add sensevoice & cosevoice (#2562)

Signed-off-by: EthanD <EthanD4869@gmail.com>
Co-authored-by: EthanD <EthanD4869@gmail.com>

python/sensevoice/app/iic/SenseVoiceSmall/.mv

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+Revision:master,CreatedAt:1720157464

python/sensevoice/app/iic/SenseVoiceSmall/README.md

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+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- auto-speech-recognition
+
+#model-type:
+##如 gpt、phi、llama、chatglm、baichuan 等
+#- gpt
+
+#domain:
+##如 nlp、cv、audio、multi-modal
+#- nlp
+
+#language:
+##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+#- cn 
+
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+#- pretrained
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+#- vllm
+---
+
+# Highlights
+**SenseVoice**专注于高精度多语言语音识别、情感辨识和音频事件检测
+- **多语言识别：** 采用超过40万小时数据训练，支持超过50种语言，识别效果上优于Whisper模型。
+- **富文本识别：** 
+  - 具备优秀的情感识别，能够在测试数据上达到和超过目前最佳情感识别模型的效果。
+  - 支持声音事件检测能力，支持音乐、掌声、笑声、哭声、咳嗽、喷嚏等多种常见人机交互事件进行检测。
+- **高效推理：** SenseVoice-Small模型采用非自回归端到端框架，推理延迟极低，10s音频推理仅耗时70ms，15倍优于Whisper-Large。
+- **微调定制：** 具备便捷的微调脚本与策略，方便用户根据业务场景修复长尾样本问题。
+- **服务部署：** 具有完整的服务部署链路，支持多并发请求，支持客户端语言有，python、c++、html、java与c#等。
+
+
+## <strong>[SenseVoice开源项目介绍]()</strong>
+<strong>[SenseVoice]()</strong>开源模型是多语言音频理解模型，具有包括语音识别、语种识别、语音情感识别，声学事件检测能力。
+
+[**github仓库**]()
+| [**最新动态**]()
+| [**环境安装**]()
+
+# 模型结构图
+SenseVoice多语言音频理解模型，支持语音识别、语种识别、语音情感识别、声学事件检测、逆文本正则化等能力，采用工业级数十万小时的标注音频进行模型训练，保证了模型的通用识别效果。模型可以被应用于中文、粤语、英语、日语、韩语音频识别，并输出带有情感和事件的富文本转写结果。
+
+<p align="center">
+<img src="fig/sensevoice.png" alt="SenseVoice模型结构"  width="1500" />
+</p>
+
+SenseVoice-Small是基于非自回归端到端框架模型，为了指定任务，我们在语音特征前添加四个嵌入作为输入传递给编码器：
+- LID：用于预测音频语种标签。
+- SER：用于预测音频情感标签。
+- AED：用于预测音频包含的事件标签。
+- ITN：用于指定识别输出文本是否进行逆文本正则化。
+
+
+# 用法
+
+## 推理
+
+### modelscope pipeline推理
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='iic/SenseVoiceSmall',
+    model_revision="master")
+
+rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+```
+
+### 直接推理
+
+```python
+from model import SenseVoiceSmall
+
+model_dir = "iic/SenseVoiceSmall"
+m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
+
+
+res = m.inference(
+    data_in="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+    language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=False,
+    **kwargs,
+)
+
+print(res)
+```
+
+### 使用funasr推理
+
+```python
+from funasr import AutoModel
+
+model_dir = "iic/SenseVoiceSmall"
+input_file = (
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
+)
+
+model = AutoModel(model=model_dir,
+                  vad_model="fsmn-vad",
+                  vad_kwargs={"max_single_segment_time": 30000},
+                  trust_remote_code=True, device="cuda:0")
+
+res = model.generate(
+    input=input_file,
+    cache={},
+    language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=False,
+    batch_size_s=0,
+)
+
+print(res)
+```
+
+funasr版本已经集成了vad模型，支持任意时长音频输入，`batch_size_s`单位为秒。
+如果输入均为短音频，并且需要批量化推理，为了加快推理效率，可以移除vad模型，并设置`batch_size`
+
+```python
+model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
+
+res = model.generate(
+    input=input_file,
+    cache={},
+    language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=False,
+    batch_size=64,
+)
+```
+
+更多详细用法，请参考 [文档](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
+
+## 模型下载
+
+
+SDK下载
+```bash
+#安装ModelScope
+pip install modelscope
+```
+```python
+#SDK模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('iic/SenseVoiceSmall')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/iic/SenseVoiceSmall.git
+```
+
+## 服务部署
+
+Undo
+
+# Performance
+
+## 语音识别效果
+我们在开源基准数据集（包括 AISHELL-1、AISHELL-2、Wenetspeech、Librispeech和Common Voice）上比较了SenseVoice与Whisper的多语言语音识别性能和推理效率。在中文和粤语识别效果上，SenseVoice-Small模型具有明显的效果优势。
+
+<p align="center">
+<img src="fig/asr_results.png" alt="SenseVoice模型在开源测试集上的表现"  width="2500" />
+</p>
+
+
+
+## 情感识别效果
+由于目前缺乏被广泛使用的情感识别测试指标和方法，我们在多个测试集的多种指标进行测试，并与近年来Benchmark上的多个结果进行了全面的对比。所选取的测试集同时包含中文/英文两种语言以及表演、影视剧、自然对话等多种风格的数据，在不进行目标数据微调的前提下，SenseVoice能够在测试数据上达到和超过目前最佳情感识别模型的效果。
+
+<p align="center">
+<img src="fig/ser_table.png" alt="SenseVoice模型SER效果1"  width="1500" />
+</p>
+
+同时，我们还在测试集上对多个开源情感识别模型进行对比，结果表明，SenseVoice-Large模型可以在几乎所有数据上都达到了最佳效果，而SenseVoice-Small模型同样可以在多数数据集上取得超越其他开源模型的效果。
+
+<p align="center">
+<img src="fig/ser_figure.png" alt="SenseVoice模型SER效果2"  width="500" />
+</p>
+
+## 事件检测效果
+
+尽管SenseVoice只在语音数据上进行训练，它仍然可以作为事件检测模型进行单独使用。我们在环境音分类ESC-50数据集上与目前业内广泛使用的BEATS与PANN模型的效果进行了对比。SenseVoice模型能够在这些任务上取得较好的效果，但受限于训练数据与训练方式，其事件分类效果专业的事件检测模型相比仍然有一定的差距。
+
+<p align="center">
+<img src="fig/aed_figure.png" alt="SenseVoice模型AED效果"  width="500" />
+</p>
+
+
+
+## 推理效率
+SenseVoice-Small模型采用非自回归端到端架构，推理延迟极低。在参数量与Whisper-Small模型相当的情况下，比Whisper-Small模型推理速度快7倍，比Whisper-Large模型快17倍。同时SenseVoice-small模型在音频时长增加的情况下，推理耗时也无明显增加。
+
+
+<p align="center">
+<img src="fig/inference.png" alt="SenseVoice模型的推理效率"  width="1500" />
+</p>
+
+<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>

