Instructions to use Trimux/basqueModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trimux/basqueModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Trimux/basqueModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Trimux/basqueModel") model = AutoModelForTextToWaveform.from_pretrained("Trimux/basqueModel") - Notebooks
- Google Colab
- Kaggle
Upload feature extractor
Browse files- preprocessor_config.json +11 -0
preprocessor_config.json
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{
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"feature_extractor_type": "VitsFeatureExtractor",
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"feature_size": 80,
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"hop_length": 256,
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"max_wav_value": 32768.0,
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"n_fft": 1024,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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