What is GLM-ASR
GLM-ASR is a speech recognition model series launched by Zhipu,Speech Recognitionmodel series, including the cloud-based GLM-ASR-2512 and the open-source GLM-ASR-Nano-2512. GLM-ASR-2512 is a globally leading cloud-based speech recognition model, supporting multiple scenarios, multiple languages, and multiple accents, with a character error rate of only 0.0717. GLM-ASR-Nano-2512 is a 1.5B-parameter on-device model, achieving SOTA performance in the open-source field, supporting dialect recognition and low-volume speech capture, while balancing privacy protection and low latency. Based on this model, Zhipu AI Input Method can enable speech-to-text, translation, rewriting, and other functions, driving voice interaction toward greater efficiency and intelligence.
Main Features of GLM-ASR
- Accurate Speech-to-Text: The model can convert speech into text in real time, supporting multiple scenarios, multiple languages, and multiple accents, with a low character error rate, ensuring high-precision recognition.
- Dialect and Low-Volume Recognition: The model optimizes support for dialects such as Cantonese, and can accurately capture and transcribe speech in low-volume scenarios (such as whispering).
- On-Device Privacy Protection: GLM-ASR-Nano-2512 can run locally without uploading speech data to the cloud, protecting user privacy while reducing interaction latency.
- Intelligent Interaction and Feature Expansion: Zhipu AI Input Method based on GLM-ASR supports translation, rewriting, emotion conversion, and other operations, and provides a "persona" switching function to adapt to expression needs in different scenarios.
- Developer Support: Provides developers with a "language-sense programming" function, supporting voice input of code logic and comments, finding instructions, and completing complex mathematical calculations or script writing.
- Custom Vocabulary: Users can import custom vocabulary, project code names, uncommon personal names and place names, etc., to improve recognition accuracy in specific domains.
Performance of GLM-ASR
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GLM-ASR-2512: In complex environments with multiple scenarios, multiple languages, and multiple accents, the character error rate (CER) is only 0.0717, at an industry-leading level.
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GLM-ASR-Nano-2512: It performs excellently in multiple benchmark tests, with an average error rate of only 4.10%, reaching the SOTA (State-of-the-Art) level among open-source models.
How to Use GLM-ASR
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Cloud API: Visit Zhipu Open Platform to register an account and call the latest GLM-ASR-2512 model.
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Local Deployment (Open-Source Model): Zhipu provides the GLM-ASR-Nano-2512 model (1.5B parameters) to the open-source community, suitable for running locally. The model weights and inference code have been released, and developers can download and integrate them into their own projects, suitable for scenarios requiring privacy protection or offline use.
GLM-ASR Project Address
- GitHub Repository：https://github.com/zai-org/GLM-ASR
- HuggingFace Model Library：https://huggingface.co/zai-org/GLM-ASR-Nano-2512
Application Scenarios of GLM-ASR
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Office Meeting Minutes: The model can accurately transcribe meeting speech into text in real time, automatically generate meeting minutes, and improve office efficiency.
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Education and Language Learning: GLM-ASR assists students with oral practice, supports multilingual translation and pronunciation correction, and helps language learning.
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Developer Programming Assistance: Developers input code logic and comments by voice, and GLM-ASR helps quickly generate code, improving development efficiency.
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Video Content Creation: The model can automatically generate multilingual subtitles for videos, facilitating content creation and dissemination and improving production efficiency.
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Low-volume input in public places: GLM-ASR optimizes the recognition of faint sounds, making it suitable for use in quiet places such as libraries and offices while protecting privacy.