M5-TTS-Audio-Tokenizer

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This repository provides the Qwen 12Hz audio codec used by M5-TTS, encoding reference recordings into discrete audio tokens and decoding generated tokens into speech. It is not a standalone text-to-speech model and is distinct from the text tokenizer bundled with M5 checkpoints.

Property	Value
Audio sample rate	24 kHz
Codec frame rate	12.5 frames/s
Codebooks used by M5	16
Codes layout through the M5 adapter	(batch, frames, 16) for a padded batch

The VoiceClone and CustomVoice inference scripts download this codec automatically. You normally do not need to load it separately.

Quickstart

For standalone audio encoding and reconstruction, use Python 3.10 and the M5 repository:

git clone https://github.com/ASLP-lab/M5-TTS.git
cd M5-TTS
conda create -n m5tts python=3.10 -y
conda activate m5tts
pip install -r requirements.txt

Run from the repository root; this CPU example does not require FlashAttention:

import soundfile as sf
from m5.codec_qwen12hz import Qwen3TTSTokenizer

codec = Qwen3TTSTokenizer.from_pretrained(
    "ASLP-lab/M5-TTS-Audio-Tokenizer",
    device_map="cpu",
)

encoded = codec.encode("assets/aslp_default_voice.wav")
waveforms, sample_rate = codec.decode(encoded)
sf.write("reconstructed.wav", waveforms[0], sample_rate)

encode() also accepts NumPy waveforms with an explicit source sample rate, for example codec.encode(audio, sr=24000). The adapter resamples input audio as needed; reconstruction is lossy. The TTS inference scripts apply their reference-audio preprocessing separately.

License and Attribution

Apache-2.0. This repository uses the Qwen3-TTS 12Hz tokenizer. The M5 repository includes its minimal codec implementation with the upstream license and modification notice. No separate qwen-tts package installation is required.