Automatic Speech Recognition
Transformers
PyTorch
Ganda
wav2vec2
audio
common_voice
hf-asr-leaderboard
robust-speech-event
speech
Eval Results (legacy)
Instructions to use cahya/wav2vec2-luganda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/wav2vec2-luganda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/wav2vec2-luganda")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cahya/wav2vec2-luganda") model = AutoModelForCTC.from_pretrained("cahya/wav2vec2-luganda") - Notebooks
- Google Colab
- Kaggle
removed bos eos
Browse files- alphabet.json +1 -1
- special_tokens_map.json +1 -1
- tokenizer_config.json +1 -1
alphabet.json
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{"labels": [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "\u2047", ""
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{"labels": [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "\u2047", ""], "is_bpe": false}
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special_tokens_map.json
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{"bos_token":
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{"bos_token": null, "eos_token": null, "unk_token": "[UNK]", "pad_token": "[PAD]"}
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tokenizer_config.json
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{"unk_token": "[UNK]", "bos_token":
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{"unk_token": "[UNK]", "bos_token": null, "eos_token": null, "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
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