Configuration Parsing Warning:In tokenizer_config.json: "tokenizer_config.chat_template" must be one of [string, array]

FireRedAudio-MLX-8bit

Apple Silicon MLX 8-bit quantized checkpoint for FireRedAudio (9B multimodal audio language model).

  • 🐙 GitHub Repository & WebUI Studio: vanch007/mlx-FireRedAudio
  • Hardware Acceleration: Metal GPU native unified memory, fused SDPA attention, and Gated Delta Metal kernel.
  • 🚀 Real-Time Factor (RTF):
    • ASR (Speech Recognition): RTF 0.1396 (~7.2x real-time speedup)
    • TTS (Voice Cloning): RTF 0.7529 (Real-time speech generation on Apple M3 Max)
  • 💾 Memory Footprint: Only 13.54 GB active Metal memory (runs smoothly on 16GB / 24GB+ Mac devices).

Model Architecture & Quantization Details

  • Backbone LLM: 9B Qwen3.5 language model with selective 8-bit affine quantization (group_size=64).
  • Continuous Audio Autoencoder / Vocoder: RedAE VAE and Native ISTFT vocoder preserved in full FP32/BF16 precision for lossless audio phase fidelity.
  • Audio Understanding Encoder: Whisper / Conv1D acoustic encoder preserved in high precision.
  • Audio Generation Head: 11-layer Flow Matching DiT.

Quick Start

1. Installation

git clone https://github.com/vanch007/mlx-FireRedAudio.git
cd mlx-FireRedAudio
uv venv .venv
source .venv/bin/activate
uv pip install -e .

2. Download Model

hf download vanch007/FireRedAudio-MLX-8bit --local-dir models/FireRedAudio-8bit

3. Run Inference (CLI & Python)

from fireredaudio_mlx import FireRedAudioInference

engine = FireRedAudioInference(model_path="models/FireRedAudio-8bit")

# ASR Speech Transcription (RTF ~ 0.14)
res = engine.understand("assets/examples/asr_zh_fleurs.wav", task="asr")
print("Transcript:", res.answer)

# Zero-shot TTS Voice Cloning (RTF ~ 0.75)
audio_res = engine.tts(
    prompt_text="收到你的来信,我很高兴。",
    prompt_audio="assets/examples/tts_zh_prompt.wav",
    target_text="你好,欢迎体验 FireRedAudio 8-bit MLX 实时语音生成!",
)

4. Launch WebUI Studio

python run_webui.py --model models/FireRedAudio-8bit

Open your browser at http://127.0.0.1:7860.


Citation & References

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