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modelscope/funasr

★ 16,051PythonAudience · researcherComplexity · 4/5Setup · hard

TLDR

A Python speech recognition toolkit that transcribes audio to text and adds punctuation, speaker labels, and emotion detection, with pretrained models available from ModelScope and Hugging Face.

Mindmap

mindmap
  root((FunASR))
    Core tasks
      Speech transcription
      Punctuation restoration
      Speaker diarization
    Features
      Voice activity detection
      Keyword spotting
      Emotion recognition
    Models
      Paraformer-large
      Whisper-v3
      Qwen-Audio
    Deploy
      Offline batch
      Real-time runtime
      Windows SDK
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Things people build with this

USE CASE 1

Transcribe audio or video files to text with automatic punctuation using a pretrained Paraformer model.

USE CASE 2

Run speaker diarization on a meeting recording to separate and label who said what and when.

USE CASE 3

Fine-tune a speech recognition model on your own audio dataset using the PyTorch training pipeline.

Tech stack

PythonPyTorchCUDA

Getting it running

Difficulty · hard Time to first run · 1h+

GPU with CUDA recommended for production workloads, CPU variants exist but are significantly slower for large models.

License terms were not mentioned in the explanation.

In plain English

FunASR is a toolkit for turning recorded or live audio into text, and for the surrounding jobs that make that text useful. The README describes it as a bridge between academic research and industrial applications in speech recognition, aiming to make it easier for researchers and developers to train, fine-tune, and deploy speech models. The name is a play on ASR, the short name for automatic speech recognition. Around the core transcription feature, the toolkit bundles related tasks. Voice Activity Detection finds where speech actually occurs in an audio file so silent stretches are skipped. Punctuation Restoration adds commas and full stops to raw transcripts. Speaker Verification and Speaker Diarization figure out who is talking and when speakers change. Multi-talker ASR handles overlapping voices, and there is also keyword spotting and emotion recognition. The project ships pretrained models that can be pulled from ModelScope and Hugging Face. One headline model is Paraformer-large, a non-autoregressive end-to-end model tuned for accuracy and efficient deployment. The toolkit also wires in third-party models such as Whisper-large-v3 and the audio-text Qwen-Audio family. FunASR provides runtime packages for offline file transcription and real-time transcription, including CPU and GPU variants and a Windows SDK. Someone would reach for FunASR when they need to add transcription or voice analytics to a product, or when they want a starting point for research that involves fine-tuning a strong baseline. The project is written in Python and the topics list and changelog show it builds on the PyTorch ecosystem. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Using FunASR and the Paraformer-large model, write me a Python script that takes an audio file path, transcribes it, and prints the transcript with punctuation.
Prompt 2
I want to run speaker diarization with FunASR on a podcast episode. Show me the Python code to load the diarization model and output timestamped speaker segments.
Prompt 3
Set up a real-time transcription server using FunASR runtime and show me how to stream microphone audio to it and receive text back.
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