freevc plugin

ckpts_freevc/freevc.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

ckpts_freevc/freevc.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2cc2d047f63b80d1d6780e37611cec11a01d597560393b1fe6118158b3bd47f
+size 472644351

dreamvoice/freevc/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dreamvoice/freevc/.gitignore

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+__pycache__
+flagged

dreamvoice/freevc/README.md

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+---
+title: FreeVC
+emoji: 🚀
+colorFrom: gray
+colorTo: red
+sdk: gradio
+sdk_version: 3.13.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

dreamvoice/freevc/app.py

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+import os
+import torch
+import librosa
+import gradio as gr
+from scipy.io.wavfile import write
+from transformers import WavLMModel
+
+import utils
+from models import SynthesizerTrn
+from mel_processing import mel_spectrogram_torch
+from speaker_encoder.voice_encoder import SpeakerEncoder
+
+'''
+def get_wavlm():
+    os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
+    shutil.move('WavLM-Large.pt', 'wavlm')
+'''
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# print("Loading FreeVC...")
+# hps = utils.get_hparams_from_file("configs/freevc.json")
+# freevc = SynthesizerTrn(
+#     hps.data.filter_length // 2 + 1,
+#     hps.train.segment_size // hps.data.hop_length,
+#     **hps.model).to(device)
+# _ = freevc.eval()
+# _ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None)
+smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
+
+print("Loading FreeVC(24k)...")
+hps = utils.get_hparams_from_file("configs/freevc-24.json")
+freevc_24 = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc_24.eval()
+_ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None)
+
+# print("Loading FreeVC-s...")
+# hps = utils.get_hparams_from_file("configs/freevc-s.json")
+# freevc_s = SynthesizerTrn(
+#     hps.data.filter_length // 2 + 1,
+#     hps.train.segment_size // hps.data.hop_length,
+#     **hps.model).to(device)
+# _ = freevc_s.eval()
+# _ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None)
+#
+# print("Loading WavLM for content...")
+cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
+ 
+def convert(model, cmodel, src, tgt):
+    with torch.no_grad():
+        # tgt
+        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
+        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
+        g_tgt = smodel.embed_utterance(wav_tgt)
+        g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
+
+        # src
+        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
+        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
+        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
+        # infer
+        if model == "FreeVC":
+            audio = freevc.infer(c, g=g_tgt)
+        elif model == "FreeVC-s":
+            audio = freevc_s.infer(c, mel=mel_tgt)
+        else:
+            audio = freevc_24.infer(c, g=g_tgt)
+        audio = audio[0][0].data.cpu().float().numpy()
+        if model == "FreeVC" or model == "FreeVC-s":
+            write("out.wav", hps.data.sampling_rate, audio)
+        else:
+            write("out.wav", 24000, audio)
+    out = "out.wav"
+    return out
+    
+# model = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC",type="value", label="Model")
+# audio1 = gr.inputs.Audio(label="Source Audio", type='filepath')
+# audio2 = gr.inputs.Audio(label="Reference Audio", type='filepath')
+# inputs = [model, audio1, audio2]
+# outputs = gr.outputs.Audio(label="Output Audio", type='filepath')
+#
+# title = "FreeVC"
+# description = "Gradio Demo for FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion. To use it, simply upload your audio, or click the example to load. Read more at the links below. Note: It seems that the WavLM checkpoint in HuggingFace is a little different from the one used to train FreeVC, which may degrade the performance a bit. In addition, speaker similarity can be largely affected if there are too much silence in the reference audio, so please <strong>trim</strong> it before submitting."
+# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.15418' target='_blank'>Paper</a> | <a href='https://github.com/OlaWod/FreeVC' target='_blank'>Github Repo</a></p>"
+#
+# examples=[["FreeVC", 'p225_001.wav', 'p226_002.wav'], ["FreeVC-s", 'p226_002.wav', 'p225_001.wav'], ["FreeVC (24kHz)", 'p225_001.wav', 'p226_002.wav']]
+#
+# gr.Interface(convert, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
+convert(freevc_24, cmodel, 'p225_001.wav', 'p226_002.wav')

dreamvoice/freevc/commons.py

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+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+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 get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+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 intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    ret[i] = x[i, :, idx_str:idx_end]
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def rand_spec_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@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
+
+
+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)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm

dreamvoice/freevc/configs/freevc-24.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8640,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8008"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,4,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

dreamvoice/freevc/configs/freevc-s.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": false
+  }
+}

dreamvoice/freevc/configs/freevc.json

@@ -0,0 +1,54 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

dreamvoice/freevc/mel_processing.py

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+import math
+import os
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.data
+import numpy as np
+import librosa
+import librosa.util as librosa_util
+from librosa.util import normalize, pad_center, tiny
+from scipy.signal import get_window
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    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 spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=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[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=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[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec

dreamvoice/freevc/models.py

@@ -0,0 +1,351 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .commons import sequence_mask, rand_slice_segments
+from .modules import ResidualCouplingLayer, WN, Flip, ResBlock1, ResBlock2, LRELU_SLOPE
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from .commons import init_weights, get_padding
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class Encoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_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.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = ResBlock1 if resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            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()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 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 DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, 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):
+        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 MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+        
+        
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+        
+    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
+        mel_slices = []
+        for i in range(0, total_frames-partial_frames, partial_hop):
+            mel_range = torch.arange(i, i+partial_frames)
+            mel_slices.append(mel_range)
+            
+        return mel_slices
+    
+    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
+        mel_len = mel.size(1)
+        last_mel = mel[:,-partial_frames:]
+        
+        if mel_len > partial_frames:
+            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
+            mels = list(mel[:,s] for s in mel_slices)
+            mels.append(last_mel)
+            mels = torch.stack(tuple(mels), 0).squeeze(1)
+        
+            with torch.no_grad():
+                partial_embeds = self(mels)
+            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
+            #embed = embed / torch.linalg.norm(embed, 2)
+        else:
+            with torch.no_grad():
+                embed = self(last_mel)
+        
+        return embed
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self, 
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock, 
+    resblock_kernel_sizes, 
+    resblock_dilation_sizes, 
+    upsample_rates, 
+    upsample_initial_channel, 
+    upsample_kernel_sizes,
+    gin_channels,
+    ssl_dim,
+    use_spk,
+    **kwargs):
+
+    super().__init__()
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_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.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.gin_channels = gin_channels
+    self.ssl_dim = ssl_dim
+    self.use_spk = use_spk
+
+    self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) 
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+    
+    if not self.use_spk:
+      self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
+
+  def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if spec_lengths == None:
+      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
+      
+    if not self.use_spk:
+      g = self.enc_spk(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+      
+    _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
+    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
+    z_p = self.flow(z, spec_mask, g=g)
+
+    z_slice, ids_slice = rand_slice_segments(z, spec_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    
+    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, c, g=None, mel=None, c_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if not self.use_spk:
+      g = self.enc_spk.embed_utterance(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+
+    z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
+    z = self.flow(z_p, c_mask, g=g, reverse=True)
+    o = self.dec(z * c_mask, g=g)
+    
+    return o

dreamvoice/freevc/modules.py

@@ -0,0 +1,341 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from .commons import init_weights, get_padding, fused_add_tanh_sigmoid_multiply
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    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):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+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 DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    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.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      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, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      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)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * 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):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        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, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        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, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        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, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x

dreamvoice/freevc/requirements.txt

@@ -0,0 +1,8 @@
+altair
+httpx==0.24.1
+numpy
+scipy
+torch
+transformers
+librosa
+webrtcvad==2.0.10

