metadata
license: apache-2.0
language:
- ig
- yo
- ha
- pcm
tags:
- text-to-speech
- tts
- african-languages
- voice-cloning
- spark-tts
datasets:
- chukypedro/african_voice
Spark-TTS African Voice
Text-to-speech model for African languages, fine-tuned using the Spark-TTS / SparkVox framework.
Languages Supported
- Igbo (ig)
- Yoruba (yo)
- Hausa (ha)
- Nigerian Pidgin (pcm)
Features
- Text-to-Speech (TTS): Generate natural speech from text
- Voice Cloning: Clone any voice from a short reference audio
- Controllable TTS: Control pitch, speed, and gender
Model Structure
This model follows the official Spark-TTS HuggingFace format:
├── config.yaml
├── BiCodec/
│ ├── config.yaml
│ └── model.safetensors
├── LLM/
│ ├── config.json
│ ├── model.safetensors
│ └── (tokenizer files)
└── wav2vec2-large-xlsr-53/
├── config.json
├── preprocessor_config.json
└── pytorch_model.bin
Usage with Spark-TTS Inference
# 1. Clone the official Spark-TTS inference repo
# git clone https://github.com/SparkAudio/Spark-TTS.git
# cd Spark-TTS
# 2. Download this model
# from huggingface_hub import snapshot_download
# snapshot_download("NaijaLingo/Spark-TTS-African", local_dir="pretrained_models/Spark-TTS-African")
# 3. Run inference
from cli.SparkTTS import SparkTTS
model = SparkTTS(model_dir="pretrained_models/Spark-TTS-African", device="cuda:0")
# Voice cloning
wav = model.inference(
text="How you dey, hope say you dey fine?",
prompt_speech_path="path/to/reference.wav",
prompt_text="Optional transcript of reference audio",
)
# Controllable TTS (no reference audio needed)
wav = model.inference(
text="Sannu da zuwa, yaya kake?",
gender="female",
pitch="moderate",
speed="moderate",
)
Training Details
| Component | Details |
|---|---|
| Base LLM | Qwen2.5-0.5B-Instruct |
| Audio Tokenizer | BiCodec (fine-tuned on African voice data) |
| Feature Extractor | wav2vec2-large-xlsr-53 |
| Dataset | chukypedro/african_voice |
| Training Framework | SparkVox |
License
Apache 2.0