Murya Piper Hausa Tts

WAXAL–Piper Hausa TTS (8-voice)

Multi-speaker Hausa text-to-speech, fine-tuned on Google's WAXAL corpus. To our knowledge this is the first TTS model trained on WAXAL-Hausa, the first open multi-speaker Hausa voice model, and the first built by a native speaker of the target language with native evaluation embedded at every decision point.

• Voices: 8 (M1–M4 male, F1–F4 female)
• Sample rate: 22.05 kHz
• Format: ONNX (~73.5 MB) — real-time on commodity CPU, no GPU or cloud required
• Orthography-native: trained in grapheme mode on a custom 42-symbol Hausa alphabet — the hooked consonants ɓ ɗ ƙ ƴ and glottal ' are first-class model symbols and are never flattened.
• License: MIT (commercial use permitted). Please cite Google WAXAL (below).

Developed by Adamu Danjuma Abubakar (ADAB-TECH Labs) · github.com/adab-tech

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Usage

# Requires: pip install piper-tts  (or use the ONNX directly with onnxruntime)
import wave
from piper import PiperVoice

voice = PiperVoice.load("model.onnx", config_path="model.onnx.json")

# Speaker index is NOT sorted-alphabetical — read speaker_id_map from
# model.onnx.json rather than assuming an order. Current map:
#   F2:0  M3:1  M2:2  F4:3  M4:4  F1:5  M1:6  F3:7
with wave.open("out.wav", "wb") as wav:
    voice.synthesize("sannu da zuwa, ina fatan kana lafiya.", wav, speaker_id=6)  # M1

Text tip: normalize input to Unicode NFD before synthesis. NFC composes tone-marked vowels (e.g. á) into codepoints outside the training alphabet, which drops the vowel; NFD keeps the base vowel. Lowercase input (the model is trained casefolded). Digits should be spelled out.

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Training

• Base / warm start: rhasspy/piper (VITS-family, MIT), warm-started from the public-domain en_US-lessac-medium checkpoint (acoustic init only — all voice identity comes from WAXAL).
• Method: grapheme-mode (--phoneme-type text) with an injected Hausa codepoint alphabet, because espeak-ng has no Hausa voice.
• Data: WAXAL Hausa TTS clips (8 speakers), filtered to 1–20 s, digit-free. A character-level forced-alignment segmentation pass (whisper-large-v3; robust to phonetic ASR output) recovered long/digit clips into clean sentence segments, growing usable audio from 2.83 h → 6.03 h (2,693 utterances) from the same source recordings.
• Evaluation: native-speaker audition on fixed benchmark sentences, including minimal-pair orthography/tone tests, gating base-model selection, data certification, and epoch budget. A scored MOS (Mean Opinion Score) listening study is planned but not yet run — treat existing native-speaker audits as directional, not a substitute.
• Status: the v2 fine-tuning run is complete (Modal run stopped cleanly; final model.onnx/model.onnx.json exported 2026-07-06). Exact final epoch/step number has not yet been pulled from the checkpoint and is not stated here to avoid guessing.

Full methodology: see the project technical report.

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Intended use & limitations

Intended for: conversational and everyday-domain Hausa speech — accessibility (voice-first access for low-literacy users), education, IVR, offline/edge apps in bandwidth-constrained regions.

Limitations:

• Per-voice naturalness reflects ~45 min of audio per speaker; a single-speaker corpus with far more hours (e.g. BibleTTS) may sound smoother on one voice, but is single-speaker and single-register. This model's strengths are voice diversity, everyday-domain prosody, natural (non-scripted) delivery, and a permissive license.
• Trained on read speech; extreme expressivity/emotion is out of scope.
• Not validated for safety-critical or medical dictation without human review.

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Attribution & citation

Training data derives from Google WAXAL (google/WaxalNLP, CC-BY-4.0 / CC-BY-SA-4.0). Please credit WAXAL and its speakers in any derivative work.

@misc{abubakar2026waxalpiper,
  title  = {WAXAL--Piper Hausa TTS: Sovereign Multi-Speaker Speech Synthesis
            from a Small Corpus},
  author = {Abubakar, Adamu Danjuma},
  year   = {2026},
  note   = {ADAB-TECH Labs. Fine-tuned on the Google WAXAL corpus.},
  url    = {https://github.com/adab-tech}
}

Motsi ya fi laɓewa.