dreamvoice/freevc/utils.py

@@ -0,0 +1,305 @@
+import os
+import sys
+import argparse
+import logging
+import json
+import subprocess
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+from torch.nn import functional as F
+from .commons import sequence_mask
+
+MATPLOTLIB_FLAG = False
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+
+def get_cmodel(rank):
+    checkpoint = torch.load('wavlm/WavLM-Large.pt')
+    cfg = WavLMConfig(checkpoint['cfg'])
+    cmodel = WavLM(cfg).cuda(rank)
+    cmodel.load_state_dict(checkpoint['model'])
+    cmodel.eval()
+    return cmodel
+    
+    
+def get_content(cmodel, y):
+    with torch.no_grad():
+        c = cmodel.extract_features(y.squeeze(1))[0]
+    c = c.transpose(1, 2)
+    return c
+
+
+def get_vocoder(rank):
+    with open("hifigan/config.json", "r") as f:
+        config = json.load(f)
+    config = hifigan.AttrDict(config)
+    vocoder = hifigan.Generator(config)
+    ckpt = torch.load("hifigan/generator_v1")
+    vocoder.load_state_dict(ckpt["generator"])
+    vocoder.eval()
+    vocoder.remove_weight_norm()
+    vocoder.cuda(rank)
+    return vocoder
+    
+    
+def transform(mel, height): # 68-92
+    #r = np.random.random()
+    #rate = r * 0.3 + 0.85 # 0.85-1.15
+    #height = int(mel.size(-2) * rate)
+    tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
+    if height >= mel.size(-2):
+        return tgt[:, :mel.size(-2), :]
+    else:
+        silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1) 
+        silence += torch.randn_like(silence) / 10
+        return torch.cat((tgt, silence), 1)
+        
+        
+def stretch(mel, width): # 0.5-2
+    return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None):
+  assert os.path.isfile(checkpoint_path)
+  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+  iteration = checkpoint_dict['iteration']
+  learning_rate = checkpoint_dict['learning_rate']
+  if optimizer is not None:
+    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+  saved_state_dict = checkpoint_dict['model']
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  new_state_dict= {}
+  for k, v in state_dict.items():
+    try:
+      new_state_dict[k] = saved_state_dict[k]
+    except:
+      logger.info("%s is not in the checkpoint" % k)
+      new_state_dict[k] = v
+  if hasattr(model, 'module'):
+    model.module.load_state_dict(new_state_dict)
+  else:
+    model.load_state_dict(new_state_dict)
+  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
+    checkpoint_path, iteration))
+  return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+  logger.info("Saving model and optimizer state at iteration {} to {}".format(
+    iteration, checkpoint_path))
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  torch.save({'model': state_dict,
+              'iteration': iteration,
+              'optimizer': optimizer.state_dict(),
+              'learning_rate': learning_rate}, checkpoint_path)
+
+
+def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
+  for k, v in scalars.items():
+    writer.add_scalar(k, v, global_step)
+  for k, v in histograms.items():
+    writer.add_histogram(k, v, global_step)
+  for k, v in images.items():
+    writer.add_image(k, v, global_step, dataformats='HWC')
+  for k, v in audios.items():
+    writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+  f_list = glob.glob(os.path.join(dir_path, regex))
+  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+  x = f_list[-1]
+  print(x)
+  return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+  
+  fig, ax = plt.subplots(figsize=(10,2))
+  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                  interpolation='none')
+  plt.colorbar(im, ax=ax)
+  plt.xlabel("Frames")
+  plt.ylabel("Channels")
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+  global MATPLOTLIB_FLAG
+  if not MATPLOTLIB_FLAG:
+    import matplotlib
+    matplotlib.use("Agg")
+    MATPLOTLIB_FLAG = True
+    mpl_logger = logging.getLogger('matplotlib')
+    mpl_logger.setLevel(logging.WARNING)
+  import matplotlib.pylab as plt
+  import numpy as np
+
+  fig, ax = plt.subplots(figsize=(6, 4))
+  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                  interpolation='none')
+  fig.colorbar(im, ax=ax)
+  xlabel = 'Decoder timestep'
+  if info is not None:
+      xlabel += '\n\n' + info
+  plt.xlabel(xlabel)
+  plt.ylabel('Encoder timestep')
+  plt.tight_layout()
+
+  fig.canvas.draw()
+  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+  plt.close()
+  return data
+
+
+def load_wav_to_torch(full_path):
+  sampling_rate, data = read(full_path)
+  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(filename, split="|"):
+  with open(filename, encoding='utf-8') as f:
+    filepaths_and_text = [line.strip().split(split) for line in f]
+  return filepaths_and_text
+
+
+def get_hparams(init=True):
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
+                      help='JSON file for configuration')
+  parser.add_argument('-m', '--model', type=str, required=True,
+                      help='Model name')
+  
+  args = parser.parse_args()
+  model_dir = os.path.join("./logs", args.model)
+
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+
+  config_path = args.config
+  config_save_path = os.path.join(model_dir, "config.json")
+  if init:
+    with open(config_path, "r") as f:
+      data = f.read()
+    with open(config_save_path, "w") as f:
+      f.write(data)
+  else:
+    with open(config_save_path, "r") as f:
+      data = f.read()
+  config = json.loads(data)
+  
+  hparams = HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_dir(model_dir):
+  config_save_path = os.path.join(model_dir, "config.json")
+  with open(config_save_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  hparams.model_dir = model_dir
+  return hparams
+
+
+def get_hparams_from_file(config_path):
+  with open(config_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  return hparams
+
+
+def check_git_hash(model_dir):
+  source_dir = os.path.dirname(os.path.realpath(__file__))
+  if not os.path.exists(os.path.join(source_dir, ".git")):
+    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
+      source_dir
+    ))
+    return
+
+  cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+  path = os.path.join(model_dir, "githash")
+  if os.path.exists(path):
+    saved_hash = open(path).read()
+    if saved_hash != cur_hash:
+      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
+        saved_hash[:8], cur_hash[:8]))
+  else:
+    open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+  global logger
+  logger = logging.getLogger(os.path.basename(model_dir))
+  logger.setLevel(logging.DEBUG)
+  
+  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+  if not os.path.exists(model_dir):
+    os.makedirs(model_dir)
+  h = logging.FileHandler(os.path.join(model_dir, filename))
+  h.setLevel(logging.DEBUG)
+  h.setFormatter(formatter)
+  logger.addHandler(h)
+  return logger
+
+
+class HParams():
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      if type(v) == dict:
+        v = HParams(**v)
+      self[k] = v
+    
+  def keys(self):
+    return self.__dict__.keys()
+
+  def items(self):
+    return self.__dict__.items()
+
+  def values(self):
+    return self.__dict__.values()
+
+  def __len__(self):
+    return len(self.__dict__)
+
+  def __getitem__(self, key):
+    return getattr(self, key)
+
+  def __setitem__(self, key, value):
+    return setattr(self, key, value)
+
+  def __contains__(self, key):
+    return key in self.__dict__
+
+  def __repr__(self):
+    return self.__dict__.__repr__()

