Text-to-Speech
Transformers
Safetensors
Igbo
vits
text-to-audio
tts
mms
nigerian-languages
low-resource
waxal
soro-tts
igbo
Eval Results (legacy)
Instructions to use Shinzmann/soro-tts-ibo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shinzmann/soro-tts-ibo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Shinzmann/soro-tts-ibo")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Shinzmann/soro-tts-ibo") model = AutoModelForTextToWaveform.from_pretrained("Shinzmann/soro-tts-ibo") - Notebooks
- Google Colab
- Kaggle
File size: 254 Bytes
3b5c972 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"feature_extractor_type": "VitsFeatureExtractor",
"feature_size": 80,
"hop_length": 256,
"max_wav_value": 32768.0,
"n_fft": 1024,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
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