210 -0.9935540 +-0.0770940 -0.9319830 -0.0459210 0.0663570 -0.0194460 0.0915450 -0.9933970 +-0.0739850 -0.9364690 -0.0466240 0.0690670 -0.0154700 0.0920560 -0.9932350 +-0.0707540 -0.9408780 -0.0473270 0.0719730 -0.0112510 0.0923100 -0.9930620 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-0.7452070 -0.2569210 0.0633460 0.0285050 -0.2320110 0.9702300 +0.0671400 -0.7438730 -0.2565890 0.0633890 0.0277030 -0.2344320 0.9696680 diff --git a/sound_extraction/model/LASSNet.py b/sound_extraction/model/LASSNet.py new file mode 100644 index 0000000000000000000000000000000000000000..a525e3c8b303753a52a8a001929414d27c08007f --- /dev/null +++ b/sound_extraction/model/LASSNet.py @@ -0,0 +1,25 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .text_encoder import Text_Encoder +from .resunet_film import UNetRes_FiLM + +class LASSNet(nn.Module): + def __init__(self, device='cuda'): + super(LASSNet, self).__init__() + self.text_embedder = Text_Encoder(device) + self.UNet = UNetRes_FiLM(channels=1, cond_embedding_dim=256) + + def forward(self, x, caption): + # x: (Batch, 1, T, 128)) + input_ids, attns_mask = self.text_embedder.tokenize(caption) + + cond_vec = self.text_embedder(input_ids, attns_mask)[0] + dec_cond_vec = cond_vec + + mask = self.UNet(x, cond_vec, dec_cond_vec) + mask = torch.sigmoid(mask) + return mask + + def get_tokenizer(self): + return self.text_embedder.tokenizer diff --git a/sound_extraction/model/__pycache__/LASSNet.cpython-38.pyc b/sound_extraction/model/__pycache__/LASSNet.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..34ae8be313973ac68b226d0bb21f909cd3492027 Binary files /dev/null and b/sound_extraction/model/__pycache__/LASSNet.cpython-38.pyc differ diff --git a/sound_extraction/model/__pycache__/film.cpython-38.pyc b/sound_extraction/model/__pycache__/film.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..51649d4d85552592b5e45c92446cca5953cdfb1d Binary files /dev/null and b/sound_extraction/model/__pycache__/film.cpython-38.pyc differ diff --git a/sound_extraction/model/__pycache__/modules.cpython-38.pyc b/sound_extraction/model/__pycache__/modules.cpython-38.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..e4e9a9ddd518f7749537dde6250358e0e76bf90f --- /dev/null +++ b/sound_extraction/model/film.py @@ -0,0 +1,27 @@ +import torch +import torch.nn as nn + +class Film(nn.Module): + def __init__(self, channels, cond_embedding_dim): + super(Film, self).__init__() + self.linear = nn.Sequential( + nn.Linear(cond_embedding_dim, channels * 2), + nn.ReLU(inplace=True), + nn.Linear(channels * 2, channels), + nn.ReLU(inplace=True) + ) + + def forward(self, data, cond_vec): + """ + :param data: [batchsize, channels, samples] or [batchsize, channels, T, F] or [batchsize, channels, F, T] + :param cond_vec: [batchsize, cond_embedding_dim] + :return: + """ + bias = self.linear(cond_vec) # [batchsize, channels] + if len(list(data.size())) == 3: + data = data + bias[..., None] + elif len(list(data.size())) == 4: + data = data + bias[..., None, None] + else: + print("Warning: The size of input tensor,", data.size(), "is not correct. Film is not working.") + return data \ No newline at end of file diff --git a/sound_extraction/model/modules.py b/sound_extraction/model/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..1124b1af31d1d720c07391186e2bfd504de879f1 --- /dev/null +++ b/sound_extraction/model/modules.py @@ -0,0 +1,483 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from .film import Film + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, activation, momentum): + super(ConvBlock, self).