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license: cc-by-4.0
language:
- en
library_name: mnn
base_model:
- kyutai/pocket-tts
- IgnitiveLabs/PocketTTS-ONNX
pipeline_tag: text-to-speech
tags:
- tts
- voice-cloning
- mnn
- onnx
- int8
- pocket-tts
---
# Pocket TTS MNN (English)
MNN conversion of [IgnitiveLabs/PocketTTS-ONNX](https://huggingface.co/IgnitiveLabs/PocketTTS-ONNX), itself an ONNX export of Kyutai's [Pocket TTS](https://huggingface.co/kyutai/pocket-tts) (`english_2026-04`).
Weights use **INT8 weight quantization** via `MNNConvert --weightQuantBits 8` (text conditioner kept FP32). Unsupported `ONNX::IsNaN` ops were rewritten to `Not(Equal(x, x))` before conversion.
## Files
```
mnn/
flow_lm_main_w8.mnn # ~73 MB
flow_lm_flow_w8.mnn # ~9.5 MB
mimi_decoder_w8.mnn # ~10 MB
mimi_encoder_w8.mnn # ~9.5 MB (converted; runtime uses ONNX fallback)
text_conditioner_fp32.mnn # ~16 MB (preferred)
text_conditioner_w8.mnn # ~16 MB
flow_lm_main_int8.onnx # empty-seq priming fallback
mimi_encoder.onnx # voice encode fallback
bundle.json
tokenizer.model
bos_before_voice.npy
pocket_tts_mnn.py
pocket_tts_onnx.py
reference_sample.wav
```
## Hybrid runtime
MNN runs the generation hot path (frame loop, flow net, decoder, text conditioner).
ONNX is still required for:
1. **Empty-sequence priming** (`seq_len=0`) — not supported by MNN on `flow_lm_main`
2. **`mimi_encoder`** — MNN fails streaming Concat shape inference
## Quick start
```bash
pip install MNN onnxruntime numpy soundfile sentencepiece scipy huggingface_hub safetensors
huggingface-cli download developerabu/pocket-tts-mnn --local-dir pocket-tts-mnn
cd pocket-tts-mnn
python generate_mnn.py "Hello, this is a test." reference_sample.wav out.wav
```
Or in Python:
```python
from pocket_tts_mnn import PocketTTSMnn
tts = PocketTTSMnn(models_dir=".")
audio = tts.generate("Hello, this is a test.", voice="reference_sample.wav")
tts.save_audio(audio, "out.wav")
```
## Benchmark (Apple M1, CPU, 5 runs)
| Backend | Medium RTFx | Stream TTFB |
|---------|-------------|-------------|
| ONNX INT8 | ~4.1× | ~100 ms |
| MNN INT8 (this repo) | ~2.4× | ~123 ms |
MNN is mainly targeted at mobile; desktop Python numbers above are not the expected mobile ranking.
## Attribution
- Original model: [Kyutai Labs](https://kyutai.org/) — [kyutai/pocket-tts](https://huggingface.co/kyutai/pocket-tts) (CC-BY-4.0)
- ONNX export used as source: [IgnitiveLabs/PocketTTS-ONNX](https://huggingface.co/IgnitiveLabs/PocketTTS-ONNX)
- ONNX inference reference: [KevinAHM/pocket-tts-onnx](https://huggingface.co/KevinAHM/pocket-tts-onnx)
- MNN conversion / packaging: developerabu
## License
Original model weights: **CC-BY-4.0** (provide attribution to Kyutai Labs).
Conversion scripts and packaging in this repo: MIT.
## Acceptable use
Same restrictions as Kyutai / IgnitiveLabs releases: no unauthorized voice cloning, no deceptive synthetic audio presented as real, no harmful/unlawful content, and disclose AI-generated speech to end users.
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