speech_recognition/SenseVoice
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游雁 2024-07-17 15:05:52 +08:00
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README.md
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@ -11,8 +11,7 @@ SenseVoice is a speech foundation model with multiple speech understanding capab
<div align="center">
<h4>
<a href="https://www.modelscope.cn/studios/iic/SenseVoice"> Online Demo </a>
｜<a href="https://fun-audio-llm.github.io/"> Homepage </a>
<a href="https://fun-audio-llm.github.io/"> Homepage </a>
｜<a href="#What's News"> What's News </a>
｜<a href="#Benchmarks"> Benchmarks </a>
｜<a href="#Install"> Install </a>
@ -23,6 +22,10 @@ SenseVoice is a speech foundation model with multiple speech understanding capab
Model Zoo:
[modelscope](https://www.modelscope.cn/models/iic/SenseVoiceSmall), [huggingface](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)
Online Demo:
[modelscope demo](https://www.modelscope.cn/studios/iic/SenseVoice), [huggingface space](https://huggingface.co/spaces/FunAudioLLM/SenseVoice)
</div>
@ -162,7 +165,7 @@ from model import SenseVoiceSmall
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
res = m.inference(

8
README_zh.md
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@ -22,7 +22,11 @@ SenseVoice是具有音频理解能力的音频基础模型，包括语音识别
｜<a href="#联系我们"> 联系我们 </a>
</h4>
模型仓库：中国大陆用户推荐 [modelscope](https://www.modelscope.cn/models/iic/SenseVoiceSmall)，海外用户推荐 [huggingface](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)
模型仓库：[modelscope](https://www.modelscope.cn/models/iic/SenseVoiceSmall)，[huggingface](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)
在线体验：
[modelscope demo](https://www.modelscope.cn/studios/iic/SenseVoice), [huggingface space](https://huggingface.co/spaces/FunAudioLLM/SenseVoice)
</div>
<a name="核心功能"></a>
@ -166,7 +170,7 @@ from model import SenseVoiceSmall
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir)
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
res = m.inference(