__init__() + + self.activation = activation + padding = (kernel_size[0] // 2, kernel_size[1] // 2) + + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=(1, 1), + dilation=(1, 1), + padding=padding, + bias=False, + ) + + self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) + + self.conv2 = nn.Conv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=(1, 1), + dilation=(1, 1), + padding=padding, + bias=False, + ) + + self.bn2 = nn.BatchNorm2d(out_channels, momentum=momentum) + + self.init_weights() + + def init_weights(self): + init_layer(self.conv1) + init_layer(self.conv2) + init_bn(self.bn1) + init_bn(self.bn2) + + def forward(self, x): + x = act(self.bn1(self.conv1(x)), self.activation) + x = act(self.bn2(self.conv2(x)), self.activation) + return x + + +class EncoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, downsample, activation, momentum): + super(EncoderBlock, self).__init__() + + self.conv_block = ConvBlock( + in_channels, out_channels, kernel_size, activation, momentum + ) + self.downsample = downsample + + def forward(self, x): + encoder = self.conv_block(x) + encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) + return encoder_pool, encoder + + +class DecoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, upsample, activation, momentum): + super(DecoderBlock, self).__init__() + self.kernel_size = kernel_size + self.stride = upsample + self.activation = activation + + self.conv1 = torch.nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=self.stride, + stride=self.stride, + padding=(0, 0), + bias=False, + dilation=(1, 1), + ) + + self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum) + + self.conv_block2 = ConvBlock( + out_channels * 2, out_channels, kernel_size, activation, momentum + ) + + def init_weights(self): + init_layer(self.conv1) + init_bn(self.bn) + + def prune(self, x): + """Prune the shape of x after transpose convolution.""" + padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) + x = x[ + :, + :, + padding[0] : padding[0] - self.stride[0], + padding[1] : padding[1] - self.stride[1]] + return x + + def forward(self, input_tensor, concat_tensor): + x = act(self.bn1(self.conv1(input_tensor)), self.activation) + # from IPython import embed; embed(using=False); os._exit(0) + # x = self.prune(x) + x = torch.cat((x, concat_tensor), dim=1) + x = self.conv_block2(x) + return x + + +class EncoderBlockRes1B(nn.Module): + def __init__(self, in_channels, out_channels, downsample, activation, momentum): + super(EncoderBlockRes1B, self).__init__() + size = (3,3) + + self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum) + self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.downsample = downsample + + def forward(self, x): + encoder = self.conv_block1(x) + encoder = self.conv_block2(encoder) + encoder = self.conv_block3(encoder) + encoder = self.conv_block4(encoder) + encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) + return encoder_pool, encoder + +class DecoderBlockRes1B(nn.Module): + def __init__(self, in_channels, out_channels, stride, activation, momentum): + super(DecoderBlockRes1B, self).__init__() + size = (3,3) + self.activation = activation + + self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=size, stride=stride, + padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) + + self.bn1 = nn.BatchNorm2d(in_channels) + self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum) + self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + + def init_weights(self): + init_layer(self.conv1) + + def prune(self, x, both=False): + """Prune the shape of x after transpose convolution. + """ + if(both): x = x[:, :, 0 : - 1, 0:-1] + else: x = x[:, :, 0: - 1, :] + return x + + def forward(self, input_tensor, concat_tensor,both=False): + x = self.conv1(F.relu_(self.bn1(input_tensor))) + x = self.prune(x,both=both) + x = torch.cat((x, concat_tensor), dim=1) + x = self.conv_block2(x) + x = self.conv_block3(x) + x = self.conv_block4(x) + x = self.conv_block5(x) + return x + + +class EncoderBlockRes2BCond(nn.Module): + def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim): + super(EncoderBlockRes2BCond, self).