Instructions to use diarray/bam-vits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diarray/bam-vits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="diarray/bam-vits")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("diarray/bam-vits") model = AutoModelForTextToWaveform.from_pretrained("diarray/bam-vits") - Notebooks
- Google Colab
- Kaggle
File size: 741 Bytes
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"add_blank": true,
"added_tokens_decoder": {
"0": {
"content": "_",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"backend": "custom",
"clean_up_tokenization_spaces": true,
"is_local": true,
"is_uroman": false,
"language": null,
"local_files_only": false,
"model_max_length": 1000000000000000019884624838656,
"normalize": false,
"pad_token": "_",
"phonemize": false,
"tokenizer_class": "VitsTokenizer",
"unk_token": "<unk>",
"verbose": false
}
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