Instructions to use rossevine/Model_G_S_Berita_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_S_Berita_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_S_Berita_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_S_Berita_Wav2Vec2") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_S_Berita_Wav2Vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload lm-boosted decoder
Browse files- preprocessor_config.json +1 -0
- special_tokens_map.json +14 -0
- tokenizer_config.json +1 -0
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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{
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"additional_special_tokens": [
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{
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"content": "<s>",
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"lstrip": false,
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{
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"additional_special_tokens": [
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{
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<s>",
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"lstrip": false,
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tokenizer_config.json
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"eos_token": "</s>",
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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": null,
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"eos_token": "</s>",
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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": null,
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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