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
| { | |
| "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 | |
| } | |