stable-twi-tts

Twi / Ghanaian English TTS (IPA-driven)

A Piper VITS voice for Twi that also reads the English words Ghanaian text is full of. It is driven by IPA phonemes rather than orthography, which is what lets one model serve both languages from a shared inventory.

Warm-started from en_US/libritts_r/medium and finetuned on 327 h (164 h Twi + 164 h Ghanaian English), 22.05 kHz, 1,555 speaker embeddings.

Use it

pip install stable-twi-tts && pip install "stable-twi-tts[twi]" && apt install espeak-ng
stable-twi-tts --model . --voice twi-6 --text "Akwaaba, wo ho te sen?" --out hello.wav
stable-twi-tts --model . --language mixed --voice twi-1 \
    --text "Mepe se mesua [computer science] wo [University of Ghana]." --out mixed.wav

Code: https://github.com/GhanaNLP/stable-twi-tts

Voices are ranked by measurement, not by training hours

12 voices are exposed of 1,555 speaker embeddings. Each synthesised the same held-out text, which was re-recognised and scored for phoneme error -- Twi with ghana-speech-phoneme-asr, the English spans with KoelLabs/xlsr-english-01.

The two rankings disagree sharply. twi-1 is the best code-switch voice yet 21st of 30 on pure Twi. Two of the three best Twi voices have under 3.3 h of audio each and would never have survived an hours-based shortlist. voices.json records every measurement.

Quality, honestly

Round-trip phoneme error against a real-audio floor, on held-out text:

Twi is good; English is markedly weaker. English had a third of the utterances (43k vs 151k) at 3.5x the length, and VITS learns alignment per utterance, so English got far less practice on harder material. English audio was also 16 kHz upsampled to 22.05 kHz, so English voices are band-limited to 8 kHz and sound duller. Twi was 24 kHz and is unaffected.

Finetuning

finetune/ has the Lightning checkpoint, the phoneme id map, the training command and dataset stats. The phoneme id map is required -- the weights encode "id 26 means /n/", so loading them against a different map yields nonsense. See finetune/FINETUNING.md, which also documents two traps: resize the speaker table before loading, and clear the phoneme cache or stale targets are silently reused.

Limits

• Speakers are derived, not real identities -- pseudo-speakers from clustering unlabelled broadcast audio. One person may appear as two voices, and no voice is a consented identity.
• Trained on broadcast speech, so it suits news-register text better than conversation.
• Training targets came from an ASR, not human annotation, at roughly 17% phoneme error.
• cc-by-nc-4.0: non-commercial, inherited from the Twi source audio.