__init__() + size = (3, 3) + + self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.downsample = downsample + + def forward(self, x, cond_vec): + encoder = self.conv_block1(x, cond_vec) + encoder = self.conv_block2(encoder, cond_vec) + encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) + return encoder_pool, encoder + +class DecoderBlockRes2BCond(nn.Module): + def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim): + super(DecoderBlockRes2BCond, self).__init__() + size = (3, 3) + self.activation = activation + + self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=size, stride=stride, + padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) + + self.bn1 = nn.BatchNorm2d(in_channels) + self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + + def init_weights(self): + init_layer(self.conv1) + + def prune(self, x, both=False): + """Prune the shape of x after transpose convolution. + """ + if(both): x = x[:, :, 0 : - 1, 0:-1] + else: x = x[:, :, 0: - 1, :] + return x + + def forward(self, input_tensor, concat_tensor, cond_vec, both=False): + x = self.conv1(F.relu_(self.bn1(input_tensor))) + x = self.prune(x, both=both) + x = torch.cat((x, concat_tensor), dim=1) + x = self.conv_block2(x, cond_vec) + x = self.conv_block3(x, cond_vec) + return x + +class EncoderBlockRes4BCond(nn.Module): + def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim): + super(EncoderBlockRes4B, self).__init__() + size = (3,3) + + self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.downsample = downsample + + def forward(self, x, cond_vec): + encoder = self.conv_block1(x, cond_vec) + encoder = self.conv_block2(encoder, cond_vec) + encoder = self.conv_block3(encoder, cond_vec) + encoder = self.conv_block4(encoder, cond_vec) + encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) + return encoder_pool, encoder + +class DecoderBlockRes4BCond(nn.Module): + def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim): + super(DecoderBlockRes4B, self).__init__() + size = (3, 3) + self.activation = activation + + self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=size, stride=stride, + padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) + + self.bn1 = nn.BatchNorm2d(in_channels) + self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + self.conv_block5 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim) + + def init_weights(self): + init_layer(self.conv1) + + def prune(self, x, both=False): + """Prune the shape of x after transpose convolution. + """ + if(both): x = x[:, :, 0 : - 1, 0:-1] + else: x = x[:, :, 0: - 1, :] + return x + + def forward(self, input_tensor, concat_tensor, cond_vec, both=False): + x = self.conv1(F.relu_(self.bn1(input_tensor))) + x = self.prune(x,both=both) + x = torch.cat((x, concat_tensor), dim=1) + x = self.conv_block2(x, cond_vec) + x = self.conv_block3(x, cond_vec) + x = self.conv_block4(x, cond_vec) + x = self.conv_block5(x, cond_vec) + return x + +class EncoderBlockRes4B(nn.Module): + def __init__(self, in_channels, out_channels, downsample, activation, momentum): + super(EncoderBlockRes4B, self).__init__() + size = (3, 3) + + self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum) + self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.downsample = downsample + + def forward(self, x): + encoder = self.conv_block1(x) + encoder = self.conv_block2(encoder) + encoder = self.conv_block3(encoder) + encoder = self.conv_block4(encoder) + encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample) + return encoder_pool, encoder + +class DecoderBlockRes4B(nn.Module): + def __init__(self, in_channels, out_channels, stride, activation, momentum): + super(DecoderBlockRes4B, self).