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add sensevoice & cosevoice (#2562)

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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}
}
```

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

13
python/sensevoice/app/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch/configuration.json Normal file
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{
"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":""}
}

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