python/sensevoice/app/iic/SenseVoiceSmall/config.yaml

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+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+
+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

python/sensevoice/app/iic/SenseVoiceSmall/configuration.json

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+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}

python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/.mv

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+Revision:master,CreatedAt:1707184291

python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/README.md

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+---
+tasks:
+- voice-activity-detection
+domain:
+- audio
+model-type:
+- VAD model
+frameworks:
+- pytorch
+backbone:
+- fsmn
+metrics:
+- f1_score
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- FunASR
+- FSMN
+- Alibaba
+- Online
+datasets:
+  train:
+  - 20,000 hour industrial Mandarin task
+  test:
+  - 20,000 hour industrial Mandarin task
+widgets:
+  - task: voice-activity-detection
+    model_revision: v2.0.4
+    inputs:
+      - type: audio
+        name: input
+        title: 音频
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: git://example/vad_example.wav 
+    inferencespec:
+      cpu: 1 #CPU数量
+      memory: 4096
+---
+
+# FSMN-Monophone VAD 模型介绍
+
+[//]: # (FSMN-Monophone VAD 模型)
+
+## Highlight
+- 16k中文通用VAD模型：可用于检测长语音片段中有效语音的起止时间点。
+  - 基于[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)场景的使用
+  - 基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)，可进行ASR，VAD，[中文标点](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)的自由组合
+  - 基于音频数据的有效语音片段起止时间点检测
+
+## <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！
+
+[**github仓库**](https://github.com/alibaba-damo-academy/FunASR)
+| [**最新动态**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
+| [**环境安装**](https://github.com/alibaba-damo-academy/FunASR#installation)
+| [**服务部署**](https://www.funasr.com)
+| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)
+| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
+
+
+## 模型原理介绍
+
+FSMN-Monophone VAD是达摩院语音团队提出的高效语音端点检测模型，用于检测输入音频中有效语音的起止时间点信息，并将检测出来的有效音频片段输入识别引擎进行识别，减少无效语音带来的识别错误。
+
+<p align="center">
+<img src="fig/struct.png" alt="VAD模型结构"  width="500" />
+
+FSMN-Monophone VAD模型结构如上图所示：模型结构层面，FSMN模型结构建模时可考虑上下文信息，训练和推理速度快，且时延可控；同时根据VAD模型size以及低时延的要求，对FSMN的网络结构、右看帧数进行了适配。在建模单元层面，speech信息比较丰富，仅用单类来表征学习能力有限，我们将单一speech类升级为Monophone。建模单元细分，可以避免参数平均，抽象学习能力增强，区分性更好。
+
+## 基于ModelScope进行推理
+
+- 推理支持音频格式如下：
+  - wav文件路径，例如：data/test/audios/vad_example.wav
+  - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav
+  - wav二进制数据，格式bytes，例如：用户直接从文件里读出bytes数据或者是麦克风录出bytes数据。
+  - 已解析的audio音频，例如：audio, rate = soundfile.read("vad_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
+  - wav.scp文件，需符合如下要求：
+
+```sh
+cat wav.scp
+vad_example1  data/test/audios/vad_example1.wav
+vad_example2  data/test/audios/vad_example2.wav
+...
+```
+
+- 若输入格式wav文件url，api调用方式可参考如下范例：
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model='iic/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    model_revision="v2.0.4",
+)
+
+segments_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
+print(segments_result)
+```
+
+- 输入音频为pcm格式，调用api时需要传入音频采样率参数fs，例如：
+
+```python
+segments_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.pcm', fs=16000)
+```
+
+- 若输入格式为文件wav.scp(注：文件名需要以.scp结尾)，可添加 output_dir 参数将识别结果写入文件中，参考示例如下：
+
+```python
+inference_pipeline(input="wav.scp", output_dir='./output_dir')
