Practical overview
When it fits
Use FireRedASR when you need open-source ASR models and can prepare a Linux/Python environment, download model weights and format audio for inference.
Who it is for
- Developers building transcription features for Mandarin, Chinese dialect or English audio.
- Teams exploring lyrics transcription for sung audio.
- Python practitioners who want model files and local inference examples to adapt into a speech application.
Documented uses
- Convert supported speech recordings into text.
- Recognize singing lyrics from audio.
- Integrate speech recognition into a Python application using the provided transcription interface.
Requirements & limits
The public repository identifies the project as open source under the Apache-2.0 license and provides model-download links.
- The repository documents Linux setup with Python 3.10 and dependency installation through pip.
- Input audio should be converted to mono 16 kHz, 16-bit PCM WAV for the documented workflow.
- FireRedASR-AED and FireRedASR-LLM have different model architectures and audio-length guidance.
- FireRedASR-LLM-L requires Qwen2-7B-Instruct files in addition to the FireRedASR model files.
- The repository warns of possible repetition with widely different utterance lengths in LLM batch beam search.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up FireRedASR for local inference
Prepare the documented Python environment, model files and audio format for using the repository's inference examples.
Before you start
- Linux environment
- Conda and Python 3.10
- Git and pip
- FFmpeg
- FireRedASR model files from the linked model collection
Steps
Clone the FireRedASR repository, create and activate a Conda environment with Python 3.10, then install dependencies from requirements.txt.
Set PATH and PYTHONPATH as described in the repository README.
Download model files into the pretrained_models directory; for FireRedASR-LLM-L, also download Qwen2-7B-Instruct and create the documented symbolic link from the model folder.
Convert the input recording to mono 16 kHz, 16-bit PCM WAV using the documented FFmpeg command.
Expected outcome
A configured local environment with model files and audio prepared for the repository's inference examples or command-line usage.
Guide limitations
- This setup guide does not claim that inference has been run or verified.
- The README gives variant-specific input limits: AED supports audio up to 60 seconds, while LLM supports up to 30 seconds; longer-input behavior for LLM is unknown.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Fireredasr | Convert supported speech recordings into text. | Linux | The public repository identifies the project as open source under the Apache-2.0 license and provides model-download links. |
| Edgedict | Decode speech from a YouTube Live video into text. | Cloud |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 11 October 2026
- Original website title
- FireRedTeam/FireRedASR · Official homepage title · retained in its source language