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GLM-ASR-Nano-2512 is a robust, open-source speech recognition model with 1.5B parameters. Designed for real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size.
Key capabilities include:
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Exceptional Dialect Support Beyond standard Mandarin and English, the model is highly optimized for Cantonese (粤语) and other dialects, effectively bridging the gap in dialectal speech recognition.
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Low-Volume Speech Robustness Specifically trained for "Whisper/Quiet Speech" scenarios. It captures and accurately transcribes extremely low-volume audio that traditional models often miss.
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SOTA Performance Achieves the lowest average error rate (4.10) among comparable open-source models, showing significant advantages in Chinese benchmarks (Wenet Meeting, Aishell-1, etc..).
We evaluated GLM-ASR-Nano against leading open-source and closed-source models. The results demonstrate that GLM-ASR-Nano (1.5B) achieves superior performance, particularly in challenging acoustic environments.
Notes:
- Wenet Meeting reflects real-world meeting scenarios with noise and overlapping speech.
- Aishell-1 is a standard Mandarin benchmark.
| Model | Download Links |
|---|---|
| GLM-ASR-Nano-2512 | 🤗 Hugging Face 🤖 ModelScope |
GLM-ASR-Nano-2512 can be easily integrated using the transformers library.
We will support transformers 5.x as well as inference frameworks such as vLLM and SGLang.
pip install -r requirements.txt
sudo apt install ffmpegpython inference.py --checkpoint_dir zai-org/GLM-ASR-Nano-2512 --audio examples/example_en.wav # English
python inference.py --checkpoint_dir zai-org/GLM-ASR-Nano-2512 --audio examples/example_zh.wav # 中文For the two example audio clips above, the model is able to produce accurate transcription results. They are:
be careful not to allow fabric to become too hot which can cause shrinkage or in extreme cases scorch
我还能再搞一个，就算是非常小的声音也能识别准确
