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readme: merge GPU/CPU benchmark into one compact table

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游雁
2026-05-24 17:23:31 +08:00
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@@ -69,28 +69,17 @@ That's it. **One model, one call** — VAD segmentation, speech recognition, pun
## Benchmark
> 7 models on GPU, 3 on CPU. 184 long-form Chinese audio files (192 min total). [Full report →](https://modelscope.github.io/FunASR/benchmark.html)
> 184 long-form audio files (192 min). [Full report →](https://modelscope.github.io/FunASR/benchmark.html)
### GPU Results (PyTorch)
| Model | GPU Speed | CPU Speed | vs Whisper-large-v3 |
|-------|-----------|-----------|-------------------|
| **SenseVoice-Small** | **170x** realtime | **17x** realtime | 🚀 **13x faster** |
| **Paraformer-Large** | **120x** realtime | **15x** realtime | 🚀 **9x faster** |
| Whisper-large-v3-turbo | 46x realtime | ❌ | 3.4x faster |
| **Fun-ASR-Nano** | 17x realtime | 3.6x realtime | 1.3x faster |
| Whisper-large-v3 | 13x realtime | ❌ | baseline |
| Model | Type | Speed | Highlights |
|-------|------|-------|-----------|
| **SenseVoice-Small** | NAR | **170x** realtime | Fastest + most accurate |
| **Paraformer-Large** | NAR | **120x** realtime | Best speed/accuracy tradeoff |
| Whisper-large-v3-turbo (OpenAI) | AR | 46x realtime | Fastest Whisper variant |
| Whisper-large-v3-turbo (FunASR) | AR | 26x realtime | With VAD pipeline |
| faster-whisper-large-v3 | AR | 21.5x realtime | CTranslate2 optimized |
| **Fun-ASR-Nano** | LLM | 17x realtime | Best LLM-based accuracy |
| Whisper-large-v3 (OpenAI) | AR | 13.4x realtime | Baseline |
### CPU Results
| Model | Speed | Note |
|-------|-------|------|
| **SenseVoice-Small** | **17.2x** realtime | Production-ready on CPU |
| **Paraformer-Large** | **15.6x** realtime | Production-ready on CPU |
| **Fun-ASR-Nano** | 3.6x realtime | Still faster than realtime |
| Whisper models | - | Impractical on CPU (>2h for 192min audio) |
> **Key takeaway:** FunASR models run on CPU faster than Whisper runs on GPU.
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