__init__() + size = (3,3) + self.activation = activation + + self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=size, stride=stride, + padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1) + + self.bn1 = nn.BatchNorm2d(in_channels) + self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum) + self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum) + + def init_weights(self): + init_layer(self.conv1) + + def prune(self, x, both=False): + """Prune the shape of x after transpose convolution. + """ + if(both): x = x[:, :, 0 : - 1, 0:-1] + else: x = x[:, :, 0: - 1, :] + return x + + def forward(self, input_tensor, concat_tensor,both=False): + x = self.conv1(F.relu_(self.bn1(input_tensor))) + x = self.prune(x,both=both) + x = torch.cat((x, concat_tensor), dim=1) + x = self.conv_block2(x) + x = self.conv_block3(x) + x = self.conv_block4(x) + x = self.conv_block5(x) + return x + +class ConvBlockResCond(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, activation, momentum, cond_embedding_dim): + r"""Residual block. + """ + super(ConvBlockResCond, self).__init__() + + self.activation = activation + padding = [kernel_size[0] // 2, kernel_size[1] // 2] + + self.bn1 = nn.BatchNorm2d(in_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, stride=(1, 1), + dilation=(1, 1), padding=padding, bias=False) + self.film1 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) + self.conv2 = nn.Conv2d(in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, stride=(1, 1), + dilation=(1, 1), padding=padding, bias=False) + self.film2 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) + + if in_channels != out_channels: + self.shortcut = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) + self.film_res = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim) + self.is_shortcut = True + else: + self.is_shortcut = False + + self.init_weights() + + def init_weights(self): + init_bn(self.bn1) + init_bn(self.bn2) + init_layer(self.conv1) + init_layer(self.conv2) + + if self.is_shortcut: + init_layer(self.shortcut) + + def forward(self, x, cond_vec): + origin = x + x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01)) + x = self.film1(x, cond_vec) + x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01)) + x = self.film2(x, cond_vec) + if self.is_shortcut: + residual = self.shortcut(origin) + residual = self.film_res(residual, cond_vec) + return residual + x + else: + return origin + x + +class ConvBlockRes(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, activation, momentum): + r"""Residual block. + """ + super(ConvBlockRes, self).__init__() + + self.activation = activation + padding = [kernel_size[0] // 2, kernel_size[1] // 2] + + self.bn1 = nn.BatchNorm2d(in_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, stride=(1, 1), + dilation=(1, 1), padding=padding, bias=False) + + self.conv2 = nn.Conv2d(in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, stride=(1, 1), + dilation=(1, 1), padding=padding, bias=False) + + if in_channels != out_channels: + self.shortcut = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) + self.is_shortcut = True + else: + self.is_shortcut = False + + self.init_weights() + + def init_weights(self): + init_bn(self.bn1) + init_bn(self.bn2) + init_layer(self.conv1) + init_layer(self.conv2) + + if self.is_shortcut: + init_layer(self.shortcut) + + def forward(self, x): + origin = x + x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01)) + x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01)) + + if self.is_shortcut: + return self.shortcut(origin) + x + else: + return origin + x + +def init_layer(layer): + """Initialize a Linear or Convolutional layer. """ + nn.init.xavier_uniform_(layer.weight) + + if hasattr(layer, 'bias'): + if layer.bias is not None: + layer.bias.data.fill_(0.) + +def init_bn(bn): + """Initialize a Batchnorm layer. """ + bn.bias.data.fill_(0.) + bn.weight.data.fill_(1.) + +def init_gru(rnn): + """Initialize a GRU layer. """ + + def _concat_init(tensor, init_funcs): + (length, fan_out) = tensor.shape + fan_in = length // len(init_funcs) + + for (i, init_func) in enumerate(init_funcs): + init_func(tensor[i * fan_in: (i + 1) * fan_in, :]) + + def _inner_uniform(tensor): + fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in') + nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in)) + + for i in range(rnn.num_layers): + _concat_init( + getattr(rnn, 