This is a single-speaker Text-to-Speech (TTS) model for Kanuri language, fine-tuned from the CML-TTS multilingual checkpoint using the YourTTS architecture. The model features one female speaker (spk1) and can generate high-quality Kanuri speech from text input.
The model was trained on approximately 10 hours of Kanuri speech data from a high-quality source:
The model supports standard Kanuri orthography including diacritics:
Characters: abcdefghijklmnopqrstuvwxyzáúǝəә
Punctuation: ',-?. and space
The model was fine-tuned until no improvement is recorded with the following configuration:
The model was evaluated on a set of 30 Kanuri sentences by a human evaluator using two criteria:
Sample Evaluation Sentences:
Evaluation Metrics:
pip install coqui-tts torch scipy numpy huggingface_hub
from TTS.api import TTS
from huggingface_hub import hf_hub_download
import json
import tempfile
import scipy.io.wavfile as wavfile
import numpy as np
import os
# Download and setup model files
config_path = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "config.json")
with open(config_path, 'r') as f:
config = json.load(f)
# Download required files
model_path = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "best_model_264313.pth")
speakers_file = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "speakers.pth")
language_ids_file = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "language_ids.json")
d_vector_file = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "d_vector.pth")
config_se_file = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "config_se.json")
model_se_file = hf_hub_download("CLEAR-Global/TWB-Voice-Kanuri-TTS-1.0", "model_se.pth")
# Update config paths
config["speakers_file"] = speakers_file
config["language_ids_file"] = language_ids_file
config["d_vector_file"] = [d_vector_file]
config["model_args"]["speakers_file"] = speakers_file
config["model_args"]["language_ids_file"] = language_ids_file
config["model_args"]["d_vector_file"] = [d_vector_file]
config["model_args"]["speaker_encoder_config_path"] = config_se_file
config["model_args"]["speaker_encoder_model_path"] = model_se_file
# Save updated config
temp_config = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False)
json.dump(config, temp_config, indent=2)
temp_config.close()
# Load TTS model
tts = TTS(model_path=model_path, config_path=temp_config.name)
# Generate speech
text = "Loktu nǝngriyi ye lan, nǝyama kulo ye dǝ so shawwa ro wurazen."
speaker = "spk1" # Options: spk1 (female)
wav = tts.synthesizer.tts(text=text.lower(), speaker_name=speaker)
wav_array = np.array(wav, dtype=np.float32)
wavfile.write("output.wav", tts.synthesizer.output_sample_rate, wav_array)
For batch processing multiple sentences, use the provided inference script:
python3 inference.py /path/to/model.pth
# or
python3 inference.py /path/to/model/directory
The script will generate audio for all evaluation sentences with the available speaker.
This model is released under CC-BY-NC license. For commercial licensing or other uses, please contact [email protected].
If you use this model in your research or applications, please cite:
@misc{yourtts-kanuri-2025,
title={YourTTS Kanuri Single-Speaker Text-to-Speech Model},
author={Alp Öktem},
year={2025},
howpublished={Hugging Face Model Hub},
url={https://huggingface.co/your-username/yourtts-kanuri-singlespeaker}
}
This dataset was created by CLEAR Global with support from the Patrick J. McGovern Foundation. We acknowledge the following open source projects and resources that made this model possible:
Alp Öktem ([email protected])