Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Hebrew
wav2vec2
Generated from Trainer
robust-speech-event
Instructions to use imvladikon/wav2vec2-xls-r-300m-lm-hebrew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imvladikon/wav2vec2-xls-r-300m-lm-hebrew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="imvladikon/wav2vec2-xls-r-300m-lm-hebrew")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("imvladikon/wav2vec2-xls-r-300m-lm-hebrew") model = AutoModelForCTC.from_pretrained("imvladikon/wav2vec2-xls-r-300m-lm-hebrew", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Parent(s): 06e4742
Update tokenizer_config.json
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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": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "imvladikon/wav2vec2-xls-r-300m-hebrew", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}
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{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "imvladikon/wav2vec2-xls-r-300m-lm-hebrew", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}
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