164 lines
5.6 KiB
Markdown
164 lines
5.6 KiB
Markdown
---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- auto-speech-recognition
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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# Highlights
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- SenseVoice-Small模型支持中、粤、英、日、韩多语言语音识别，在中、粤识别效果上明显优于Whisper模型。
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- SenseVoice-Small模型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。
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- SenseVoice-Small模型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。
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- SenseVoice-Small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。
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- SenseVoice支持模型微调和onnx服务部署。
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## <strong>[SenseVoice开源项目介绍]()</strong>
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<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。
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[**github仓库**]()
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| [**最新动态**]()
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| [**环境安装**]()
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## <strong>[FunASR开源项目介绍](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！
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[**github仓库**](https://github.com/alibaba-damo-academy/FunASR)
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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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| [**服务部署**](https://www.funasr.com)
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| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)
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| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
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# 模型结构图
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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。
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<p align="center">
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<img src="fig/sensevoice.png" alt="SenseVoice模型结构" width="500" />
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</p>
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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：
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- <LID>：用于预测音频语种标签。
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- <SER>：用于预测音频情感标签。
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- <AED>：用于预测音频包含的事件标签。
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- <ITN>：用于指定识别输出文本是否进行逆文本正则化。
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# 用法
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## 推理
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### 直接推理
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```python
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from model import SenseVoiceSmall
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model_dir = "iic/SenseVoiceSmall"
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m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
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res = m.inference(
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data_in="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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**kwargs,
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)
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print(res)
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```
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### 使用funasr推理
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```python
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from funasr import AutoModel
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model_dir = "iic/SenseVoiceSmall"
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input_file = (
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
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)
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model = AutoModel(model=model_dir,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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trust_remote_code=True, device="cuda:0")
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res = model.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size_s=0,
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)
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print(res)
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```
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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。
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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`
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```python
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model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
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res = model.generate(
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input=input_file,
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size=64,
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)
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```
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更多详细用法，请参考 [文档](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
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## 模型下载
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('iic/SenseVoiceSmall')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/iic/SenseVoiceSmall.git
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```
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## 服务部署
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Undo
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<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>
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