Instructions to use diarray/bam-vits-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diarray/bam-vits-train with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("diarray/bam-vits-train") model = AutoModelForPreTraining.from_pretrained("diarray/bam-vits-train") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer_config.json +34 -0
- vocab.json +46 -0
tokenizer_config.json
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{
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"add_blank": true,
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"added_tokens_decoder": {
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"0": {
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"content": "_",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"backend": "custom",
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"clean_up_tokenization_spaces": true,
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"is_local": true,
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"is_uroman": false,
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"language": null,
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"normalize": false,
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"pad_token": "_",
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"phonemize": false,
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"tokenizer_class": "VitsTokenizer",
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"unk_token": "<unk>",
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"verbose": false
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}
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vocab.json
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{
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" ": 2,
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"!": 5,
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",": 6,
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".": 7,
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"<unk>": 1,
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"?": 8,
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"_": 0,
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"a": 9,
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"b": 10,
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"c": 3,
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"d": 11,
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"e": 12,
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"f": 13,
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"g": 14,
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"h": 15,
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"i": 16,
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"j": 4,
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"k": 17,
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"l": 18,
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"m": 19,
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"n": 20,
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"o": 21,
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"p": 22,
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"q": 23,
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"r": 24,
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"s": 25,
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"t": 26,
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"u": 27,
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"v": 28,
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"w": 29,
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"x": 30,
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"y": 31,
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"z": 32,
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"ã": 33,
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"õ": 34,
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"ĩ": 35,
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"ŋ": 36,
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"ũ": 37,
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"ɔ": 38,
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"ɛ": 39,
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"ɲ": 40,
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"ʃ": 41,
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"ʒ": 42,
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"ẽ": 43
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}
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