dreamvoice/freevc_wrapper.py

@@ -0,0 +1,63 @@
+import os
+import torch
+import librosa
+import soundfile as sf
+from pathlib import Path
+
+from transformers import WavLMModel
+from .freevc.utils import load_checkpoint, get_hparams_from_file
+from .freevc.models import SynthesizerTrn
+# from mel_processing import mel_spectrogram_torch
+# from free_vc.speaker_encoder.voice_encoder import SpeakerEncoder
+# from speaker_encoder.voice_encoder import SpeakerEncoder
+
+
+def get_freevc_models(path='freevc', speaker_path='../pre_ckpts/spk_encoder/pretrained.pt', device='cuda'):
+    hps = get_hparams_from_file(f"{path}/freevc.json")
+    freevc = SynthesizerTrn(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        **hps.model).to(device)
+    freevc.eval()
+    load_checkpoint(f"{path}/freevc.pth", freevc, None)
+
+    cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
+    cmodel.eval()
+
+    # smodel = spk_encoder.load_model(Path(speaker_path), device)
+    # smodel = spk_encoder.load_model(Path(f"speaker_encoder/ckpt/pretrained_bak_5805000.pt"), 'cuda')
+    # smodel = SpeakerEncoder(f"speaker_encoder/ckpt/pretrained_bak_5805000.pt", device)
+
+    return freevc, cmodel, hps
+
+
+@torch.no_grad()
+def convert(freevc, content, speaker):
+    audio = freevc.infer(content, g=speaker)
+    audio = audio[0][0].data.cpu().float().numpy()
+    return audio, 16000
+
+
+if __name__ == '__main__':
+    freevc_24, cmodel, smodel, hps = get_freevc_models()
+
+    tgt = 'p226_002.wav'
+    # src = 'p226_002.wav'
+    src = 'p225_001.wav'
+    device = 'cuda'
+
+    # tgt
+    wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
+    wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
+    g_tgt = smodel.embed_utterance(wav_tgt)
+    g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
+    # g_tgt = spk_encoder.embed_utterance_batch(torch.tensor(wav_tgt).unsqueeze(0).cuda())
+
+    # src
+    wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
+    wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
+    content = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
+
+    output, sr = convert(freevc_24, content, g_tgt)
+
+    sf.write('output.wav', output, sr)

dreamvoice/plugin.py

@@ -108,7 +108,6 @@ class DreamVoice_Plugin:
         self.spk_encoder = spk_encoder
         self.spk_embed_cache = None
 
-
     @torch.no_grad()
     def gen_spk(self, prompt,
                 prompt_guidance_scale=3, prompt_guidance_rescale=0.0,

dreamvoice/plugin_ckpts/freevc.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0589fd38d965a7f8aab6eb3bedae5d1c007acb0f305e04bbe0fd4a771fff717d
+size 104892189

dreamvoice/plugin_freevc.yaml

@@ -0,0 +1,8 @@
+version: 1.1
+
+lm_path: 'google/flan-t5-base'
+
+dreamvg:
+  config_path: 'src/configs/plugin_cross_freevc.yaml'
+  ckpt_path: 'plugin_ckpts/freevc.pt'
+  ckpt_url: 'https://huggingface.co/myshell-ai/DreamVoice/resolve/main/dreamvoice/plugin_ckpts/freevc.pt'

dreamvoice/train_utils/prepare_freevc/freevc/README.md

@@ -0,0 +1,13 @@
+---
+title: FreeVC
+emoji: 🚀
+colorFrom: gray
+colorTo: red
+sdk: gradio
+sdk_version: 3.13.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

dreamvoice/train_utils/prepare_freevc/freevc/app.py

@@ -0,0 +1,103 @@
+import os
+import torch
+import librosa
+import gradio as gr
+from scipy.io.wavfile import write
+from transformers import WavLMModel
+
+import utils
+from models import SynthesizerTrn
+from mel_processing import mel_spectrogram_torch
+from speaker_encoder.voice_encoder import SpeakerEncoder
+
+'''
+def get_wavlm():
+    os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
+    shutil.move('WavLM-Large.pt', 'wavlm')
+'''
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("Loading FreeVC...")
+hps = utils.get_hparams_from_file("configs/freevc.json")
+freevc = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc.eval()
+_ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None)
+smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
+
+print("Loading FreeVC(24k)...")
+hps = utils.get_hparams_from_file("configs/freevc-24.json")
+freevc_24 = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc_24.eval()
+_ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None)
+
+print("Loading FreeVC-s...")
+hps = utils.get_hparams_from_file("configs/freevc-s.json")
+freevc_s = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+_ = freevc_s.eval()
+_ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, None)
+
+print("Loading WavLM for content...")
+cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
+ 
+def convert(model, src, tgt):
+    with torch.no_grad():
+        # tgt
+        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
+        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
+        if model == "FreeVC" or model == "FreeVC (24kHz)":
+            g_tgt = smodel.embed_utterance(wav_tgt)
+            g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
+        else:
+            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
+            mel_tgt = mel_spectrogram_torch(
+                wav_tgt, 
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax
+            )
+        # src
+        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
+        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
+        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
+        # infer
+        if model == "FreeVC":
+            audio = freevc.infer(c, g=g_tgt)
+        elif model == "FreeVC-s":
+            audio = freevc_s.infer(c, mel=mel_tgt)
+        else:
+            audio = freevc_24.infer(c, g=g_tgt)
+        audio = audio[0][0].data.cpu().float().numpy()
+        if model == "FreeVC" or model == "FreeVC-s":
+            write("out.wav", hps.data.sampling_rate, audio)
+        else:
+            write("out.wav", 24000, audio)
+    out = "out.wav"
+    return out
+    
+model = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC",type="value", label="Model") 
+audio1 = gr.Audio(label="Source Audio", type='filepath')
+audio2 = gr.Audio(label="Reference Audio", type='filepath')
+inputs = [model, audio1, audio2]
+outputs =  gr.Audio(label="Output Audio", type='filepath')
+
+title = "FreeVC"
+description = "Gradio Demo for FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion. To use it, simply upload your audio, or click the example to load. Read more at the links below. Note: It seems that the WavLM checkpoint in HuggingFace is a little different from the one used to train FreeVC, which may degrade the performance a bit. In addition, speaker similarity can be largely affected if there are too much silence in the reference audio, so please <strong>trim</strong> it before submitting."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.15418' target='_blank'>Paper</a> | <a href='https://github.com/OlaWod/FreeVC' target='_blank'>Github Repo</a></p>"
+
+examples=[["FreeVC", 'p225_001.wav', 'p226_002.wav'], ["FreeVC-s", 'p226_002.wav', 'p225_001.wav'], ["FreeVC (24kHz)", 'p225_001.wav', 'p226_002.wav']]
+
+gr.Interface(convert, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()

dreamvoice/train_utils/prepare_freevc/freevc/commons.py

@@ -0,0 +1,171 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+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 get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+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 intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    ret[i] = x[i, :, idx_str:idx_end]
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def rand_spec_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@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
+
+
+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)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm

dreamvoice/train_utils/prepare_freevc/freevc/configs/freevc-24.json

@@ -0,0 +1,54 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8640,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8008"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,4,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

dreamvoice/train_utils/prepare_freevc/freevc/configs/freevc-s.json

@@ -0,0 +1,54 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": false
+  }
+}

dreamvoice/train_utils/prepare_freevc/freevc/configs/freevc.json

@@ -0,0 +1,54 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 10000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 8960,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 128,
+    "port": "8001"
+  },
+  "data": {
+    "training_files":"filelists/train.txt",
+    "validation_files":"filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 16000,
+    "filter_length": 1280,
+    "hop_length": 320,
+    "win_length": 1280,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 1024,
+    "use_spk": true
+  }
+}

