Practical overview
When it fits
Use it when building a local audio-processing pipeline that benefits from transcription alongside language identification, activity segmentation or punctuation, especially for Chinese dialects and singing.
Who it is for
- Developers assembling speech or singing transcription workflows.
- Teams processing multilingual recordings that need voice-activity or spoken-language labels.
- Users who want to run documented model inference from a Python environment, command line or Python code.
Documented uses
- Transcribe speech and singing in supported Chinese varieties, English and code-switched audio.
- Locate speech, singing or music activity in audio.
- Identify the spoken language, including among more than 100 supported languages.
- Add Chinese or English punctuation to transcription text.
Requirements & limits
The project provides model weights and inference code through its repository documentation, with model download instructions for Hugging Face and ModelScope.
- The documented local setup uses Python 3.10, Conda, repository dependencies and downloaded model files.
- Input preparation instructions specify 16 kHz, 16-bit mono PCM WAV audio when conversion is required.
- ASR coverage and module coverage differ: the recognizer lists Chinese varieties, English and code-switching, while VAD and language identification list more than 100 languages.
- The README demonstrates command-line and Python inference, but does not document an API service.
- Published benchmark figures are project-reported test results and should not be treated as a guarantee for other datasets or environments.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up FireRedASR2S for local audio inference
Prepare the documented Python environment, dependencies and model files for the repository’s inference workflow.
Before you start
- Python 3.10 and Conda
- A local clone of the FireRedASR2S repository
- Access to model downloads through Hugging Face or ModelScope
Steps
Create and activate a Conda environment using Python 3.10, then clone the repository and enter its directory.
Install dependencies with the command below and set the PATH and PYTHONPATH entries shown in the README.
pip install -r requirements.txtDownload the required model files using the documented Hugging Face or ModelScope commands.
If needed, convert audio to 16 kHz, 16-bit mono PCM WAV using the documented ffmpeg command.
Expected outcome
A local environment with model files and audio in the format specified for the documented inference examples.
Guide limitations
- This guide covers setup and input preparation; it does not establish that a particular machine or audio file will run successfully.
Process audio using the documented CLI example
Use the repository’s command-line example to request transcription for WAV files and write results to an output directory.
Before you start
- A prepared FireRedASR2S environment with dependencies and model files
- WAV audio files; the setup instructions specify 16 kHz, 16-bit mono PCM when conversion is needed
Steps
Run
fireredasr2s-cli --helpto display the CLI help.Pass audio paths to
fireredasr2s-cli --wav_pathsand specify an output directory with--outdir.Read the documented
output/result.jsonlfile to inspect example transcript and audio metadata.
Expected outcome
The documented CLI workflow writes JSON Lines results that can include recognized text, language, confidence, voice-activity segments, duration and word timings.
Guide limitations
- The output fields shown are examples and may differ by processing mode; the Python example, for instance, shows an empty words list.
- The guide reflects repository instructions and is not a human-run execution test.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- FireRedTeam/FireRedASR2S · Official homepage title · retained in its source language