poto-tts

English text-to-speech that pronounces Ghanaian words properly. Kwabena, Achimota, the Okuapenhene, Nyankpani, Gyasi — names, places and titles that every general TTS mangles.

To be clear about what this is not: the voices do not have a Ghanaian accent. They are Kokoro's speakers — British and American — and this library changes what they say, not who they sound like. A Ghanaian-sounding voice needs a model trained on Ghanaian speech, which is a different problem. What you get here is an English voice that no longer stumbles over Ghanaian words.

                 general TTS                    poto-tts
Kwabena          kwˈeɪbnə                       kwabˈina
Achimota         ɐtʃɪmˈoʊɾə                     atʃimˈota
Okuapenhene      ˈoʊkjuːˌeɪpənhˌiːn             okwapenhˈene
Nyankpani        nˌaɪɐŋkpˈɑːni                  njankpˈani
Gyasi            dʒaɪʲˈɑːsi                     dʒˈasi

pip install poto-tts
poto-tts "Kwabena went to Achimota" -o out.wav

from poto_tts import load

tts = load()                               # downloads the voice on first use
tts.save("The Okuapenhene met Nana Bawumia", "out.wav")

How it works

Pronunciation comes from a Ghanaian lexicon compiled into espeak's own dictionary. Your text is sent to the model unchanged:

The dictionary holds 44,321 Ghanaian words -- names, places, titles, Twi and Ga loans, food, money, everyday coinage. Every other word is left to espeak's ordinary English. So Kwabena, Achimota and Okuapenhene come from the lexicon, while bus, passed and through are pronounced as any English TTS would.

That division is deliberate and was arrived at the hard way. The upstream lexicon covers the whole language -- bus and way have entries too, recording the Ghanaian accent of an English word rather than a pronunciation that cannot be derived. Using all of it made every word of every sentence Ghanaian: from as /frɔm/, on as /an/. That is a different product from an English voice that says Ghanaian names properly. poto_tts/data/ghanaian-words.txt is the subset used, and tools/classify_lexicon.py regenerates it.

To see what will be sent to espeak, without loading a model:

poto-tts --phonemes "Nana Addo met Kwame Nkrumah"

Changing any of it is docs/CUSTOMISING.md.

Voices

Eight British speakers:

tts = load(voice="Emmanuel")
tts.annotate("Yaw went to Kumasi by bus")   # which words the lexicon supplied

poto-tts --voices

British, and only British, for two reasons. It is the variety Ghanaian English is closest to -- non-rhotic, with vowels in roughly the same places. And the pronunciations shipped here are shaped for it: entries are read with espeak's British phoneme table, so the phonemes Kokoro receives are non-rhotic and use /a/ where American English has /æ/. Kokoro's American speakers were trained on American phonemes, and handing them these is a mismatch users should not have to find by ear.

Kokoro's twenty American speakers and twenty-five other-language speakers are still in the model and still reachable by their own names -- load(voice="af_heart") -- for anyone who wants to try. They are simply not offered.

Cross-platform

sherpa-onnx runs on Android, iOS, WebAssembly, C++, C, Go, C#, Java, Kotlin, Rust, Dart and Swift, and they all get the same pronunciations as Python, because the front-end is data rather than code. There is no Python-only path to fall back from: the dictionary and the voice file are the front-end.

Ship four things and send plain text with lang=en:

onnx/model.onnx      the model
voices.bin           speaker embeddings
tokens.txt           phoneme -> id
espeak-ng-data/      the dictionary and the voice -- the Ghanaian part

Swap in a stock espeak-ng-data and you get a working voice that mispronounces every Ghanaian name, with no error. That directory is the deliverable.

Send plain text with lang=en. examples/bare_sherpa_onnx.py does exactly that with no poto_tts import at all -- it is there to keep this claim testable rather than merely stated.

What does need Python is authoring a pronunciation: poto-tts dict compiles the lexicon into espeak-ng-data and wants the espeak-ng binary as well. That is a build step you run once; nothing on the device does it.

sherpa-onnx docs · the voice on the Hub

Web interface and REST API

pip install 'poto-tts[api]'
poto-tts serve                    # then open http://localhost:8080

Paste text and hear it, or upload a CSV for batch work and get back a ZIP of WAVs with a manifest. One HTML file, no build step, no CDN — it renders on a laptop or a Pi with no internet.

text,voice,filename
"Kwabena went to Achimota",Grace,kwabena
"The Okuapenhene met Nana Bawumia",Emmanuel,durbar

text is the only required column, and a single-column file with no header works too. Batches are capped at 500 rows and refused rather than trimmed.

Changing how a word is said

The lexicon will still miss your grandmother's name. Put it in a TSV -- Ghana IPA, space-separated -- and rebuild the dictionary:

Owusu	o w u s u
Tetteh	t ɛ t ɛ

pip install 'poto-tts[lexicon]'
poto-tts dict --out build/espeak-ng-data --ghanaian-stress --extra my_words.tsv

Where each kind of change belongs -- a single word, a sound across the whole accent, or the lexicon upstream -- and why a rule must never overrule the lexicon: docs/CUSTOMISING.md.

Models

Fetched from the Hub through huggingface_hub, so downloads are counted: ghananlpcommunity/poto-tts-kokoro-gh. Each repo's config.json names its own files, speakers and licence, so a new voice needs no release of this library:

tts = load(repo_id="your-org/your-voice")

POTO_TTS_CACHE moves model files off ~/.cache.

Training a voice

tools/ holds a full Piper training pipeline — dataset fetch, filtering, speaker clustering, forced alignment, punctuation from measured pauses, training, export. It was used to train a Ghanaian voice on 85 hours of broadcast speech, and that voice is not shipped: at 45,000 steps it sounded worse than Kokoro with a good front-end, and its corpus is broadcast recordings whose speakers never consented to being modelled. The pipeline is kept because it works on any dataset you hold rights to.

Thresholds in it are measured rather than chosen, and the comments record the distributions they came from — including the ones that were wrong first.

Credits

sherpa-onnx (k2-fsa) · Kokoro-82M (hexgrad, Apache-2.0) · ghana-english-g2p (GhanaNLP) · espeak-ng, whose voice files turn out to be a perfectly good place to keep an accent

MIT, except the bundled espeak-ng-data, which is GPL-3.0.