dreamvoice/train_utils/prepare_freevc/freevc/freevc_pipeline.py

@@ -0,0 +1,69 @@
+import os
+import torch
+import torch.nn.functional as F
+import librosa
+import sounddevice as sd
+from transformers import WavLMModel
+from scipy.io.wavfile import write
+from models import SynthesizerTrn
+from speaker_encoder.voice_encoder import SpeakerEncoder
+import utils
+import numpy as np
+from transformers import T5Tokenizer, T5EncoderModel
+from src.plugin_wrapper import DreamVG
+import soundfile as sf
+
+
+# Load configurations and models
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("Loading FreeVC...")
+hps = utils.get_hparams_from_file("configs/freevc.json")
+freevc = SynthesizerTrn(
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).to(device)
+freevc.eval()
+utils.load_checkpoint("checkpoints/freevc.pth", freevc, None)
+
+print("Loading Speaker Encoder...")
+smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
+
+print("Loading WavLM for content...")
+cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
+
+lm_path = 'google/flan-t5-base'
+tokenizer = T5Tokenizer.from_pretrained(lm_path)
+text_encoder = T5EncoderModel.from_pretrained(lm_path).to(device).eval()
+
+dreamvg = DreamVG(config_path='src/configs/plugin_cross.yaml',
+                  ckpt_path='checkpoints/dreamvc_plugin.pt',
+                  device=device)
+
+
+prompt = "girl's voice, very young and cute"
+prompt_guidance_scale = 3.0
+
+text_batch = tokenizer(prompt, max_length=32,
+                       padding='max_length', truncation=True, return_tensors="pt")
+text, text_mask = text_batch.input_ids.to(device), \
+    text_batch.attention_mask.to(device)
+text = text_encoder(input_ids=text, attention_mask=text_mask)[0]
+target_embedding = dreamvg.inference([text, text_mask],
+                                     guidance_scale=prompt_guidance_scale,
+                                     guidance_rescale=0.0,
+                                     ddim_steps=100, eta=1,
+                                     random_seed=None)
+
+# Convert to tensor and pad
+audio, sr = librosa.load('segment_1.mp3', sr=16000)
+audio = torch.from_numpy(audio).unsqueeze(0).to(device).float()
+audio = F.pad(audio, (40, 40))
+
+# Extract content features using WavLM
+c = cmodel(audio).last_hidden_state.transpose(1, 2).to(device)
+
+audio = freevc.infer(c, g=target_embedding)
+audio = audio[0][0].data.cpu().float().numpy()
+
+sf.write('freevc_out.wav', audio, 16000)

dreamvoice/train_utils/prepare_freevc/freevc/mel_processing.py

@@ -0,0 +1,112 @@
+import math
+import os
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.data
+import numpy as np
+import librosa
+import librosa.util as librosa_util
+from librosa.util import normalize, pad_center, tiny
+from scipy.signal import get_window
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    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 spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=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[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    fmax_dtype_device = str(fmax) + '_' + dtype_device
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=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[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec

dreamvoice/train_utils/prepare_freevc/freevc/models.py

@@ -0,0 +1,351 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class Encoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_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.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            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()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 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, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, 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):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.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_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+        
+        
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+        
+    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
+        mel_slices = []
+        for i in range(0, total_frames-partial_frames, partial_hop):
+            mel_range = torch.arange(i, i+partial_frames)
+            mel_slices.append(mel_range)
+            
+        return mel_slices
+    
+    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
+        mel_len = mel.size(1)
+        last_mel = mel[:,-partial_frames:]
+        
+        if mel_len > partial_frames:
+            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
+            mels = list(mel[:,s] for s in mel_slices)
+            mels.append(last_mel)
+            mels = torch.stack(tuple(mels), 0).squeeze(1)
+        
+            with torch.no_grad():
+                partial_embeds = self(mels)
+            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
+            #embed = embed / torch.linalg.norm(embed, 2)
+        else:
+            with torch.no_grad():
+                embed = self(last_mel)
+        
+        return embed
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self, 
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock, 
+    resblock_kernel_sizes, 
+    resblock_dilation_sizes, 
+    upsample_rates, 
+    upsample_initial_channel, 
+    upsample_kernel_sizes,
+    gin_channels,
+    ssl_dim,
+    use_spk,
+    **kwargs):
+
+    super().__init__()
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_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.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.gin_channels = gin_channels
+    self.ssl_dim = ssl_dim
+    self.use_spk = use_spk
+
+    self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) 
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+    
+    if not self.use_spk:
+      self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
+
+  def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if spec_lengths == None:
+      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
+      
+    if not self.use_spk:
+      g = self.enc_spk(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+      
+    _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
+    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
+    z_p = self.flow(z, spec_mask, g=g)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    
+    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, c, g=None, mel=None, c_lengths=None):
+    if c_lengths == None:
+      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    if not self.use_spk:
+      g = self.enc_spk.embed_utterance(mel.transpose(1,2))
+    g = g.unsqueeze(-1)
+
+    z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
+    z = self.flow(z_p, c_mask, g=g, reverse=True)
+    o = self.dec(z * c_mask, g=g)
+    
+    return o

dreamvoice/train_utils/prepare_freevc/freevc/modules.py

@@ -0,0 +1,342 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import commons
+from commons import init_weights, get_padding
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    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):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+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 DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    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.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      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, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      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 = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * 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):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        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, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        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, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        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, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x

dreamvoice/train_utils/prepare_freevc/freevc/requirements.txt

@@ -0,0 +1,8 @@
+altair
+httpx==0.24.1
+numpy
+scipy
+torch
+transformers
+librosa
+webrtcvad==2.0.10

dreamvoice/train_utils/prepare_freevc/freevc/src/configs/diffvc_base.yaml

@@ -0,0 +1,47 @@
+version: 1.0
+
+system: "base"
+
+model:
+  cls_embedding:
+    speaker_dim: 256
+    feature_dim: 512
+    content_dim: 768
+    content_hidden: 256
+    use_pitch: false
+
+  unet:  
+    sample_size: [128, 256]
+    in_channels: 257
+    out_channels: 1
+    layers_per_block: 2
+    block_out_channels: [128, 256, 256, 512]
+    down_block_types:
+      [
+        "DownBlock2D",
+        "DownBlock2D",
+        "AttnDownBlock2D",
+        "AttnDownBlock2D",
+      ]
+    up_block_types:
+      [
+        "AttnUpBlock2D",
+        "AttnUpBlock2D",
+        "UpBlock2D",
+        "UpBlock2D"
+      ]
+    attention_head_dim: 32
+    class_embed_type: 'identity'
+
+scheduler:
+  num_train_steps: 1000
+  beta_schedule: 'linear'
+  beta_start: 0.0001
+  beta_end: 0.02
+  num_infer_steps: 50
+  rescale_betas_zero_snr: true
+  timestep_spacing: "trailing"
+  clip_sample: false
+  prediction_type: 'v_prediction'
+  scale: 2.75
+  shift: 5.80

dreamvoice/train_utils/prepare_freevc/freevc/src/configs/diffvc_base_pitch.yaml

@@ -0,0 +1,34 @@
+version: 1.0
+
+system: "base"
+
+diffwrap:
+  cls_embedding:
+    speaker_dim: 256
+    feature_dim: 512
+    content_dim: 768
+    content_hidden: 256
+    use_pitch: true
+    pitch_dim: 1
+    pitch_hidden: 128
+    
+  unet:  
+    sample_size: [128, 256]
+    in_channels: 385
+    out_channels: 1
+    layers_per_block: 2
+    block_out_channels: [128, 256, 512]
+    down_block_types:
+      [
+        "DownBlock2D",
+        "AttnDownBlock2D",
+        "AttnDownBlock2D",
+      ]
+    up_block_types:
+      [
+        "AttnUpBlock2D",
+        "AttnUpBlock2D",
+        "UpBlock2D"
+      ]
+    attention_head_dim: 32
+    class_embed_type: 'identity'

