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README.md

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+---
+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.
+
+## Android sample
+
+Full sample app + conversion scripts: [LiteRT-Models / matcha](https://github.com/john-rocky/LiteRT-Models/tree/main/matcha).
+
+## License
+
+Model: MIT (Matcha-TTS / HiFi-GAN). G2P dict: Clear BSD (OpenPhonemizer) + MIT (DeepPhonemizer).