Instructions to use subasine/xls_r_init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subasine/xls_r_init with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="subasine/xls_r_init")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("subasine/xls_r_init") model = AutoModelForCTC.from_pretrained("subasine/xls_r_init") - Notebooks
- Google Colab
- Kaggle
File size: 1,099 Bytes
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"added_tokens_decoder": {
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"content": "<bos>",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false,
"special": false
},
"43": {
"content": "<eos>",
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},
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"content": "<unk>",
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},
"45": {
"content": "<pad>",
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"rstrip": true,
"single_word": false,
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}
},
"bos_token": "<bos>",
"clean_up_tokenization_spaces": true,
"do_lower_case": false,
"eos_token": "<eos>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<pad>",
"processor_class": "Wav2Vec2Processor",
"replace_word_delimiter_char": " ",
"target_lang": null,
"tokenizer_class": "Wav2Vec2CTCTokenizer",
"unk_token": "<unk>",
"word_delimiter_token": "|"
}
|