dreamvoice/train_utils/prepare_freevc/freevc/src/configs/diffvc_cross.yaml

@@ -0,0 +1,45 @@
+version: 1.0
+
+system: "cross"
+
+model:
+  cls_embedding:
+    content_dim: 768
+    content_hidden: 256
+    use_pitch: false
+
+  unet:  
+    sample_size: [128, 256]
+    in_channels: 257
+    out_channels: 1
+    layers_per_block: 2
+    block_out_channels: [128, 256, 256, 512]
+    down_block_types:
+      [
+        "DownBlock2D",
+        "DownBlock2D",
+        "CrossAttnDownBlock2D",
+        "CrossAttnDownBlock2D",
+      ]
+    up_block_types:
+      [
+        "CrossAttnUpBlock2D",
+        "CrossAttnUpBlock2D",
+        "UpBlock2D",
+        "UpBlock2D",
+      ]
+    attention_head_dim: 32
+    cross_attention_dim: 768
+  
+scheduler:
+  num_train_steps: 1000
+  beta_schedule: 'linear'
+  beta_start: 0.0001
+  beta_end: 0.02
+  num_infer_steps: 50
+  rescale_betas_zero_snr: true
+  timestep_spacing: "trailing"
+  clip_sample: false
+  prediction_type: 'v_prediction'
+  scale: 2.75
+  shift: 5.80

dreamvoice/train_utils/prepare_freevc/freevc/src/configs/diffvc_cross_pitch.yaml

@@ -0,0 +1,33 @@
+version: 1.0
+
+system: "cross"
+
+diffwrap:
+  cls_embedding:
+    content_dim: 768
+    content_hidden: 256
+    use_pitch: true
+    pitch_dim: 1
+    pitch_hidden: 128
+
+  unet:  
+    sample_size: [100, 256]
+    in_channels: 385
+    out_channels: 1
+    layers_per_block: 2
+    block_out_channels: [128, 256, 512]
+    down_block_types:
+      [
+        "DownBlock2D",
+        "CrossAttnDownBlock2D",
+        "CrossAttnDownBlock2D",
+      ]
+    up_block_types:
+      [
+        "CrossAttnUpBlock2D",
+        "CrossAttnUpBlock2D",
+        "UpBlock2D",
+      ]
+    attention_head_dim: 32
+    cross_attention_dim: 768
+    

dreamvoice/train_utils/prepare_freevc/freevc/src/configs/plugin_cross.yaml

@@ -0,0 +1,39 @@
+version: 1.0
+
+system: "cross"
+
+model:
+  cls_embedding:
+    content_dim: 768
+    content_hidden: 256
+
+  unet:  
+    sample_size: [1, 1]
+    in_channels: 256
+    out_channels: 256
+    layers_per_block: 2
+    block_out_channels: [256]
+    down_block_types:
+      [
+        "CrossAttnDownBlock2D",
+      ]
+    up_block_types:
+      [
+        "CrossAttnUpBlock2D",
+      ]
+    attention_head_dim: 32
+    cross_attention_dim: 768
+
+scheduler:
+  num_train_steps: 1000
+  beta_schedule: 'linear'
+  beta_start: 0.0001
+  beta_end: 0.02
+  num_infer_steps: 50
+  rescale_betas_zero_snr: true
+  timestep_spacing: "trailing"
+  clip_sample: false
+  prediction_type: 'v_prediction'
+  scale: 0.05 
+  shift: -0.035
+    

dreamvoice/train_utils/prepare_freevc/freevc/src/extract_features.py

@@ -0,0 +1,103 @@
+import os
+import torch
+import librosa
+import numpy as np
+import soundfile as sf
+import pandas as pd
+# from feats.hubert_model import get_soft_model, get_hubert_soft_content
+from feats.contentvec_hf import get_content_model, get_content
+# from modules.speaker_encoder.encoder import inference as spk_encoder
+# from pathlib import Path
+from tqdm import tqdm
+from multiprocessing import Process
+import pyworld as pw
+
+
+def resample_save(infolder, audio_path, model,
+                  audio_sr=24000, content_sr=16000, min_length=1.92,
+                  content_resolution=50,
+                  save_path='features'):
+    if os.path.isfile(save_path + '/' + 'audio_24k/' + audio_path) is False:
+        audio, sr = librosa.load(infolder + audio_path, sr=content_sr)
+        final_length = audio.shape[-1] // (content_sr / content_resolution) * (content_sr / content_resolution)
+        # final_length = final_length / content_sr
+
+        length = max(round(min_length*content_sr), round(final_length))
+        assert length % 10 == 0
+        audio = audio[:length]
+        audio_save = np.zeros(length, dtype=audio.dtype)
+        audio_save[:audio.shape[-1]] = audio[:audio.shape[-1]]
+
+        # content = get_hubert_soft_content(model, torch.tensor(audio_save).unsqueeze(0))
+        content = get_content(model, torch.tensor(audio_save).unsqueeze(0))
+        content = content.cpu()
+        os.makedirs(os.path.dirname(save_path + '/' + 'content/' + audio_path), exist_ok=True)
+        torch.save(content, save_path + '/' + 'content/' + audio_path+'.pt')
+        # print(audio_save.shape)
+        # print(content.shape)
+        os.makedirs(os.path.dirname(save_path + '/' + 'audio_16k/' + audio_path), exist_ok=True)
+        sf.write(save_path + '/' + 'audio_16k/' + audio_path, audio_save, int(sr))
+        # print(save_path + '/' + 'audio_16k/' + audio_path)
+
+        audio, sr = librosa.load(infolder + audio_path, sr=audio_sr)
+        length = max(round(min_length*audio_sr), round(final_length/content_sr*audio_sr))
+        assert length % 10 == 0
+        audio = audio[:length]
+        audio_save = np.zeros(length, dtype=audio.dtype)
+        audio_save[:audio.shape[-1]] = audio[:audio.shape[-1]]
+        # print(audio_save.shape)
+        os.makedirs(os.path.dirname(save_path + '/' + 'audio_24k/' + audio_path), exist_ok=True)
+        sf.write(save_path + '/' + 'audio_24k/' + audio_path, audio_save, int(sr))
+
+
+def extract_f0(in_folder, audio_path, save_path):
+    audio, sr = librosa.load(in_folder + audio_path, sr=None)
+    assert sr == 16000
+    if os.path.isfile(save_path + '/' + 'f0/' + audio_path + '.pt') is False:
+        # wav = audio
+        # wav = np.pad(wav, int((1024-320)/2), mode='reflect')
+        # f0_, _, _ = librosa.pyin(wav, frame_length=1024, hop_length=320, center=False, sr=sr,
+        #                         fmin=librosa.note_to_hz('C2'),
+        #                         fmax=librosa.note_to_hz('C6'))
+
+        _f0, t = pw.dio(audio.astype(np.float64), sr, frame_period=320 / sr * 1000)
+        f0 = pw.stonemask(audio.astype(np.float64), _f0, t, sr)[:-1]
+
+        f0 = np.nan_to_num(f0)
+        os.makedirs(os.path.dirname(save_path + '/' + 'f0/' + audio_path), exist_ok=True)
+        # print(save_path + '/' + 'f0/' + audio_path + '.pt')
+        torch.save(torch.tensor(f0), save_path + '/' + 'f0/' + audio_path + '.pt')
+
+
+def chunks(arr, m):
+    result = [[] for i in range(m)]
+    for i in range(len(arr)):
+        result[i%m].append(arr[i])
+    return result
+
+
+def extract_f0_main(in_folder, audio_paths, save_path):
+    for audio_path in tqdm(audio_paths):
+        extract_f0(in_folder, audio_path, save_path)
+
+
+if __name__ == '__main__':
+    df = pd.read_csv('../test_data/vc_meta.csv')
+    # model = get_soft_model('../pre_ckpts/hubert_soft.pt').to('cuda')
+    model = get_content_model().to('cuda')
+    # # spk_encoder.load_model(Path('ckpts/spk_encoder/pretrained.pt'), device="cuda")
+    for i in tqdm(range(len(df))):
+        row = df.iloc[i]
+        in_path = row['path']
+        resample_save('../test_data/', in_path, model, save_path='../features/')
+
+    in_folder = '../features/audio_16k/'
+    audio_files = list(df['path'])
+    save_path = '../features/'
+    cores = 6
+
+    subsets = chunks(audio_files, cores)
+
+    for subset in subsets:
+        t = Process(target=extract_f0_main, args=(in_folder, subset, save_path))
+        t.start()