'weight_ih_l{}'.format(i)), + [_inner_uniform, _inner_uniform, _inner_uniform] + ) + torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0) + + _concat_init( + getattr(rnn, 'weight_hh_l{}'.format(i)), + [_inner_uniform, _inner_uniform, nn.init.orthogonal_] + ) + torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0) + + +def act(x, activation): + if activation == 'relu': + return F.relu_(x) + + elif activation == 'leaky_relu': + return F.leaky_relu_(x, negative_slope=0.2) + + elif activation == 'swish': + return x * torch.sigmoid(x) + + else: + raise Exception('Incorrect activation!') \ No newline at end of file diff --git a/sound_extraction/model/resunet_film.py b/sound_extraction/model/resunet_film.py new file mode 100644 index 0000000000000000000000000000000000000000..c00addcbf6586632b78511aabe8c50a479d9c24f --- /dev/null +++ b/sound_extraction/model/resunet_film.py @@ -0,0 +1,110 @@ +from .modules import * +import numpy as np + +class UNetRes_FiLM(nn.Module): + def __init__(self, channels, cond_embedding_dim, nsrc=1): + super(UNetRes_FiLM, self).__init__() + activation = 'relu' + momentum = 0.01 + + self.nsrc = nsrc + self.channels = channels + self.downsample_ratio = 2 ** 6 # This number equals 2^{#encoder_blocks} + + self.encoder_block1 = EncoderBlockRes2BCond(in_channels=channels * nsrc, out_channels=32, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.encoder_block2 = EncoderBlockRes2BCond(in_channels=32, out_channels=64, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.encoder_block3 = EncoderBlockRes2BCond(in_channels=64, out_channels=128, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.encoder_block4 = EncoderBlockRes2BCond(in_channels=128, out_channels=256, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.encoder_block5 = EncoderBlockRes2BCond(in_channels=256, out_channels=384, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.encoder_block6 = EncoderBlockRes2BCond(in_channels=384, out_channels=384, + downsample=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.conv_block7 = ConvBlockResCond(in_channels=384, out_channels=384, + kernel_size=(3, 3), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block1 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block2 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block3 = DecoderBlockRes2BCond(in_channels=384, out_channels=256, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block4 = DecoderBlockRes2BCond(in_channels=256, out_channels=128, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block5 = DecoderBlockRes2BCond(in_channels=128, out_channels=64, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + self.decoder_block6 = DecoderBlockRes2BCond(in_channels=64, out_channels=32, + stride=(2, 2), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + + self.after_conv_block1 = ConvBlockResCond(in_channels=32, out_channels=32, + kernel_size=(3, 3), activation=activation, momentum=momentum, + cond_embedding_dim=cond_embedding_dim) + + self.after_conv2 = nn.Conv2d(in_channels=32, out_channels=1, + kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True) + + self.init_weights() + + def init_weights(self): + init_layer(self.after_conv2) + + def forward(self, sp, cond_vec, dec_cond_vec): + """ + Args: + input: sp: (batch_size, channels_num, segment_samples) + Outputs: + output_dict: { + 'wav': (batch_size, channels_num, segment_samples), + 'sp': (batch_size, channels_num, time_steps, freq_bins)} + """ + + x = sp + # Pad spectrogram to be evenly divided by downsample ratio. + origin_len = x.shape[2] # time_steps + pad_len = int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio - origin_len + x = F.pad(x, pad=(0, 0, 0, pad_len)) + x = x[..., 0: x.shape[-1] - 2] # (bs, channels, T, F) + + # UNet + (x1_pool, x1) = self.encoder_block1(x, cond_vec) # x1_pool: (bs, 32, T / 2, F / 2) + (x2_pool, x2) = self.encoder_block2(x1_pool, cond_vec) # x2_pool: (bs, 64, T / 4, F / 