Instructions to use pnparam/PNP_dys_asr_960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnparam/PNP_dys_asr_960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pnparam/PNP_dys_asr_960h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pnparam/PNP_dys_asr_960h") model = AutoModelForCTC.from_pretrained("pnparam/PNP_dys_asr_960h") - Notebooks
- Google Colab
- Kaggle
add tokenizer
Browse files- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.json +1 -0
special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]"}
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tokenizer_config.json
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{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
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vocab.json
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{"p": 0, "y": 2, "s": 3, "b": 4, "e": 5, "o": 6, "g": 7, "l": 8, "m": 9, "f": 10, "d": 11, "a": 12, "k": 13, "r": 14, "i": 15, "t": 16, "w": 17, "n": 18, "h": 19, "u": 20, "c": 21, "|": 1, "[UNK]": 22, "[PAD]": 23}
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