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/contentvec.py

@@ -0,0 +1,42 @@
+import torch
+import librosa
+from fairseq import checkpoint_utils
+import torch.nn.functional as F
+
+
+def get_model(vec_path):
+    print("load model(s) from {}".format(vec_path))
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [vec_path],
+        suffix="",
+    )
+    model = models[0]
+    model.eval()
+    return model
+
+
+@torch.no_grad()
+def get_content(hmodel, wav_16k_tensor, device='cuda', layer=12):
+    # print(layer)
+    wav_16k_tensor = wav_16k_tensor.to(device)
+    # so that the output shape will be len(audio//320)
+    wav_16k_tensor = F.pad(wav_16k_tensor, ((400 - 320) // 2, (400 - 320) // 2))
+    feats = wav_16k_tensor
+    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+    inputs = {
+        "source": feats.to(wav_16k_tensor.device),
+        "padding_mask": padding_mask.to(wav_16k_tensor.device),
+        "output_layer": layer
+    }
+    logits = hmodel.extract_features(**inputs)[0]
+    # feats = hmodel.final_proj(logits[0])
+    return logits
+
+
+if __name__ == '__main__':
+    audio, sr = librosa.load('test.wav', sr=16000)
+    audio = audio[:100*320]
+    model = get_model('../../ckpts/checkpoint_best_legacy_500.pt')
+    model = model.cuda()
+    content = get_content(model, torch.tensor([audio]))
+    print(content)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/contentvec_hf.py

@@ -0,0 +1,40 @@
+from transformers import HubertModel
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+import librosa
+
+
+class HubertModelWithFinalProj(HubertModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        # The final projection layer is only used for backward compatibility.
+        # Following https://github.com/auspicious3000/contentvec/issues/6
+        # Remove this layer is necessary to achieve the desired outcome.
+        self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size)
+
+
+def get_content_model(config='lengyue233/content-vec-best'):
+    model = HubertModelWithFinalProj.from_pretrained(config)
+    model.eval()
+    return model
+
+
+@torch.no_grad()
+def get_content(model, wav_16k_tensor, device='cuda'):
+    # print(layer)
+    wav_16k_tensor = wav_16k_tensor.to(device)
+    # so that the output shape will be len(audio//320)
+    wav_16k_tensor = F.pad(wav_16k_tensor, ((400 - 320) // 2, (400 - 320) // 2))
+    logits = model(wav_16k_tensor)['last_hidden_state']
+    return logits
+
+
+if __name__ == '__main__':
+    model = get_content_model().cuda()
+    audio, sr = librosa.load('test.wav', sr=16000)
+    audio = audio[:100*320]
+    audio = torch.tensor([audio])
+    content = get_content(model, audio, 'cuda')
+    print(content)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/.gitignore

@@ -0,0 +1,132 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# VSCode project settings
+.vscode
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Benjamin van Niekerk
+
+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.

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/README.md

@@ -0,0 +1,161 @@
+# HuBERT
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2111.02392)
+[![demo](https://img.shields.io/static/v1?message=Audio%20Samples&logo=Github&labelColor=grey&color=blue&logoColor=white&label=%20&style=flat)](https://bshall.github.io/soft-vc/)
+[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bshall/soft-vc/blob/main/soft-vc-demo.ipynb)
+
+Training and inference scripts for the HuBERT content encoders in [A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion](https://ieeexplore.ieee.org/abstract/document/9746484).
+For more details see [soft-vc](https://github.com/bshall/soft-vc). Audio samples can be found [here](https://bshall.github.io/soft-vc/). Colab demo can be found [here](https://colab.research.google.com/github/bshall/soft-vc/blob/main/soft-vc-demo.ipynb).
+
+<div align="center">
+    <img width="100%" alt="Soft-VC"
+      src="https://raw.githubusercontent.com/bshall/hubert/main/content-encoder.png">
+</div>
+<div>
+  <sup>
+    <strong>Fig 1:</strong> Architecture of the voice conversion system. a) The <strong>discrete</strong> content encoder clusters audio features to produce a sequence of discrete speech units. b) The <strong>soft</strong> content encoder is trained to predict the discrete units. The acoustic model transforms the discrete/soft speech units into a target spectrogram. The vocoder converts the spectrogram into an audio waveform.
+  </sup>
+</div>
+
+## Example Usage
+
+### Programmatic Usage
+
+```python
+import torch, torchaudio
+
+# Load checkpoint (either hubert_soft or hubert_discrete)
+hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).cuda()
+
+# Load audio
+wav, sr = torchaudio.load("path/to/wav")
+assert sr == 16000
+wav = wav.unsqueeze(0).cuda()
+
+# Extract speech units
+units = hubert.units(x)
+```
+
+### Script-Based Usage
+
+```
+usage: encode.py [-h] [--extension EXTENSION] {soft,discrete} in-dir out-dir
+
+Encode an audio dataset.
+
+positional arguments:
+  {soft,discrete}       available models (HuBERT-Soft or HuBERT-Discrete)
+  in-dir                path to the dataset directory.
+  out-dir               path to the output directory.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --extension EXTENSION
+                        extension of the audio files (defaults to .flac).
+```
+
+## Training
+
+### Step 1: Dataset Preparation
+
+Download and extract the [LibriSpeech](https://www.openslr.org/12) corpus. The training script expects the following tree structure for the dataset directory:
+
+```
+│   lengths.json
+│
+└───wavs
+    ├───dev-*
+    │   ├───84
+    │   ├───...
+    │   └───8842
+    └───train-*
+        ├───19
+        ├───...
+        └───8975
+```
+
+The `train-*` and `dev-*` directories should contain the training and validation splits respectively. Note that there can be multiple `train` and `dev` folders e.g., `train-clean-100`, `train-other-500`, etc. Finally, the `lengths.json` file should contain key-value pairs with the file path and number of samples:
+
+```json
+{
+    "dev-clean/1272/128104/1272-128104-0000": 93680,
+    "dev-clean/1272/128104/1272-128104-0001": 77040,
+}
+```
+
+### Step 2: Extract Discrete Speech Units
+
+Encode LibriSpeech using the HuBERT-Discrete model and `encode.py` script:
+
+```
+usage: encode.py [-h] [--extension EXTENSION] {soft,discrete} in-dir out-dir
+
+Encode an audio dataset.
+
+positional arguments:
+  {soft,discrete}       available models (HuBERT-Soft or HuBERT-Discrete)
+  in-dir                path to the dataset directory.
+  out-dir               path to the output directory.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --extension EXTENSION
+                        extension of the audio files (defaults to .flac).
+```
+
+for example:
+
+```
+python encode.py discrete path/to/LibriSpeech/wavs path/to/LibriSpeech/discrete
+```
+
+At this point the directory tree should look like:
+
+```
+│   lengths.json
+│
+├───discrete
+│   ├───...
+└───wavs
+    ├───...
+```
+
+### Step 3: Train the HuBERT-Soft Content Encoder
+
+```
+usage: train.py [-h] [--resume RESUME] [--warmstart] [--mask] [--alpha ALPHA] dataset-dir checkpoint-dir
+
+Train HuBERT soft content encoder.
+
+positional arguments:
+  dataset-dir      path to the data directory.
+  checkpoint-dir   path to the checkpoint directory.
+
+optional arguments:
+  -h, --help       show this help message and exit
+  --resume RESUME  path to the checkpoint to resume from.
+  --warmstart      whether to initialize from the fairseq HuBERT checkpoint.
+  --mask           whether to use input masking.
+  --alpha ALPHA    weight for the masked loss.
+```
+
+## Links
+
+- [Soft-VC repo](https://github.com/bshall/soft-vc)
+- [Soft-VC paper](https://ieeexplore.ieee.org/abstract/document/9746484)
+- [Official HuBERT repo](https://github.com/pytorch/fairseq)
+- [HuBERT paper](https://arxiv.org/abs/2106.07447)
+
+## Citation
+
+If you found this work helpful please consider citing our paper:
+
+```
+@inproceedings{
+    soft-vc-2022,
+    author={van Niekerk, Benjamin and Carbonneau, Marc-André and Zaïdi, Julian and Baas, Matthew and Seuté, Hugo and Kamper, Herman},
+    booktitle={ICASSP}, 
+    title={A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion}, 
+    year={2022}
+}
+```

