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@ -38,7 +38,7 @@ Model Zoo:
- **Service Deployment:** Offer service deployment pipeline, supporting multi-concurrent requests, with client-side languages including Python, C++, HTML, Java, and C#, among others.
<a name="What's News"></a>
# What's News 🔥
# What's New 🔥
- 2024/7: The [SenseVoice-Small](https://www.modelscope.cn/models/iic/SenseVoiceSmall) voice understanding model is open-sourced, which offers high-precision multilingual speech recognition, emotion recognition, and audio event detection capabilities for Mandarin, Cantonese, English, Japanese, and Korean and leads to exceptionally low inference latency.
- 2024/7: The CosyVoice for natural speech generation with multi-language, timbre, and emotion control. CosyVoice excels in multi-lingual voice generation, zero-shot voice generation, cross-lingual voice cloning, and instruction-following capabilities. [CosyVoice repo](https://https://github.com/FunAudioLLM/CosyVoice) and [CosyVoice space](https://www.modelscope.cn/studios/iic/CosyVoice-300M).
- 2024/7: [FunASR](https://github.com/modelscope/FunASR) is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR.
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# Benchmarks 📝
## Multilingual Speech Recognition
We compared the performance of multilingual speech recognition between SenseVoice and Whisper on open-source benchmark datasets, including AISHELL-1, AISHELL-2, Wenetspeech, LibriSpeech, and Common Voice. n terms of Chinese and Cantonese recognition, the SenseVoice-Small model has advantages.
We compared the performance of multilingual speech recognition between SenseVoice and Whisper on open-source benchmark datasets, including AISHELL-1, AISHELL-2, Wenetspeech, LibriSpeech, and Common Voice. In terms of Chinese and Cantonese recognition, the SenseVoice-Small model has advantages.
<div align="center">
<img src="image/asr_results1.png" width="400" /><img src="image/asr_results2.png" width="400" />
@ -77,7 +77,7 @@ Although trained exclusively on speech data, SenseVoice can still function as a
## Computational Efficiency
The SenseVoice-Small model non-autoregressive end-to-end architecture, resulting in extremely low inference latency. With a similar number of parameters to the Whisper-Small model, it infers more than 5 times faster than Whisper-Small and 15 times faster than Whisper-Large.
The SenseVoice-Small model deploys a non-autoregressive end-to-end architecture, resulting in extremely low inference latency. With a similar number of parameters to the Whisper-Small model, it infers more than 5 times faster than Whisper-Small and 15 times faster than Whisper-Large.
<div align="center">
<img src="image/inference.png" width="1000" />
@ -154,7 +154,7 @@ res = model.generate(
)
```
For more usage, please ref to [docs](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
For more usage, please refer to [docs](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)