acul3 / chatterbox-executorch

Chatterbox Multilingual TTS — ExecuTorch Models

Pre-exported .pte model files for running Resemble AI's Chatterbox Multilingual TTS fully on-device using ExecuTorch .

📦 Code & export scripts: acul3/chatterbox-executorch on GitHub

What's Here

9 ExecuTorch .pte files covering the complete TTS pipeline — from text input to 24kHz waveform — with zero PyTorch runtime required:

Quick Download

from huggingface_hub import snapshot_download

snapshot_download(
    "acul3/chatterbox-executorch",
    local_dir="et_models",
    repo_type="model"
)

Pipeline Overview

Text → MTLTokenizer → text tokens
Reference Audio → VoiceEncoder + CAMPPlus → speaker conditioning
                          ↓
              T3 Prefill (LlamaModel, conditioned)
                          ↓
              T3 Decode (autoregressive, ~100 tokens)
                          ↓
              S3Gen Encoder (Conformer)
                          ↓
              CFM Step × 2 (flow matching)
                          ↓
              HiFiGAN (vocoder, chunked)
                          ↓
              24kHz PCM waveform 🎵

Key Technical Notes

• T3 Decode uses a manually unrolled 30-layer Llama forward pass with static KV cache ( torch.where writes) — bypasses HF DynamicCache for torch.export compatibility
• HiFiGAN uses a manual real-valued DFT (cosine/sine matrix multiply) — replaces torch.stft / torch.istft which XNNPACK doesn't support
• T3 models are FP16 (XNNPACK half-precision kernels) — ~half the size of FP32 with near-identical quality
• Fixed shapes: CFM expects T_MEL=2200 , HiFiGAN expects T_MEL=300 (use chunked processing for longer audio)

Usage

See the GitHub repo for full inference code: acul3/chatterbox-executorch

# Clone code
git clone https://github.com/acul3/chatterbox-executorch.git
cd chatterbox-executorch

# Download models (this repo)
python -c "
from huggingface_hub import snapshot_download
snapshot_download('acul3/chatterbox-executorch', local_dir='et_models', repo_type='model')
"

# Run full PTE inference
python test_true_full_pte.py

Android Integration

These models are designed for Android deployment via the ExecuTorch Android SDK . Load with:

val module = Module.load(context.filesDir.path + "/t3_prefill.pte")

With QNN/NPU delegation on a Snapdragon device, expect 10–50× speedup over the CPU timings below.

Performance (Jetson AGX Orin, CPU only)

License

Model weights are derived from Resemble AI's Chatterbox . The export pipeline code is MIT licensed. Please refer to the original Chatterbox license for model weights usage terms.