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+    └── text
+
+1 directory, 1 files
+```
+text：VAD检测语音起止时间点结果文件（单位：ms）
+
+- 若输入音频为已解析的audio音频，api调用方式可参考如下范例：
+
+```python
+import soundfile
+
+waveform, sample_rate = soundfile.read("vad_example_zh.wav")
+segments_result = inference_pipeline(input=waveform)
+print(segments_result)
+```
+
+- VAD常用参数调整说明（参考：vad.yaml文件）：
+  - max_end_silence_time：尾部连续检测到多长时间静音进行尾点判停，参数范围500ms～6000ms，默认值800ms(该值过低容易出现语音提前截断的情况)。
+  - speech_noise_thres：speech的得分减去noise的得分大于此值则判断为speech，参数范围：（-1,1）
+    - 取值越趋于-1，噪音被误判定为语音的概率越大，FA越高
+    - 取值越趋于+1，语音被误判定为噪音的概率越大，Pmiss越高
+    - 通常情况下，该值会根据当前模型在长语音测试集上的效果取balance
+    
+
+
+
+## 基于FunASR进行推理
+
+下面为快速上手教程，测试音频（[中文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav)，[英文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav)）
+
+### 可执行命令行
+在命令行终端执行：
+
+```shell
+funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=vad_example.wav
+```
+
+注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id   wav_path`
+
+### python示例
+#### 非实时语音识别
+```python
+from funasr import AutoModel
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh", model_revision="v2.0.4",
+                  vad_model="fsmn-vad", vad_model_revision="v2.0.4",
+                  punc_model="ct-punc-c", punc_model_revision="v2.0.4",
+                  # spk_model="cam++", spk_model_revision="v2.0.2",
+                  )
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
+            batch_size_s=300, 
+            hotword='魔搭')
+print(res)
+```
+注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
+
+#### 实时语音识别
+
+```python
+from funasr import AutoModel
+
+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="paraformer-zh-streaming", model_revision="v2.0.4")
+
+import soundfile
+import os
+
+wav_file = os.path.join(model.model_path, "example/asr_example.wav")
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = chunk_size[1] * 960 # 600ms
+
+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)
+```
+
+注：`chunk_size`为流式延时配置，`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`，未来信息为`5*60=300ms`。每次推理输入为`600ms`（采样点数为`16000*0.6=960`），输出为对应文字，最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。
+
+#### 语音端点检测（非实时）
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### 语音端点检测（实时）
+```python
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+
+import soundfile
+
+wav_file = f"{model.model_path}/example/vad_example.wav"
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = int(chunk_size * sample_rate / 1000)
+
+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)
+    if len(res[0]["value"]):
+        print(res)
+```
+
+#### 标点恢复
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc", model_revision="v2.0.4")
+
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
+print(res)
+```
+
+#### 时间戳预测
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fa-zh", model_revision="v2.0.4")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
+print(res)
+```
+
+更多详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+
+## 微调
+
+详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+
+
+
+
+## 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：可以直接对长语音数据进行计算，有效语音片段的起止时间点信息（单位：ms）。
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{zhang2018deep,
+  title={Deep-FSMN for large vocabulary continuous speech recognition},
+  author={Zhang, Shiliang and Lei, Ming and Yan, Zhijie and Dai, Lirong},
+  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={5869--5873},
+  year={2018},
+  organization={IEEE}
+}
+```

