Breeze-TTS-2-int8

Breeze-TTS-2 int8

Int8 weight-only quantisation of BreezeBlue/Breeze-TTS-2, a 3.5B voice-cloning TTS model. Official code: https://github.com/breezeblue-ai/breeze-tts

What is quantised

• Quantised to int8 (torchao Int8WeightOnlyConfig(version=2), per-row scales): all nn.Linear layers in the speech backbone (backbone_model.layers, Qwen3 architecture, 1.4B params) and the depth decoder (depth_decoder.model, 0.4B params) - 281 weights total
• Kept bf16: T5Gemma2 text encoder, Mimi codec (codec_model), text/audio embeddings, lm_head, depth_decoder.codebooks_head, all norms
• audio_tokenizer/ is the unmodified bf16 copy from the base checkpoint

Weights: 5.2 GB vs 7.0 GB bf16. Per-layer mean relative error vs bf16 is ~1%.

Format

Weights are pickle shards (pytorch_model-*.bin), not safetensors. transformers 4.57.3 only writes torchao safetensors for Float8 configs, and its reader expects the :-flattened key format of torchao <= 0.14 while torchao 0.15+ writes ._weight_qdata keys - an int8 safetensors checkpoint written with this pairing cannot be read back. The .bin route keeps stock from_pretrained working. Loading a pickle checkpoint requires torchao to be installed (its tensor classes are registered as safe globals for torch.load).

Requirements

Built and verified with torch 2.9.1, transformers 4.57.3, torchao 0.17.0, qwen-tts 0.1.1 (torchao 0.18+ needs torch >= 2.11). The inference code from the official repo is bundled (models/, breeze_infer/, infer.py, Apache-2.0 - see LICENSE); the checkpoint has no trust_remote_code wiring, so it must be loaded through that code.

pip install -r requirements.txt torchao==0.17.0

Load

CLI (from the repo root):

python infer.py . \
  --text "The committee has voted: all meetings are now conducted underwater." \
  --ref-audio reference.wav --ref-text "transcript of the reference audio" \
  --device mps --dtype bfloat16 --seed 42 --output out.wav

Python (works on mps, cpu and cuda):

import sys
from pathlib import Path

repo = Path("/path/to/Breeze-TTS-2-int8")
sys.path.insert(0, str(repo))

import torch
from breeze_infer.runtime import load_runtime, update_generation_config_for_breeze

tokenizer, model, audio_tokenizer = load_runtime(
    repo, device="mps", attn_implementation="eager", dtype=torch.bfloat16
)
update_generation_config_for_breeze(model)

from_pretrained picks up the quantization_config in config.json and restores the int8 tensors; no extra arguments needed.

Verification (M5 Max, macOS, MPS)

scripts/verify.py renders a seed-42 voice clone twice (cold, warm) and transcribes the result:

• Transcription word match vs target text: 16/16
• 6.24 s of audio, warm wall time 28.3 s, RTF 4.5
• Repo on disk 5.5 GB (bf16 checkpoint: 7.0 GB weights + 0.7 GB audio tokenizer)

MPS caveats

• torchao has no fused int8 matmul kernel on MPS; each linear dequantises to bf16 before the matmul. Generation is ~2.5x slower than the bf16 checkpoint (warm RTF ~4.5 vs ~1.8). The quantisation buys disk and memory, not speed, on Apple silicon.
• MPS is not an officially validated torchao platform; validated here empirically (see verification above).
• Outputs differ sample-for-sample from bf16 at the same seed (quantisation shifts sampled logits) but speaker cloning and text fidelity hold.

Reproduce

python scripts/convert.py <bf16_snapshot_dir> <out_dir> --code-dir <breeze-tts checkout>
python scripts/verify.py <out_dir> --ref-audio ref.wav --ref-text "..." --text "..." --output out.wav

Licence

Weights inherit the BreezeBlue Research and Non-Commercial License (MODEL_LICENSE). Bundled inference code is Apache-2.0 (LICENSE).