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Matcha Zh Tw En 8k GGUF

A GGUF conversion of Luigi/matcha-zh-tw-en-8k (a Taiwan-Mandarin + English, 8 kHz Matcha-TTS — CFM acoustic model + Vocos vocoder) for the ggml runtime RapidSpeech.cpp. It lets Matcha-TTS run on ggml-CUDA / ggml-CPU...

KindOtherBase modelLuigi/matcha-zh-tw-en-8kVersionRevision 33f06aa

Model source

Kind
Other
File size
100 MB
Base model
Luigi/matcha-zh-tw-en-8k
Version
vRevision 33f06aa
Updated
Sep 13, 2026
Downloads
25
Source
Hugging Face

Source description

matcha-zh-tw-en-8k-gguf — Matcha-TTS (CFM) zh-TW/en @ 8 kHz, ggml/GGUF for RapidSpeech.cpp

A GGUF conversion of Luigi/matcha-zh-tw-en-8k (a Taiwan-Mandarin + English, 8 kHz Matcha-TTS — CFM acoustic model + Vocos vocoder) for the ggml runtime RapidSpeech.cpp. It lets Matcha-TTS run on ggml-CUDA / ggml-CPU — in particular on the Jetson Nano gen1 (Maxwell sm_53, CUDA 10.2), where the ggml engine fits the 4 GB budget that ONNX Runtime's CUDA EP overflows.

Contents

  • matcha8k.gguf (~105 MB) — 474 learned tensors (acoustic 394 + Vocos 80) + all hyper-parameters (sample_rate=8000, ode_steps=3, ...) packed into GGUF KV. Converted with scripts/convert_matcha_onnx_to_gguf.py from the source bundle's model-steps-3.onnx + vocos-8khz-univ.onnx. Round-trip validated: every GGUF tensor's bytes == the ONNX numpy (PASS).

Validated on real Jetson Nano gen1 (sm_53 Maxwell, CUDA 10.2, 4 GB)

Cross-compiled (x86 → aarch64, gcc-8.3 + CUDA-10.2 / kreier patch set) and run on the device:

Backendwarm RTFaudionotes
RapidSpeech ggml-CPU0.5073.18 s synth, valid (rms 0.118)
RapidSpeech ggml-CUDA0.184identical audioTegra X1 cc 5.3, MATCHA_USE_CUDA=1; ~2.75× over CPU

Real-utterance check (sentence 這個星期的研究進度。, 10 phoneme ids): 2.40 s @ 8 kHz, 151 mel frames — matches the ONNX reference. ggml-CUDA peaks well under the 4 GB budget (the engine's whole reason on this device).

Usage (RapidSpeech.cpp)

The GGUF self-identifies as arch=matcha-tts. The CFM acoustic + Vocos vocoder + iSTFT run end-to-end; the text→phoneme frontend is supplied externally (e.g. sherpa-onnx's Matcha frontend, --debug=1 dumps the int32 phoneme ids). Feed the ids via the MATCHA_IDS env (little-endian int32 file):

# CPU
matcha_e2e_test matcha8k.gguf out.wav            # built-in fixture, or:
MATCHA_IDS=ids.bin matcha_e2e_test matcha8k.gguf out.wav
# ggml-CUDA (Maxwell sm_53+; matcha defaults to CPU because CUDA-graph replay needs cc>=800)
MATCHA_USE_CUDA=1 MATCHA_IDS=ids.bin matcha_e2e_test matcha8k.gguf out.wav

Provenance

Sources

1 source
Hugging FacePrimaryVersion 33f06aa142b003b67a090af975558cb39e181953
huggingface.co/Luigi/matcha-zh-tw-en-8k-gguf/tree/33f06aa142b003b67a090af975558cb39e181953

Verified Sep 13

Model artifacts

1 artifact

matcha8k.gguf

gguf · 100 MB · SHA-256 a0dfbe2cd4c8…1d5d · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 language: - zh - en pipeline_tag: text-to-speech tags: - matcha-tts - tts - ggml - gguf - rapidspeech - jetson - edge base_model: Luigi/matcha-zh-tw-en-8k library_name: rapidspeech.cpp --- # matcha-zh-tw-en-8k-gguf — Matcha-TTS (CFM) zh-TW/en @ 8 kHz, **ggml/GGUF** for RapidSpeech.cpp A **GGUF** conversion of [**Luigi/matcha-zh-tw-en-8k**](https://huggingface.co/Luigi/matcha-zh-tw-en-8k) (a Taiwan-Mandarin + English, 8 kHz Matcha-TTS — CFM acoustic model + Vocos vocoder) for the **ggml** runtime [**RapidSpeech.cpp**](https://github.com/vieenrose/RapidSpeech.cpp). It lets Matcha-TTS run on **ggml-CUDA / ggml-CPU** — in particular on the **Jetson Nano gen1** (Maxwell sm_53, CUDA 10.2), where the ggml engine fits the 4 GB budget that ONNX Runtime's CUDA EP overflows. ## Contents - `matcha8k.gguf` (~105 MB) — **474 learned tensors** (acoustic 394 + Vocos 80) + all hyper-parameters (`sample_rate=8000`, `ode_steps=3`, …) packed into GGUF KV. Converted with `scripts/convert_matcha_onnx_to_gguf.py` from the source bundle's `model-steps-3.onnx` + `vocos-8khz-univ.onnx`. **Round-trip validated**: every GGUF tensor's bytes == the ONNX numpy (PASS). ## Validated on real Jetson Nano gen1 (sm_53 Maxwell, CUDA 10.2, 4 GB) Cross-compiled (x86 → aarch64, gcc-8.3 + CUDA-10.2 / kreier patch set) and run on the device: | Backend | warm RTF | audio | notes | |---|---|---|---| | RapidSpeech **ggml-CPU** | **0.507** | 3.18 s synth, valid (rms 0.118) | | | RapidSpeech **ggml-CUDA** | **0.184** | identical audio | `Tegra X1 cc 5.3`, `MATCHA_USE_CUDA=1`; ~2.75× over CPU | Real-utterance check (sentence `這個星期的研究進度。`, 10 phoneme ids): **2.40 s @ 8 kHz, 151 mel frames** — matches the ONNX reference. ggml-CUDA peaks well under the 4 GB budget (the engine's whole reason on this device). ## Usage (RapidSpeech.cpp) The GGUF self-identifies as `arch=matcha-tts`. The CFM acoustic + Vocos vocoder + iSTFT run end-to-end; the **text→phoneme frontend is supplied externally** (e.g. sherpa-onnx's Matcha frontend, `--debug=1` dumps the int32 phoneme ids). Feed the ids via the `MATCHA_IDS` env (little-endian int32 file): ```sh # CPU matcha_e2e_test matcha8k.gguf out.wav # built-in fixture, or: MATCHA_IDS=ids.bin matcha_e2e_test matcha8k.gguf out.wav # ggml-CUDA (Maxwell sm_53+; matcha defaults to CPU because CUDA-graph replay needs cc>=800) MATCHA_USE_CUDA=1 MATC...

Source context: 25 downloads · 0 likes · Pipeline text-to-speech · Library rapidspeech.cpp · Repo Luigi/matcha-zh-tw-en-8k-gguf