python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/am.mvn

@@ -0,0 +1,8 @@
+<Nnet>
+<Splice> 400 400
+[ 0 ]
+<AddShift> 400 400
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+</Nnet>

python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/config.yaml

@@ -0,0 +1,56 @@
+frontend: WavFrontendOnline
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 5
+    lfr_n: 1
+
+model: FsmnVADStreaming
+model_conf:
+    sample_rate: 16000
+    detect_mode: 1 
+    snr_mode: 0
+    max_end_silence_time: 800
+    max_start_silence_time: 3000
+    do_start_point_detection: True
+    do_end_point_detection: True
+    window_size_ms: 200
+    sil_to_speech_time_thres: 150
+    speech_to_sil_time_thres: 150
+    speech_2_noise_ratio: 1.0
+    do_extend: 1
+    lookback_time_start_point: 200
+    lookahead_time_end_point: 100
+    max_single_segment_time: 60000
+    snr_thres: -100.0
+    noise_frame_num_used_for_snr: 100
+    decibel_thres: -100.0
+    speech_noise_thres: 0.6
+    fe_prior_thres: 0.0001
+    silence_pdf_num: 1
+    sil_pdf_ids: [0]
+    speech_noise_thresh_low: -0.1
+    speech_noise_thresh_high: 0.3
+    output_frame_probs: False
+    frame_in_ms: 10
+    frame_length_ms: 25
+    
+encoder: FSMN
+encoder_conf:
+    input_dim: 400
+    input_affine_dim: 140
+    fsmn_layers: 4
+    linear_dim: 250
+    proj_dim: 128
+    lorder: 20
+    rorder: 0
+    lstride: 1
+    rstride: 0
+    output_affine_dim: 140
+    output_dim: 248
+
+

python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/configuration.json

@@ -0,0 +1,13 @@
+{
+  "framework": "pytorch",
+  "task" : "voice-activity-detection",
+  "pipeline": {"type":"funasr-pipeline"},
+  "model": {"type" : "funasr"},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "frontend_conf":{"cmvn_file": "am.mvn"}},
+  "model_name_in_hub": {
+    "ms":"iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", 
+    "hf":""}
+}