XTTS-v2 Hausa Conversational (Common Voice Domain-Adapted)

This repository provides fine-tuned weights, extended tokenizer vocabulary, and configuration files for Coqui XTTS-v2 adapted for conversational Hausa (ha).

This model represents Stage 2 in a two-stage training strategy:

1. Stage 1 (Foundation): samuelolubukun/xtts-v2-hausa-openbible — 8-epoch foundation model trained on high-fidelity OpenBible audio to master Hausa orthography, hook letters (ɓ, ɗ, ƙ, ƴ), and stable phonetics.
2. Stage 2 (Conversational Adaptation — This Model): Adapted from the Stage 1 checkpoint on benjaminogbonna/nigerian_common_voice_dataset (Hausa split) using conservative learning rates (1e-6) to learn natural spoken cadence, informal pauses, and diverse speaker acoustic environments without catastrophic forgetting.

Model Summary

• Base Architecture: Coqui XTTS-v2 (Autoregressive GPT text/mel encoder + Discrete VAE spectrogram vocoder)
• Base Model Checkpoint: samuelolubukun/xtts-v2-hausa-openbible (31,360 steps)
• Parameters: 521.8M
• Target Language: Hausa (ha)
• Tokenizer: Extended Byte Pair Encoding (BPE) with special language tag [ha] (extended vocabulary: 8,320 tokens)
• Adaptation Dataset: benjaminogbonna/nigerian_common_voice_dataset (Hausa split)
• Filtered Training Set: 3,795 clean conversational audio samples (filtered for length 1.5s–12s and valid text)
• Adaptation Steps: 7,592 optimization steps (4 epochs, lr=1.5e-6, grad_accum=4, batch_size=2)
• Hardware: NVIDIA A10G (24 GB VRAM)

Conversational Adaptation Dashboard & Convergence

The visualization below highlights the multidimensional shift from the Stage 1 OpenBible foundation model to this Stage 2 Common Voice conversational model across prosody, pitch dynamics, acoustic loss, and speech continuity:

Training Progression Summary

Acoustic Improvements Over Stage 1

Objective acoustic and linguistic evaluation across unseen Common Voice test speakers demonstrates significant improvements:

Audio Comparison: Original vs. Stage 1 vs. Stage 2 Cloned Synthesis

Below is a side-by-side comparison between the unseen reference speakers and the speech synthesized by the Stage 1 foundation model versus this Stage 2 conversational adapted model.

All reference audios are sourced from benjaminogbonna/nigerian_common_voice_dataset (Hausa split) by Benjamin Ogbonna:

Sample 1 (Conversational Narrative): "Wani abu ya kama gaban jacob."

Sample 2 (Community & Indigenes): "Na ci kwai da yawa a ranar bikin Easter Sunday."

Sample 3 (Civic & Leadership): "Yunwa wani abu ne da jiki kan nuna yayin da yake buƙatar abinci."

Sample 4 (Cultural & Heritage): "Ko zan iya fara canka."

Sample 5 (Expressive Spoken Idiom): "Da gaske muke."

🔁 Stage 1 OpenBible Benchmark Samples: Foundation vs. Conversational Comparison

These are the exact benchmark samples originally evaluated on the samuelolubukun/xtts-v2-hausa-openbible Stage 1 foundation model repository. Below, the same reference audio clips and prompt texts are synthesized side-by-side using the Stage 2 Conversational fine-tuned model to directly compare the change in cadence, natural pacing, and conversational tone:

Stage 1 Benchmark Sample 1: "Bude kofar. Na san kina ciki."

Stage 1 Benchmark Sample 2: "Na ga wani aikin dabba mai ban mamaki a circus"

Stage 1 Benchmark Sample 3: "Nagode da yadda ka ƙona mini riga ta da sigarin ka."

Repository Files

This repository contains all files necessary to run standalone synthesis or merge with standard Coqui pipelines:

How to Use

Ensure coqui-tts and dependencies are installed:

pip install coqui-tts "transformers<4.45.0" torch torchaudio huggingface_hub

Load the model and synthesize speech directly from this Hugging Face repository:

import os
import re
import torch
import torchaudio
from huggingface_hub import snapshot_download
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

# 1. Download model repository
model_dir = snapshot_download(repo_id="samuelolubukun/xtts-v2-hausa-openbible-conversational")

# 2. Load model config and checkpoint
config = XttsConfig()
config.load_json(os.path.join(model_dir, "config.json"))
model = Xtts.init_from_config(config)
model.load_checkpoint(
    config,
    checkpoint_path=os.path.join(model_dir, "model.pth"),
    vocab_path=os.path.join(model_dir, "vocab.json"),
    use_deepspeed=False
)
model.cuda() if torch.cuda.is_available() else model.cpu()

# 3. Text cleaner fallback for custom language support
orig_preprocess = model.tokenizer.preprocess_text
def safe_preprocess_text(txt, lang):
    try:
        return orig_preprocess(txt, lang)
    except NotImplementedError:
        txt = txt.lower()
        return re.sub(r"\s+", " ", txt).strip()
model.tokenizer.preprocess_text = safe_preprocess_text

# 4. Compute speaker conditioning latents (using bundled reference audio or your own 3-10s WAV)
ref_audio = os.path.join(model_dir, "samples/sample1_ref.wav")
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
    audio_path=ref_audio,
    gpt_cond_len=30,
    max_ref_length=30,
    sound_norm_refs=False,
)

# 5. Synthesize speech
text = "A wannan lokacin banbancin tsakanin mutum da dabba ba daɗi ba ragi illa suttura."
out = model.inference(
    text=text,
    language="ha",
    gpt_cond_latent=gpt_cond_latent,
    speaker_embedding=speaker_embedding,
    temperature=0.3,
    length_penalty=1.0,
    repetition_penalty=5.0,
    top_k=30,
    top_p=0.85,
)

# 6. Save output audio (24,000 Hz)
torchaudio.save("output_ha.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
print("Saved synthesized speech to output_ha.wav")

Limitations and Ethical Usage

• Domain & Conversational Acoustic Artifacts: While Stage 2 removes scripture-reading bias and significantly improves informal prosody, Common Voice recordings contain diverse crowd-sourced microphones, varying gain levels, and background ambient noise. Very noisy speaker prompts may slightly affect synthesized clarity.
• Glottal and Hooked Letter Sensitivity: Hausa relies heavily on hooked characters (ɓ, ɗ, ƙ, ƴ) and apostrophes for glottal stops. Text fed into the model should preserve proper standard Hausa orthography for optimal pronunciation.
• Ethical Voice Cloning: This model is intended for research, linguistic preservation, and accessibility in African languages. Explicit consent should be obtained prior to cloning recognizable individual voices.
• Licensing: Inherits the Coqui Public Model License / MRCUL.

Author & Attribution

Developed and fine-tuned by Samuel Olubukun:

• Website: samuelolubukun.com
• GitHub: @samolubukun
• Hugging Face: @samuelolubukun

Citation

If you use this model or checkpoints in your research, applications, or benchmarks, please cite:

@misc{olubukun2026xttshausaconversational,
  author = {Samuel Olubukun},
  title = {XTTS-v2 Hausa Conversational: Domain Adaptation on Nigerian Common Voice},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/samuelolubukun/xtts-v2-hausa-conversational}}
}

Acknowledgments

• Base Model: Coqui XTTS-v2
• Foundation Model: samuelolubukun/xtts-v2-hausa-openbible
• Dataset: Benjamin Ogbonna for curating and releasing benjaminogbonna/nigerian_common_voice_dataset, and the Mozilla Common Voice contributors.