---
license: mit
tags:
- text-to-speech
- tts
- litert
- tflite
- on-device
- matcha-tts
- hifigan
language:
- en
library_name: litert
pipeline_tag: text-to-speech
---

# Matcha-TTS — LiteRT (on-device, FFT-free, GPU)

On-device English text-to-speech for Android via LiteRT `CompiledModel`. This is the
**FFT-free** TTS lane: [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS) pairs a
conditional flow-matching (CFM) acoustic model with a **HiFi-GAN time-domain vocoder**, so
there is **no FFT/iSTFT anywhere** in the synthesis path. 22.05 kHz, LJSpeech voice.

Converted from the official `matcha_ljspeech` + `hifigan_T2_v1` checkpoints with
[litert-torch](https://github.com/google-ai-edge/litert), re-authored to be ML-Drift-GPU-clean
(per-graph tflite-vs-torch corr **1.000000**; end-to-end waveform corr ≥0.99). fp16 weights.

## Files

| File | Size | In → Out | Delegate (Pixel 8a) |
|---|---|---|---|
| `matcha_textenc_fp16.tflite` | 15 MB | emb[1,256,192] + mask[1,1,256] → mu[1,80,256], logw[1,1,256] | GPU |
| `matcha_decoder_fp16.tflite` | 23 MB | x,mu[1,80,512] + t_sin[1,160] + mask[1,1,512] → v[1,80,512] | CPU¹ |
| `matcha_vocoder_fp16.tflite` | 29 MB | mel[1,80,512] → wav[1,1,131072] | GPU |
| `dp_g2p_matcha_fp16.tflite` | 26 MB | text[1,96] (char ids) → logits[1,96,64] (IPA) | CPU |
| `emb.bin` | 0.1 MB | phoneme embedding table (178×192 f32, host lookup) | host |
| `g2p_dict.txt.gz` | 1.8 MB | 275k-entry espeak-IPA dictionary (primary G2P) | host |
| `config.json`, `g2p_meta.json` | — | symbols, shapes, mel stats, G2P tokenizer tables | host |

¹ The CFM decoder runs on the **CompiledModel CPU** delegate. It converts GPU-clean and is
correct on CPU, but the Mali ML Drift GPU delegate **mis-fuses the decoder's transformer blocks
at large activation magnitude** (the same block is correct as a standalone GPU graph, corr 0.984,
but collapses to corr 0.006 fused — a graph-fusion bug, not a bad op). text encoder + vocoder run
on the GPU; the GPU vocoder dominates wall time so the pipeline stays **realtime (RTF ~0.8)**.

## Pipeline (host orchestration)

```
text --G2P(CPU dict+neural)--> phoneme ids
     --host: embed + intersperse + pad-->     text_encoder(GPU) -> mu, logw
     --host: durations + length-regulator-->  mu_y[1,80,T]
     --host: Euler ODE loop (N steps)-->        decoder(CPU) x N -> v
     --host: denormalize-->                     vocoder(GPU)     -> waveform
```

Fixed shapes (256 phonemes, 512 mel frames ≈ 5.9 s); a runtime float mask makes padded positions
a no-op so one compiled graph handles any length.

## G2P (espeak-free)

Matcha-LJSpeech is trained on espeak en-us IPA, but espeak is GPL. The clean replacement is a
275k-entry espeak-IPA dictionary (from [OpenPhonemizer](https://github.com/NeuralVox/OpenPhonemizer),
Clear BSD) as primary + [DeepPhonemizer](https://github.com/as-ideas/DeepPhonemizer) (MIT) on
LiteRT CPU for out-of-dictionary words. Output IPA maps 1:1 onto the keithito 178-symbol set.

## Sample

See the LiteRT `compiled_model_api/text_to_speech` sample (Matcha-TTS) in
[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples) for the full
Android app and the conversion scripts.

## License

Model: MIT (Matcha-TTS / HiFi-GAN). G2P dict: Clear BSD (OpenPhonemizer) + MIT (DeepPhonemizer).