4) + (x3_pool, x3) = self.encoder_block3(x2_pool, cond_vec) # x3_pool: (bs, 128, T / 8, F / 8) + (x4_pool, x4) = self.encoder_block4(x3_pool, dec_cond_vec) # x4_pool: (bs, 256, T / 16, F / 16) + (x5_pool, x5) = self.encoder_block5(x4_pool, dec_cond_vec) # x5_pool: (bs, 512, T / 32, F / 32) + (x6_pool, x6) = self.encoder_block6(x5_pool, dec_cond_vec) # x6_pool: (bs, 1024, T / 64, F / 64) + x_center = self.conv_block7(x6_pool, dec_cond_vec) # (bs, 2048, T / 64, F / 64) + x7 = self.decoder_block1(x_center, x6, dec_cond_vec) # (bs, 1024, T / 32, F / 32) + x8 = self.decoder_block2(x7, x5, dec_cond_vec) # (bs, 512, T / 16, F / 16) + x9 = self.decoder_block3(x8, x4, cond_vec) # (bs, 256, T / 8, F / 8) + x10 = self.decoder_block4(x9, x3, cond_vec) # (bs, 128, T / 4, F / 4) + x11 = self.decoder_block5(x10, x2, cond_vec) # (bs, 64, T / 2, F / 2) + x12 = self.decoder_block6(x11, x1, cond_vec) # (bs, 32, T, F) + x = self.after_conv_block1(x12, cond_vec) # (bs, 32, T, F) + x = self.after_conv2(x) # (bs, channels, T, F) + + # Recover shape + x = F.pad(x, pad=(0, 2)) + x = x[:, :, 0: origin_len, :] + return x + + +if __name__ == "__main__": + model = UNetRes_FiLM(channels=1, cond_embedding_dim=16) + cond_vec = torch.randn((1, 16)) + dec_vec = cond_vec + print(model(torch.randn((1, 1, 1001, 513)), cond_vec, dec_vec).size()) diff --git a/sound_extraction/model/text_encoder.py b/sound_extraction/model/text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..e78528594d035ea706ea3ea8537abab62b781e99 --- /dev/null +++ b/sound_extraction/model/text_encoder.py @@ -0,0 +1,45 @@ +import torch +import torch.nn as nn +from transformers import * +import warnings +warnings.filterwarnings('ignore') +# pretrained model name: (model class, model tokenizer, output dimension, token style) +MODELS = { + 'prajjwal1/bert-mini': (BertModel, BertTokenizer), +} + +class Text_Encoder(nn.Module): + def __init__(self, device): + super(Text_Encoder, self).__init__() + self.base_model = 'prajjwal1/bert-mini' + self.dropout = 0.1 + + self.tokenizer = MODELS[self.base_model][1].from_pretrained(self.base_model) + + self.bert_layer = MODELS[self.base_model][0].from_pretrained(self.base_model, + add_pooling_layer=False, + hidden_dropout_prob=self.dropout, + attention_probs_dropout_prob=self.dropout, + output_hidden_states=True) + + self.linear_layer = nn.Sequential(nn.Linear(256, 256), nn.ReLU(inplace=True)) + + self.device = device + + def tokenize(self, caption): + # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + tokenized = self.tokenizer(caption, add_special_tokens=False, padding=True, return_tensors='pt') + input_ids = tokenized['input_ids'] + attns_mask = tokenized['attention_mask'] + + input_ids = input_ids.to(self.device) + attns_mask = attns_mask.to(self.device) + return input_ids, attns_mask + + def forward(self, input_ids, attns_mask): + # input_ids, attns_mask = self.tokenize(caption) + output = self.bert_layer(input_ids=input_ids, attention_mask=attns_mask)[0] + cls_embed = output[:, 0, :] + text_embed = self.linear_layer(cls_embed) + + return text_embed, output # text_embed: (batch, hidden_size) \ No newline at end of file diff --git a/sound_extraction/useful_ckpts/LASSNet.pt b/sound_extraction/useful_ckpts/LASSNet.pt new file mode 100644 index 0000000000000000000000000000000000000000..7c9ac6fbcd63d4023e14f1dcdcf46b9f6d04ebd6 --- /dev/null +++ b/sound_extraction/useful_ckpts/LASSNet.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c6a60910bc1db03d9ff7040d0e5906ab784431cb8b279cf4e295124e9e76fae +size 761532233 diff --git a/sound_extraction/utils/__pycache__/stft.cpython-38.pyc b/sound_extraction/utils/__pycache__/stft.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..690cede8aeda5682b3ddf9ca6cdda2d453bcd28a Binary files /dev/null and b/sound_extraction/utils/__pycache__/stft.cpython-38.pyc differ diff --git a/sound_extraction/utils/__pycache__/wav_io.cpython-38.pyc b/sound_extraction/utils/__pycache__/wav_io.