Instructions to use vtlustos/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vtlustos/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vtlustos/whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vtlustos/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("vtlustos/whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- merges.txt +0 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
- vocab.json +0 -0
merges.txt
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tokenizer.json
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tokenizer_config.json
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"model_max_length":
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"pad_token": "<|endoftext|>",
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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vocab.json
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