Instructions to use MatricariaV/Kabardian-ASR-kaggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatricariaV/Kabardian-ASR-kaggle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MatricariaV/Kabardian-ASR-kaggle")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("MatricariaV/Kabardian-ASR-kaggle") model = AutoModelForCTC.from_pretrained("MatricariaV/Kabardian-ASR-kaggle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
0772b3e
1
Parent(s): d394997
Upload lm-boosted decoder
Browse files- .gitattributes +1 -0
- alphabet.json +1 -0
- language_model/5gram_correct.arpa +3 -0
- language_model/attrs.json +1 -0
- language_model/unigrams.txt +0 -0
- preprocessor_config.json +1 -0
- special_tokens_map.json +28 -4
- tokenizer_config.json +1 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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language_model/5gram_correct.arpa filter=lfs diff=lfs merge=lfs -text
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alphabet.json
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{"labels": ["*", "b", "i", "j", "q", "\u0255", "\u0263", "\u026c", "\u0290", "\u0430", "\u0431", "\u0432", "\u0435", "\u0438", "\u0439", "\u043c", "\u043d", "\u043e", "\u0440", "\u0441", "\u0443", "\u0447", "\u0448", "\u044b", "\u044d", "\u044e", "\u0451", "\u049b", "\u04ad", "\u04b3", "\u04b5", "\u04e1", "\u0525", "\u10f6", "\u2047", "", "<s>", "</s>", " "], "is_bpe": false}
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language_model/5gram_correct.arpa
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e0f2a75318c5192d2b9ecdb3c47bbff0eac7905f99ed4719151a6d0ec3b0941
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size 63706271
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language_model/attrs.json
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{"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}
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language_model/unigrams.txt
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preprocessor_config.json
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "Wav2Vec2ProcessorWithLM",
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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special_tokens_map.json
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"bos_token":
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{
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"bos_token": {
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"content": "<s>",
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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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},
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"eos_token": {
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"content": "</s>",
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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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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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"single_word": false
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}
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}
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tokenizer_config.json
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"extra_special_tokens": {},
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"replace_word_delimiter_char": " ",
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"target_lang": "kbd",
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"extra_special_tokens": {},
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"processor_class": "Wav2Vec2ProcessorWithLM",
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"replace_word_delimiter_char": " ",
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"target_lang": "kbd",
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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