Matcha-TTS

Measured on device (edge-compat, matcha_textenc_fp16): Raspberry Pi 5 · LiteRT 2.2.0.dev20260804 · CPU/XNNPACK, 4 threads · 38.7 ms p50 (2026-08-31); browser · Chromium 151 on M4 Max · LiteRT.js 2.5.3 · WebGPU · 5.99 ms p50 · output matches CPU (2026-08-11). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/matcha-tts__matcha_textenc_fp16/CARD.md

Measured on device (edge-compat, matcha_vocoder_fp16): Raspberry Pi 5 · LiteRT 2.2.0.dev20260804 · CPU/XNNPACK, 4 threads · 5825 ms p50 (2026-08-31); browser · Chromium 151 on M4 Max · LiteRT.js 2.5.3 · WebGPU · 104 ms p50 · output differs from CPU (max rel diff 0.22) (2026-08-11). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/matcha-tts__matcha_vocoder_fp16/CARD.md

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 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.

Try it in your browser: john-rocky.github.io/page-demos/matcha-tts — the same four .tflite files below running on LiteRT.js (text encoder + vocoder on WebGPU, decoder on WASM). Nothing to install; inference runs on your machine.

Converted from the official matcha_ljspeech + hifigan_T2_v1 checkpoints with litert-torch, 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

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.

How to use

Android (Kotlin, LiteRT CompiledModel)

fun load(name: String, acc: Accelerator) =                    // models staged in filesDir
    CompiledModel.create(File(filesDir, name).absolutePath, CompiledModel.Options(acc), null)

val textenc = load("matcha_textenc_fp16.tflite", Accelerator.GPU)
val decoder = load("matcha_decoder_fp16.tflite", Accelerator.CPU)  // Mali mis-fuses this graph on GPU
val vocoder = load("matcha_vocoder_fp16.tflite", Accelerator.GPU)

val teIn = textenc.createInputBuffers(); val teOut = textenc.createOutputBuffers()
teIn[0].writeFloat(emb)     // [1,256,192] host phoneme-embedding lookup (emb.bin), blanks interspersed
teIn[1].writeFloat(tmask)   // [1,1,256]   1 = real phoneme position
textenc.run(teIn, teOut)    // -> mu[1,80,256], logw[1,1,256]
// host: durations ceil(exp(logw))·0.95 -> length-regulate mu -> mu_y[1,80,512]; 10 Euler steps of
// decoder(x, mu_y, t_sin[1,160], ymask[1,1,512]); mel = x·2.116101 − 5.536622 -> vocoder -> wav.
// Full pipeline: the text_to_speech (Matcha-TTS) sample in google-ai-edge/litert-samples.

Python (desktop verification)

import gzip, json, math, numpy as np, soundfile as sf
from ai_edge_litert.interpreter import Interpreter

MAXT, MAXM, LS = 256, 512, 0.95
cfg = json.