from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.models.vits import Vits
from TTS.utils.audio import AudioProcessor
from TTS.tts.utils.text.tokenizer import TTSTokenizer

# Load config
config = VitsConfig()
config.load_json("config.json")

# Load model
ap = AudioProcessor.init_from_config(config)
tokenizer, config = TTSTokenizer.init_from_config(config)
model = Vits(config, ap, tokenizer)
model.load_checkpoint(config, "model.pth", eval=True)

# Generate speech
outputs = model.synthesize(
    text="ɛte sɛn? me ho yɛ.",
    config=config,
    speaker_id=0,
)
wav = outputs["wav"]

Training Details

Architecture: VITS (Variational Inference TTS)
Dataset: google/WaxalNLP (twi_tts subset)
Sample rate: 22050 Hz
Speakers: 4 unique speakers
Training steps: 150000

Citation

bibtex

@misc{vits-twi-2024,
  title={Twi (Akan) VITS TTS Model},
  author={zirri23},
  year={2024},
  publisher={Hugging Face},
}

zirri23/vits-twi-waxalnlp

Twi (Akan) VITS TTS Model

Features

Usage

Training Details

Citation

zirri23/
vits-twi-waxalnlp