Instructions to use Siyong/MT_RN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siyong/MT_RN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Siyong/MT_RN")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Siyong/MT_RN") model = AutoModelForCTC.from_pretrained("Siyong/MT_RN") - 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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{"k": 0, "n": 1, "f": 3, "g": 4, "d": 5, "o": 6, "l": 7, "h": 8, "b": 9, "e": 10, "p": 11, "u": 12, "m": 13, "v": 14, "a": 15, "y": 16, "r": 17, "q": 18, "s": 19, "x": 20, "z": 21, "i": 22, "c": 23, "j": 24, "'": 25, "w": 26, "t": 27, "[UNK]": 28, "[PAD]": 29, "|": 2}
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