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a0f618ac042dac229f58f1e8347527ddf3bf85a Binary files /dev/null and b/sound_extraction/utils/__pycache__/wav_io.cpython-38.pyc differ diff --git a/sound_extraction/utils/create_mixtures.py b/sound_extraction/utils/create_mixtures.py new file mode 100644 index 0000000000000000000000000000000000000000..2b30d0d3b60b1e67940183812893bd323495cfea --- /dev/null +++ b/sound_extraction/utils/create_mixtures.py @@ -0,0 +1,98 @@ +import torch +import numpy as np + +def add_noise_and_scale(front, noise, snr_l=0, snr_h=0, scale_lower=1.0, scale_upper=1.0): + """ + :param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine + :param noise: noise, [samples,channel], any scale + :param snr_l: Optional + :param snr_h: Optional + :param scale_lower: Optional + :param scale_upper: Optional + :return: scaled front and noise (noisy = front + noise), all_mel_e2e outputs are noramlized within [-1 , 1] + """ + snr = None + noise, front = normalize_energy_torch(noise), normalize_energy_torch(front) # set noise and vocal to equal range [-1,1] + # print("normalize:",torch.max(noise),torch.max(front)) + if snr_l is not None and snr_h is not None: + front, noise, snr = _random_noise(front, noise, snr_l=snr_l, snr_h=snr_h) # remix them with a specific snr + + noisy, noise, front = unify_energy_torch(noise + front, noise, front) # normalize noisy, noise and vocal energy into [-1,1] + + # print("unify:", torch.max(noise), torch.max(front), torch.max(noisy)) + scale = _random_scale(scale_lower, scale_upper) # random scale these three signal + + # print("Scale",scale) + noisy, noise, front = noisy * scale, noise * scale, front * scale # apply scale + # print("after scale", torch.max(noisy), torch.max(noise), torch.max(front), snr, scale) + + front, noise = _to_numpy(front), _to_numpy(noise) # [num_samples] + mixed_wav = front + noise + + return front, noise, mixed_wav, snr, scale + +def _random_scale(lower=0.3, upper=0.9): + return float(uniform_torch(lower, upper)) + +def _random_noise(clean, noise, snr_l=None, snr_h=None): + snr = uniform_torch(snr_l,snr_h) + clean_weight = 10 ** (float(snr) / 20) + return clean, noise/clean_weight, snr + +def _to_numpy(wav): + return np.transpose(wav, (1, 0))[0].numpy() # [num_samples] + +def normalize_energy(audio, alpha = 1): + ''' + :param audio: 1d waveform, [batchsize, *], + :param alpha: the value of output range from: [-alpha,alpha] + :return: 1d waveform which value range from: [-alpha,alpha] + ''' + val_max = activelev(audio) + return (audio / val_max) * alpha + +def normalize_energy_torch(audio, alpha = 1): + ''' + If the signal is almost empty(determined by threshold), if will only be divided by 2**15 + :param audio: 1d waveform, 2**15 + :param alpha: the value of output range from: [-alpha,alpha] + :return: 1d waveform which value range from: [-alpha,alpha] + ''' + val_max = activelev_torch([audio]) + return (audio / val_max) * alpha + +def unify_energy(*args): + max_amp = activelev(args) + mix_scale = 1.0/max_amp + return [x * mix_scale for x in args] + +def unify_energy_torch(*args): + max_amp = activelev_torch(args) + mix_scale = 1.0/max_amp + return [x * mix_scale for x in args] + +def activelev(*args): + ''' + need to update like matlab + ''' + return np.max(np.abs([*args])) + +def activelev_torch(*args): + ''' + need to update like matlab + ''' + res = [] + args = args[0] + for each in args: + res.append(torch.max(torch.abs(each))) + return max(res) + +def uniform_torch(lower, upper): + if(abs(lower-upper)<1e-5): + return upper + return (upper-lower)*torch.rand(1)+lower + +if __name__ == "__main__": + wav1 = torch.randn(1, 32000) + wav2 = torch.randn(1, 32000) + target, noise, snr, scale = add_noise_and_scale(wav1, wav2) diff --git a/sound_extraction/utils/stft.py b/sound_extraction/utils/stft.py new file mode 100644 index 0000000000000000000000000000000000000000..04a1da93e3bd5777e8759f1b4bc5c0eaca149317 --- /dev/null +++ b/sound_extraction/utils/stft.py @@ -0,0 +1,159 @@ +import torch +import numpy as np +import torch.nn.functional as F +from torch.autograd import Variable +from scipy.signal import get_window +import librosa.util as librosa_util +from librosa.util import pad_center, tiny +# from audio_processing import window_sumsquare + +def window_sumsquare(window, n_frames, hop_length=512, win_length=1024, + n_fft=1024, dtype=np.float32, norm=None): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + n_frames : int > 0 + The number of analysis frames + hop_length : int > 0 + The number of samples to advance between frames + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + n_fft : int > 0 + The length of each analysis frame. + dtype : np.dtype + The data type of the output + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm)**2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))] + return x + +class STFT(torch.nn.Module): + """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" + def __init__(self, filter_length=1024, hop_length=512, win_length=1024, + window='hann'): + super(STFT, self).__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = window + self.forward_transform = None + scale = self.filter_length / self.hop_length + fourier_basis = np.fft.fft(np.eye(self.filter_length)) + + cutoff = int((self.filter_length / 2 + 1)) + fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])]) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + inverse_basis = torch.FloatTensor( + np.linalg.pinv(scale * fourier_basis).T[:, None, :]) + + if window is not None: + assert(filter_length >= win_length) + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer('forward_basis', forward_basis.float()) + self.register_buffer('inverse_basis', inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode='reflect') + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data, + Variable(self.forward_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + + magnitude = torch.sqrt(real_part**2 + imag_part**2) + phase = torch.autograd.Variable( + torch.atan2(imag_part.data, real_part.data)) + + return magnitude, phase # [batch_size, F(513), T(1251)] + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) + + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + Variable(self.inverse_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, magnitude.size(-1), hop_length=self.hop_length, + win_length=self.win_length, n_fft=self.filter_length, + dtype=np.float32) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0]) + window_sum = torch.autograd.Variable( + torch.from_numpy(window_sum), requires_grad=False) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] + inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] + + return inverse_transform #[batch_size, 1, sample_num] + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction + +if __name__ == '__main__': + a = torch.randn(4, 320000) + stft = STFT() + mag, phase = stft.transform(a) + # rec_a = stft.inverse(mag, phase) + print(mag.shape) diff --git a/sound_extraction/utils/wav_io.py b/sound_extraction/utils/wav_io.py new file mode 100644 index 0000000000000000000000000000000000000000..79f9916ca2ba8250eae655f5ccf5b6ce6999034f --- /dev/null +++ b/sound_extraction/utils/wav_io.py @@ -0,0 +1,23 @@ +import librosa +import librosa.filters +import math +import numpy as np +import scipy.io.wavfile + +def load_wav(path): + max_length = 32000 * 10 + wav = librosa.core.load(path, sr=32000)[0] + if len(wav) > max_length: + audio = wav[0:max_length] + + # pad audio to max length, 10s for AudioCaps + if len(wav) < max_length: + # audio = torch.nn.functional.pad(audio, (0, self.max_length - audio.size(1)), 'constant') + wav = np.pad(wav, (0, max_length - len(wav)), 'constant') + wav = wav[...,None] + return wav + + +def save_wav(wav, path): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + scipy.io.wavfile.write(path, 32000, wav.astype(np.int16)) \ No newline at end of file