Automatic Speech Recognition
Transformers
PyTorch
Latin
wav2vec2
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use lsb/wav2vec2-base-it-latin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lsb/wav2vec2-base-it-latin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lsb/wav2vec2-base-it-latin")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lsb/wav2vec2-base-it-latin") model = AutoModelForCTC.from_pretrained("lsb/wav2vec2-base-it-latin") - 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": "|", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
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vocab.json
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{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6, "g": 7, "h": 8, "i": 9, "k": 10, "l": 11, "m": 12, "n": 13, "o": 14, "p": 15, "q": 16, "r": 17, "s": 18, "t": 19, "u": 20, "x": 21, "y": 22, "z": 23, "|": 0, "[UNK]": 24, "[PAD]": 25}
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