319 lines
10 KiB
Markdown
319 lines
10 KiB
Markdown
---
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tasks:
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- auto-speech-recognition
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domain:
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- audio
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model-type:
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- autoregressive
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frameworks:
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- pytorch
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backbone:
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- transformer/conformer
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metrics:
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- CER
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license: Apache License 2.0
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language:
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- multilingual
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tags:
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- FunASR
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- Whisper
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datasets:
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train:
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- 680,000 hour
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test:
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- test
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indexing:
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results:
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- task:
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name: Automatic Speech Recognition
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dataset:
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name: 680,000 hour
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metrics:
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- type: CER
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value: 8.53% # float
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description: greedy search, withou lm, avg.
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args: default
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- type: RTF
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value: 0.0251 # float
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description: GPU inference on V100
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args: batch_size=1
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widgets:
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- task: auto-speech-recognition
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model_revision: v1.0.0
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inputs:
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- type: audio
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name: input
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title: 音频
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examples:
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- name: 1
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title: 示例1
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inputs:
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- name: input
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data: git://example/asr_example.wav
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inferencespec:
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cpu: 8 #CPU数量
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memory: 4096
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---
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# Whisper模型介绍
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## <strong>[ModelScope-FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>
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<strong>[FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>希望在语音识别方面建立学术研究和工业应用之间的桥梁。通过支持在ModelScope上发布的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，并促进语音识别生态系统的发展。
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[**最新动态**](https://github.com/alibaba-damo-academy/FunASR#whats-new)
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| [**环境安装**](https://github.com/alibaba-damo-academy/FunASR#installation)
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| [**介绍文档**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
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| [**中文教程**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
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| [**服务部署**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
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| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
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| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
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## 基于ModelScope进行推理
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- 推理支持音频格式如下：
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- wav文件路径，例如：data/test/audios/asr_example.wav
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- pcm文件路径，例如：data/test/audios/asr_example.pcm
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- wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
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- 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。
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- 已解析的audio音频，例如：audio, rate = soundfile.read("asr_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
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- wav.scp文件，需符合如下要求：
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```sh
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cat wav.scp
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asr_example1 data/test/audios/asr_example1.wav
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asr_example2 data/test/audios/asr_example2.wav
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...
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```
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- 若输入格式wav文件url，api调用方式可参考如下范例：
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='iic/Whisper-large-v3', model_revision="v2.0.5")
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rec_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', language=None)
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print(rec_result)
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```
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- 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：
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```python
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rec_result = inference_pipeline(input='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.pcm', fs=16000)
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```
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- 输入音频为wav格式，api调用方式可参考如下范例:
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```python
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rec_result = inference_pipeline(input'asr_example_zh.wav')
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```
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- 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，api调用方式可参考如下范例:
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```python
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inference_pipeline(input="wav.scp", output_dir='./output_dir')
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```
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识别结果输出路径结构如下：
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```sh
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tree output_dir/
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output_dir/
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└── 1best_recog
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├── score
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└── text
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1 directory, 3 files
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```
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score：识别路径得分
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text：语音识别结果文件
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- 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：
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```python
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import soundfile
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waveform, sample_rate = soundfile.read("asr_example_zh.wav")
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rec_result = inference_pipeline(input=waveform)
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```
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- ASR、VAD、PUNC模型自由组合
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可根据使用需求对VAD和PUNC标点模型进行自由组合，使用方式如下：
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```python
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', model_revision="v2.0.4",
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vad_model='iic/speech_fsmn_vad_zh-cn-16k-common-pytorch', vad_model_revision="v2.0.4",
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punc_model='iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', punc_model_revision="v2.0.4",
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# spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
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# spk_model_revision="v2.0.2",
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)
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```
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若不使用PUNC模型，可配置punc_model=""，或不传入punc_model参数，如需加入LM模型，可增加配置lm_model='damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch'，并设置lm_weight和beam_size参数。
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## 基于FunASR进行推理
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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)）
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### 可执行命令行
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在命令行终端执行：
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```shell
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funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=vad_example.wav
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```
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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`
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### python示例
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#### 非实时语音识别
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```python
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from funasr import AutoModel
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# paraformer-zh is a multi-functional asr model
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# use vad, punc, spk or not as you need
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model = AutoModel(model="paraformer-zh", model_revision="v2.0.4",
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vad_model="fsmn-vad", vad_model_revision="v2.0.4",
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punc_model="ct-punc-c", punc_model_revision="v2.0.4",
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# spk_model="cam++", spk_model_revision="v2.0.2",
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)
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res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
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batch_size_s=300,
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hotword='魔搭')
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print(res)
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```
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注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
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#### 实时语音识别
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```python
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from funasr import AutoModel
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chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
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decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
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model = AutoModel(model="paraformer-zh-streaming", model_revision="v2.0.4")
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import soundfile
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import os
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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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speech, sample_rate = soundfile.read(wav_file)
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chunk_stride = chunk_size[1] * 960 # 600ms
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cache = {}
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total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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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)
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print(res)
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```
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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。
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#### 语音端点检测（非实时）
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```python
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from funasr import AutoModel
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model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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res = model.generate(input=wav_file)
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print(res)
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```
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#### 语音端点检测（实时）
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```python
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from funasr import AutoModel
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chunk_size = 200 # ms
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model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
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import soundfile
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wav_file = f"{model.model_path}/example/vad_example.wav"
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speech, sample_rate = soundfile.read(wav_file)
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chunk_stride = int(chunk_size * sample_rate / 1000)
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cache = {}
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total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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if len(res[0]["value"]):
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print(res)
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```
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#### 标点恢复
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```python
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from funasr import AutoModel
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model = AutoModel(model="ct-punc", model_revision="v2.0.4")
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res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
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print(res)
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```
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#### 时间戳预测
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```python
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from funasr import AutoModel
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model = AutoModel(model="fa-zh", model_revision="v2.0.4")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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text_file = f"{model.model_path}/example/text.txt"
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res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
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print(res)
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```
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更多详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
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## 微调
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详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
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## 使用方式以及适用范围
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运行范围
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- 支持Linux-x86_64、Mac和Windows运行。
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使用方式
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- 直接推理：可以直接对输入音频进行解码，输出目标文字。
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使用范围与目标场景
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- 适合于离线语音识别场景
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## 模型局限性以及可能的偏差
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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。
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## 相关论文以及引用信息
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```BibTeX
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@inproceedings{radford2023robust,
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title={Robust speech recognition via large-scale weak supervision},
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author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
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booktitle={International Conference on Machine Learning},
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pages={28492--28518},
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year={2023},
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organization={PMLR}
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}
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```
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