AB
AB
AiBoss

All Categories

project

GLM-ASR - Zhipu Open Source Speech Recognition Model Series

GLM-ASR is a series of speech recognition models launched by Zhipu AI, including the cloud-based GLM-ASR-2512 and the open-source GLM-ASR-Nano-2512. GLM-ASR-2512 is a world-leading cloud-based speech recognition model, supporting multiple scenarios and multiple languages...

What is GLM-ASR?

GLM-ASR is a series of speech recognition models launched by Zhipu, 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, languages, and accents, with a character error rate of only 0.0717. GLM-ASR-Nano-2512 is a 1.5B parameter edge model, achieving state-of-the-art 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 realize functions such as speech-to-text, translation, and rewriting, promoting the development of voice interaction towards efficiency and intelligence.

Main functions of GLM-ASR

  • Accurate speech-to-textThe model can convert speech into text in real time, supports multiple scenarios, languages and accents, and has a low character error rate, ensuring high-precision recognition.
  • Dialect and low volume recognitionThe model has been optimized to support dialects such as Cantonese, and can accurately capture and transcribe speech in low-volume scenarios (such as whispers).
  • Device-side privacy protectionThe GLM-ASR-Nano-2512 can run locally without uploading voice data to the cloud, protecting user privacy and reducing interaction latency.
  • Intelligent Interaction and Function ExpansionThe Zhipu AI Input Method, based on GLM-ASR, supports operations such as translation, rewriting, and emotion transformation, and provides a "persona" switching function to adapt to the expression needs of different scenarios.
  • Developer SupportIt provides developers with a "voice-based programming" function, which supports inputting code logic and comments by voice, searching for instructions, and completing complex mathematical calculations or script writing.
  • Customized vocabularyUsers can import exclusive vocabulary, project codes, uncommon names and place names to improve the recognition accuracy in specific fields.

GLM-ASR performance

  • GLM-ASR-2512In complex environments with multiple scenarios, languages, and accents, the character error rate (CER) is only 0.0717, which is at the leading level in the industry.
  • GLM-ASR-Nano-2512It performs exceptionally well across multiple benchmark tests, with an average error rate of only 4.10%, achieving a state-of-the-art (SOTA) level among open-source models.

How to use GLM-ASR

  • Cloud callVisit the Zhipu Open Platform and register an account to access the latest GLM-ASR-2512 model.
  • Local deployment (open source model)Zhipu provides the GLM-ASR-Nano-2512 model (1.5B parameters) for the open-source community, suitable for local operation. The model's weights and inference code have been released, and developers can download and integrate them into their projects, making it suitable for scenarios requiring privacy protection or offline use.

GLM-ASR project address

  • GitHub repositoryhttps://github.com/zai-org/GLM-ASR
  • HuggingFace model libraryhttps://huggingface.co/zai-org/GLM-ASR-Nano-2512

Application scenarios of GLM-ASR

  • Office meeting minutesThe model can accurately transcribe meeting audio into text in real time, automatically generate meeting minutes, and improve office efficiency.
  • Educational Language LearningGLM-ASR assists students in oral practice, supports multilingual translation and pronunciation correction, and helps with language learning.
  • Developer Programming AssistanceDevelopers can input code logic and comments via voice, and GLM-ASR helps generate code quickly, improving development efficiency.
  • Video content creationThe model can automatically generate multilingual subtitles for videos, facilitating content creation and dissemination and improving production efficiency.
  • Low volume input in public placesGLM-ASR optimizes weak sound recognition, making it suitable for use in quiet places such as libraries and offices, and protecting privacy.
Back