Matcha-TTS (LJSpeech)

Matcha-TTS's LJSpeech flow-matching TTS checkpoint, exported for loom.cpp. Takes phoneme ids, not text.

This is a loom.cpp export: a single self-describing GGUF that carries its own graph topologies, tokenizer (if any) and driver script, produced by loom-exporter.

Original model

Exported from Matcha-TTS (LJSpeech checkpoint). Weights are unmodified; this repo packages the same parameters into loom.cpp's GGUF format.

License

mit, inherited from the base model above.

Language(s)

en

Usage

Run it with loom-py -- loom-py-rt on PyPI:

pip install -U "loom-py-rt[hub,phonemes]"

NOTE: This is a work in progress. For now, in order to avoid license conflicts and keep dependencies at a minimum, we opted for orthography2ipa as our "swiss-knife" phonemizer. For deep-orthography languages like English, to get stressing rules and context-based phonemization, the phonemizer must register a reference lexicon (e.g., ipa-dict) to get highly accurate phonemization. Full quality can be achieved by phonemizing the text yourself using your engine of choice and feeding it to the model via the argument phonemes as in the example below.

import loom

model = loom.Model.from_pretrained("loom-ai-org/matcha-tts-ljspeech-loom")

# matcha-tts-ljspeech is trained on phonemes. Its symbol table ships in the GGUF, so the only piece that is not in
# the file is grapheme-to-phoneme -- a property of the language rather than of this checkpoint, which
# is why it is the `phonemes` extra above rather than part of the model.

# THE FULL-QUALITY PATH: phonemes you produced yourself, with whatever G2P you trust. The symbol table
# in the GGUF is what encodes them, so anything that emits IPA works.
audio = model.text2speech.infer(phonemes="həˈloʊ wˈɜːld", sample_rate=22050)
audio.save("out.wav")

# THE BUILT-IN PATH: text straight in, phonemized by the bundled rule-based G2P. Good enough for
# shallow orthographies; for English see the note above, and give it a lexicon so it has stress and
# real vowels to work with -- "time" is /tɪm/ without one.
#
# open-dict-data/ipa-dict (MIT) publishes ~65k-entry wordlists WITH stress for en_UK and en_US, in
# almost the right shape: its IPA is wrapped in slashes and a rare entry carries two comma-separated
# variants, both of which the loader rejects. Take the RAW file: a github.com/.../blob/... URL serves
# an HTML PAGE, and the sed below will turn that into a .tsv that parses to zero entries -- which is
# indistinguishable from no lexicon at all except for the warning `set_lexicon` raises. Two lines:
#     curl -LO https://raw.githubusercontent.com/open-dict-data/ipa-dict/master/data/en_UK.txt
#     sed 's:/::g; s/\t\([^,]*\),.*/\t\1/' en_UK.txt > en_UK.tsv
loom.phonemizers.set_lexicon("en_UK.tsv")    # a path, an http(s):// URL, or hf://<repo>/<path>

# sample_rate=22050: this checkpoint does not carry its own rate, so it is a value you have to
# know from the model's documentation and pass. It is used only if the GGUF declares none; a wrong rate
# does not fail, it plays the voice at the wrong speed.
audio = model.text2speech.infer("hello world", sample_rate=22050)
audio.save("out.wav")

The layer underneath

The call above is the high-level door: one per task, named for the modality pair it maps between, with the windowing, sampling and assembly this model needs already applied. Under it, model.infer(...) passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the door does not name.

model.driver_source prints that driver, including a header comment documenting every argument it accepts for this model, and is the authority on it. See loom-py for the API and loom.cpp for what the engine does between the two.

Files

• matcha-tts-ljspeech.gguf -- the model, exported with loom-exporter.