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/cluster.py

@@ -0,0 +1,66 @@
+from pathlib import Path
+import logging
+import argparse
+
+import torch
+import numpy as np
+from sklearn.cluster import KMeans
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def cluster(args):
+    with open(args.subset) as file:
+        subset = [line.strip() for line in file]
+
+    logger.info(f"Loading features from {args.in_dir}")
+    features = []
+    for path in subset:
+        in_path = args.in_dir / path
+        features.append(np.load(in_path.with_suffix(".npy")))
+    features = np.concatenate(features, axis=0)
+
+    logger.info(f"Clustering features of shape: {features.shape}")
+    kmeans = KMeans(n_clusters=args.n_clusters).fit(features)
+
+    checkpoint_path = args.checkpoint_dir / f"kmeans_{args.n_clusters}.pt"
+    checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
+    torch.save(
+        checkpoint_path,
+        {
+            "n_features_in_": kmeans.n_features_in_,
+            "_n_threads": kmeans._n_threads,
+            "cluster_centers_": kmeans.cluster_centers_,
+        },
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Cluster speech features features.")
+    parser.add_argument(
+        "in_dir",
+        metavar="in-dir",
+        help="path to the encoded dataset",
+        type=Path,
+    )
+    parser.add_argument(
+        "subset",
+        matavar="subset",
+        help="path to the .txt file containing the list of files to cluster",
+        type=Path,
+    )
+    parser.add_argument(
+        "checkpoint_dir",
+        metavar="checkpoint-dir",
+        help="path to the checkpoint directory",
+        type=Path,
+    )
+    parser.add_argument(
+        "--n-clusters",
+        help="number of clusters",
+        type=int,
+        default=100,
+    )
+    args = parser.parse_args()
+    cluster(args)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/encode.py

@@ -0,0 +1,60 @@
+import argparse
+import logging
+import numpy as np
+from pathlib import Path
+from tqdm import tqdm
+
+import torch
+import torchaudio
+from torchaudio.functional import resample
+
+
+def encode_dataset(args):
+    print(f"Loading hubert checkpoint")
+    hubert = torch.hub.load(
+        "bshall/hubert:main",
+        f"hubert_{args.model}",
+        trust_repo=True,
+    ).cuda()
+
+    print(f"Encoding dataset at {args.in_dir}")
+    for in_path in tqdm(list(args.in_dir.rglob(f"*{args.extension}"))):
+        wav, sr = torchaudio.load(in_path)
+        wav = resample(wav, sr, 16000)
+        wav = wav.unsqueeze(0).cuda()
+
+        with torch.inference_mode():
+            units = hubert.units(wav)
+
+        out_path = args.out_dir / in_path.relative_to(args.in_dir)
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        np.save(out_path.with_suffix(".npy"), units.squeeze().cpu().numpy())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Encode an audio dataset.")
+    parser.add_argument(
+        "model",
+        help="available models (HuBERT-Soft or HuBERT-Discrete)",
+        choices=["soft", "discrete"],
+    )
+    parser.add_argument(
+        "in_dir",
+        metavar="in-dir",
+        help="path to the dataset directory.",
+        type=Path,
+    )
+    parser.add_argument(
+        "out_dir",
+        metavar="out-dir",
+        help="path to the output directory.",
+        type=Path,
+    )
+    parser.add_argument(
+        "--extension",
+        help="extension of the audio files (defaults to .flac).",
+        default=".flac",
+        type=str,
+    )
+    args = parser.parse_args()
+    encode_dataset(args)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/hubconf.py

@@ -0,0 +1,80 @@
+dependencies = ["torch", "torchaudio", "sklearn"]
+
+URLS = {
+    "hubert-discrete": "https://github.com/bshall/hubert/releases/download/v0.2/hubert-discrete-96b248c5.pt",
+    "hubert-soft": "https://github.com/bshall/hubert/releases/download/v0.2/hubert-soft-35d9f29f.pt",
+    "kmeans100": "https://github.com/bshall/hubert/releases/download/v0.2/kmeans100-50f36a95.pt",
+}
+
+import torch
+from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
+
+from sklearn.cluster import KMeans
+
+from hubert import HubertDiscrete, HubertSoft
+
+
+def hubert_discrete(
+    pretrained: bool = True,
+    progress: bool = True,
+) -> HubertDiscrete:
+    r"""HuBERT-Discrete from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        pretrained (bool): load pretrained weights into the model
+        progress (bool): show progress bar when downloading model
+    """
+    kmeans = kmeans100(pretrained=pretrained, progress=progress)
+    hubert = HubertDiscrete(kmeans)
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            URLS["hubert-discrete"], progress=progress
+        )
+        consume_prefix_in_state_dict_if_present(checkpoint["hubert"], "module.")
+        hubert.load_state_dict(checkpoint["hubert"])
+        hubert.eval()
+    return hubert
+
+
+def hubert_soft(
+    pretrained: bool = True,
+    progress: bool = True,
+) -> HubertSoft:
+    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        pretrained (bool): load pretrained weights into the model.
+        progress (bool): show progress bar when downloading model.
+    """
+    hubert = HubertSoft()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            URLS["hubert-soft"],
+            progress=progress,
+        )
+        consume_prefix_in_state_dict_if_present(checkpoint["hubert"], "module.")
+        hubert.load_state_dict(checkpoint["hubert"])
+        hubert.eval()
+    return hubert
+
+
+def _kmeans(
+    num_clusters: int, pretrained: bool = True, progress: bool = True
+) -> KMeans:
+    kmeans = KMeans(num_clusters)
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            URLS[f"kmeans{num_clusters}"], progress=progress
+        )
+        kmeans.__dict__["n_features_in_"] = checkpoint["n_features_in_"]
+        kmeans.__dict__["_n_threads"] = checkpoint["_n_threads"]
+        kmeans.__dict__["cluster_centers_"] = checkpoint["cluster_centers_"].numpy()
+    return kmeans
+
+
+def kmeans100(pretrained: bool = True, progress: bool = True) -> KMeans:
+    r"""
+    k-means checkpoint for HuBERT-Discrete with 100 clusters.
+    Args:
+        pretrained (bool): load pretrained weights into the model
+        progress (bool): show progress bar when downloading model
+    """
+    return _kmeans(100, pretrained, progress)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/hubert/__init__.py

@@ -0,0 +1,5 @@
+from .model import (
+    Hubert,
+    HubertDiscrete,
+    HubertSoft,
+)

