FireRedTeam/FireRedAudio
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README & documentation
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FireRedAudio FireRedAudio is a general-purpose audio language model built on a shared 9B-parameter LLM with decoupled continuous representations: an Audio Encoder handles understanding, while a RedAE pathway handles generation. A single model supports ASR, audio understanding, zero-shot TTS, instruct TTS, semantic/acoustic speech editing, and accurate temporal grounding over recordings up to one hour long. Requires Python 3.10 and uv. System prerequisites: a CUDA toolkit (GPU inference + compiling the causal-conv1d / flash-attn kernels) and ffmpeg (audio decoding via torchaudio/torchcodec). Wheels target CUDA 12.8 (cu128) by default. For a different CUDA version, change the pytorch-cu128 index URL in pyproject.toml (e.g. cu126 / cu129) and re-run uv sync. Download the pretrained model from Hugging Face with the hf CLI: Alternatively, download the pretrained model using modelscope CLI: A single-file Gradio app at app.py wraps all four tasks (zero-shot…
Read the full README ↗ · Preview checked 2026-10-29T11:43:48.747Z
Inside the original README — Document outline
- Overview
- Highlights ✨
- Long-Audio Understanding: Time–Content Alignment Demo
- News
- Contents
- Quick Start 🚀
- Installation
- Model Download
- Python API
- Command Line Inference
- Web Demo (Gradio)
- Performance
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Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
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Repository facts
- Owner
- FireRedTeam
- Primary language
- Python
- Stars
- 2,000
- Forks
- 18
- Open issues + pull requests
- 8
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2026-08-22T11:18:12.000Z
- Last push
- 2026-09-08T03:50:39.000Z
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