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## Model Introduction **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: * **Exceptional Dialect Support:** Beyond standard Mandarin and English, the model is highly optimized for **Cantonese (粤è¯)** and other dialects, effectively bridging the gap in dialectal speech recognition. * **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. * **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..). ## Benchmark 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. ## Inference `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`. you can check more code in [Github](https://github.com/zai-org/GLM-ASR).