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/hubert/dataset.py

@@ -0,0 +1,91 @@
+import random
+from pathlib import Path
+import numpy as np
+import json
+
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+import torchaudio
+
+
+class AcousticUnitsDataset(Dataset):
+    def __init__(
+        self,
+        root: Path,
+        sample_rate: int = 16000,
+        label_rate: int = 50,
+        min_samples: int = 32000,
+        max_samples: int = 250000,
+        train: bool = True,
+    ):
+        self.wavs_dir = root / "wavs"
+        self.units_dir = root / "discrete"
+
+        with open(root / "lengths.json") as file:
+            self.lenghts = json.load(file)
+
+        pattern = "train-*/**/*.flac" if train else "dev-*/**/*.flac"
+        metadata = (
+            (path, path.relative_to(self.wavs_dir).with_suffix("").as_posix())
+            for path in self.wavs_dir.rglob(pattern)
+        )
+        metadata = ((path, key) for path, key in metadata if key in self.lenghts)
+        self.metadata = [
+            path for path, key in metadata if self.lenghts[key] > min_samples
+        ]
+
+        self.sample_rate = sample_rate
+        self.label_rate = label_rate
+        self.min_samples = min_samples
+        self.max_samples = max_samples
+        self.train = train
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def __getitem__(self, index):
+        wav_path = self.metadata[index]
+        units_path = self.units_dir / wav_path.relative_to(self.wavs_dir)
+
+        wav, _ = torchaudio.load(wav_path)
+        wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        codes = np.load(units_path.with_suffix(".npy"))
+
+        return wav, torch.from_numpy(codes).long()
+
+    def collate(self, batch):
+        wavs, codes = zip(*batch)
+        wavs, codes = list(wavs), list(codes)
+
+        wav_lengths = [wav.size(-1) for wav in wavs]
+        code_lengths = [code.size(-1) for code in codes]
+
+        wav_frames = min(self.max_samples, *wav_lengths)
+
+        collated_wavs, wav_offsets = [], []
+        for wav in wavs:
+            wav_diff = wav.size(-1) - wav_frames
+            wav_offset = random.randint(0, wav_diff)
+            wav = wav[:, wav_offset : wav_offset + wav_frames]
+
+            collated_wavs.append(wav)
+            wav_offsets.append(wav_offset)
+
+        rate = self.label_rate / self.sample_rate
+        code_offsets = [round(wav_offset * rate) for wav_offset in wav_offsets]
+        code_frames = round(wav_frames * rate)
+        remaining_code_frames = [
+            length - offset for length, offset in zip(code_lengths, code_offsets)
+        ]
+        code_frames = min(code_frames, *remaining_code_frames)
+
+        collated_codes = []
+        for code, code_offset in zip(codes, code_offsets):
+            code = code[code_offset : code_offset + code_frames]
+            collated_codes.append(code)
+
+        wavs = torch.stack(collated_wavs, dim=0)
+        codes = torch.stack(collated_codes, dim=0)
+
+        return wavs, codes

dreamvoice/train_utils/prepare_freevc/freevc/src/feats/hubert/hubert/model.py

@@ -0,0 +1,241 @@
+import copy
+from typing import Optional, Tuple
+import random
+
+from sklearn.cluster import KMeans
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Hubert(nn.Module):
+    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
+        super().__init__()
+        self._mask = mask
+        self.feature_extractor = FeatureExtractor()
+        self.feature_projection = FeatureProjection()
+        self.positional_embedding = PositionalConvEmbedding()
+        self.norm = nn.LayerNorm(768)
+        self.dropout = nn.Dropout(0.1)
+        self.encoder = TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                768, 12, 3072, activation="gelu", batch_first=True
+            ),
+            12,
+        )
+        self.proj = nn.Linear(768, 256)
+
+        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
+        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
+
+    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        mask = None
+        if self.training and self._mask:
+            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
+            x[mask] = self.masked_spec_embed.to(x.dtype)
+        return x, mask
+
+    def encode(
+        self, x: torch.Tensor, layer: Optional[int] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = self.feature_extractor(x)
+        x = self.feature_projection(x.transpose(1, 2))
+        x, mask = self.mask(x)
+        x = x + self.positional_embedding(x)
+        x = self.dropout(self.norm(x))
+        x = self.encoder(x, output_layer=layer)
+        return x, mask
+
+    def logits(self, x: torch.Tensor) -> torch.Tensor:
+        logits = torch.cosine_similarity(
+            x.unsqueeze(2),
+            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        )
+        return logits / 0.1
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x, mask = self.encode(x)
+        x = self.proj(x)
+        logits = self.logits(x)
+        return logits, mask
+
+
+class HubertSoft(Hubert):
+    """HuBERT-Soft content encoder from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`."""
+
+    def __init__(self):
+        super().__init__()
+
+    @torch.inference_mode()
+    def units(self, wav: torch.Tensor) -> torch.Tensor:
+        """Extract soft speech units.
+
+        Args:
+            wav (Tensor): an audio waveform of shape (1, 1, T), where T is the number of samples.
+
+        Returns:
+            Tensor: soft speech units of shape (1, N, D), where N is the number of frames and D is the unit dimensions.
+        """
+        wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav)
+        return self.proj(x)
+
+
+class HubertDiscrete(Hubert):
+    """HuBERT-Discrete content encoder from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`."""
+
+    def __init__(self, kmeans: KMeans):
+        super().__init__(504)
+        self.kmeans = kmeans
+
+    @torch.inference_mode()
+    def units(self, wav: torch.Tensor) -> torch.LongTensor:
+        """Extract discrete speech units.
+
+        Args:
+            wav (Tensor): an audio waveform of shape (1, 1, T), where T is the number of samples.
+
+        Returns:
+            LongTensor: soft speech units of shape (N,), where N is the number of frames.
+        """
+        wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav, layer=7)
+        x = self.kmeans.predict(x.squeeze().cpu().numpy())
+        return torch.tensor(x, dtype=torch.long, device=wav.device)
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
+        self.norm0 = nn.GroupNorm(512, 512)
+        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
+        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.gelu(self.norm0(self.conv0(x)))
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = F.gelu(self.conv3(x))
+        x = F.gelu(self.conv4(x))
+        x = F.gelu(self.conv5(x))
+        x = F.gelu(self.conv6(x))
+        return x
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = nn.LayerNorm(512)
+        self.projection = nn.Linear(512, 768)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            768,
+            768,
+            kernel_size=128,
+            padding=128 // 2,
+            groups=16,
+        )
+        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x.transpose(1, 2))
+        x = F.gelu(x[:, :, :-1])
+        return x.transpose(1, 2)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
+    ) -> None:
+        super(TransformerEncoder, self).__init__()
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+    def forward(
+        self,
+        src: torch.Tensor,
+        mask: torch.Tensor = None,
+        src_key_padding_mask: torch.Tensor = None,
+        output_layer: Optional[int] = None,
+    ) -> torch.Tensor:
+        output = src
+        for layer in self.layers[:output_layer]:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+        return output
+
+
+def _compute_mask(
+    shape: Tuple[int, int],
+    mask_prob: float,
+    mask_length: int,
+    device: torch.device,
+    min_masks: int = 0,
+) -> torch.Tensor:
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = torch.ones(
+        (batch_size, sequence_length - (mask_length - 1)), device=device
+    )
+
+    # get random indices to mask
+    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    mask_indices = (
+        mask_indices.unsqueeze(dim=-1)
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    offsets = (
+        torch.arange(mask_length, device=device)[None, None, :]
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    mask_idxs = mask_indices + offsets
+
+    # scatter indices to mask
+    mask = mask.scatter(1, mask_idxs, True)
+
+    return mask