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+dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example.wav filter=lfs diff=lfs merge=lfs -text
+dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example_普通话.wav filter=lfs diff=lfs merge=lfs -text
+dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example_粤语.wav filter=lfs diff=lfs merge=lfs -text

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/.mv

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+Revision:master,CreatedAt:1707363184

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/README.md

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+---
+tasks:
+- auto-speech-recognition
+domain:
+- audio
+model-type:
+- Non-autoregressive
+frameworks:
+- pytorch
+backbone:
+- transformer/conformer
+metrics:
+- CER
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- FunASR
+- Paraformer
+- Alibaba
+- INTERSPEECH 2022
+datasets:
+  train:
+  - 60,000 hour industrial Mandarin task
+  test:
+  - AISHELL-1 dev/test
+  - AISHELL-2 dev_android/dev_ios/dev_mic/test_android/test_ios/test_mic
+  - WentSpeech dev/test_meeting/test_net
+  - SpeechIO TIOBE
+  - 60,000 hour industrial Mandarin task
+indexing:
+   results:
+   - task:
+       name: Automatic Speech Recognition
+     dataset:
+       name: 60,000 hour industrial Mandarin task
+       type: audio    # optional
+       args: 16k sampling rate, 8404 characters  # optional
+     metrics:
+       - type: CER
+         value: 8.53%  # float
+         description: greedy search, withou lm, avg.
+         args: default
+       - type: RTF
+         value: 0.0251  # float
+         description: GPU inference on V100
+         args: batch_size=1
+widgets:
+  - task: auto-speech-recognition
+    inputs:
+      - type: audio
+        name: input
+        title: 音频
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: git://example/asr_example.wav
+    inferencespec:
+      cpu: 8 #CPU数量
+      memory: 4096
+
+finetune-support: True
+---
+
+# 模型介绍
+基于Paraformer online large(iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online)，更换vocab，增加粤语部分字，通过在普通话1w小时、粤语100小时、英语1w小时音频数据集上进行训练1轮。此版本已不再更新，后续请关注vocab 11666版本。
+
+## <strong>[FunASR开源项目介绍](https://github.com/alibaba-damo-academy/FunASR)</strong>
+<strong>[FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>希望在语音识别的学术研究和工业应用之间架起一座桥梁。通过发布工业级语音识别模型的训练和微调，研究人员和开发人员可以更方便地进行语音识别模型的研究和生产，并推动语音识别生态的发展。让语音识别更有趣！
+
+#### 基于ModelScope进行推理
+
+- 流式语音识别api调用方式可参考如下范例：
+
+```python
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+import soundfile
+import os
+chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
+encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
+decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
+
+model = AutoModel(model="dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online", model_revision="master")
+cache = {}
+wav_file = os.path.join(model.model_path, "example/asr_example_普通话.wav")
+res = model.generate(input=wav_file,
+            chunk_size=chunk_size,
+            encoder_chunk_look_back=encoder_chunk_look_back,
+            decoder_chunk_look_back=decoder_chunk_look_back,
+            )
+print(res)
+
+
+
+wav_file = os.path.join(model.model_path, "example/asr_example_粤语.wav")
+speech, sample_rate = soundfile.read(wav_file)
+
+chunk_stride = chunk_size[1] * 960 # 600ms、480ms
+
+cache = {}
+
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(input=speech_chunk,
+                cache=cache,
+                is_final=is_final,
+                chunk_size=chunk_size,
+                encoder_chunk_look_back=encoder_chunk_look_back,
+                decoder_chunk_look_back=decoder_chunk_look_back,
+                )
+    print(res)
+```
+
+## 使用方式以及适用范围
+
+运行范围
+- 支持Linux-x86_64、Mac和Windows运行。
+
+使用方式
+- 直接推理：可以直接对输入音频进行解码，输出目标文字。
+- 微调：加载训练好的模型，采用私有或者开源数据进行模型训练。
+
+使用范围与目标场景
+- 适合于实时语音识别场景。
+
+
+## 模型局限性以及可能的偏差
+
+考虑到特征提取流程和工具以及训练工具差异，会对CER的数据带来一定的差异（<0.1%），推理GPU环境差异导致的RTF数值差异。
+
+
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{gao2022paraformer,
+  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
+  author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
+  booktitle={INTERSPEECH},
+  year={2022}
+}
+```

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/am.mvn

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+</Nnet> 

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/config.yaml

@@ -0,0 +1,143 @@
+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: ParaformerStreaming
+model_conf:
+    ctc_weight: 0.0
+    lsm_weight: 0.1
+    length_normalized_loss: true
+    predictor_weight: 1.0
+    predictor_bias: 1
+    sampling_ratio: 0.75
+
+# encoder
+encoder: SANMEncoderChunkOpt
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe_online
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+    chunk_size:
+    - 12
+    - 15
+    stride:
+    - 8
+    - 10
+    pad_left:
+    - 0
+    - 0
+    encoder_att_look_back_factor:
+    - 4
+    - 4
+    decoder_att_look_back_factor:
+    - 1
+    - 1
+
+# decoder
+decoder: ParaformerSANMDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 16
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.1
+    src_attention_dropout_rate: 0.1
+    att_layer_num: 16
+    kernel_size: 11
+    sanm_shfit: 5
+
+predictor: CifPredictorV2
+predictor_conf:
+    idim: 512
+    threshold: 1.0
+    l_order: 1
+    r_order: 1
+    tail_threshold: 0.45
+
+# frontend related
+frontend: WavFrontendOnline
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 6
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 150
+  val_scheduler_criterion:
+      - valid
+      - acc
+  best_model_criterion:
+  -   - valid
+      - acc
+      - max
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: DynamicBatchLocalShuffleSampler
+    batch_type: example # example or length
+    batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 500
+    shuffle: True
+    num_workers: 0
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+  split_with_space: true
+
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/configuration.json

@@ -0,0 +1,14 @@
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"token_list": "tokens.json", "seg_dict_file": "seg_dict"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example.wav

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+oid sha256:a1bd32dc78493c123f9625a66deee562aed2895f53fbc39f2cca3be7e6f4f20f
+size 177572

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example_普通话.wav

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+version https://git-lfs.github.com/spec/v1
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+size 177572

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/example/asr_example_粤语.wav

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+size 206670

dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/model.pt

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dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/tokens.txt

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dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/write_tokens_from_txt.py

@@ -0,0 +1,19 @@
+import json
+from pathlib import Path
+import sys
+
+config = sys.argv[1]
+token_list_out = sys.argv[2]
+
+config = Path(config)
+
+token_list = []
+
+with config.open("r", encoding="utf-8") as f:
+	for idx, line in enumerate(f):
+		line = line.rstrip()
+		token_list.append(line)
+
+file = open(token_list_out, 'w', encoding='utf-8')
+json.dump(token_list, file, ensure_ascii=False, indent=4)
+file.close()

km_iter_3000.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3f240ec9a524644ffd3790b6c568dd1554adfc366e76ddc4d6309a121a05fcc
+size 2097280

speech_tokenizer_v1.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56285ddd4a83e883ee0cb9f8d69c1089b53a94b1f78ff7e4a0224a27eb4cb486
+size 522625011