Instructions to use nyralabs/CrisperWhisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nyralabs/CrisperWhisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nyralabs/CrisperWhisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("nyralabs/CrisperWhisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("nyralabs/CrisperWhisper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update generation_config.json
Browse files- generation_config.json +1 -1
generation_config.json
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"max_length": 448,
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"median_filter_width": 3,
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"no_timestamps_token_id": 50364,
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"num_beams":
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"pad_token_id": 50257,
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"prev_sot_token_id": 50362,
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"return_timestamps": false,
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"max_length": 448,
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"median_filter_width": 3,
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"no_timestamps_token_id": 50364,
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"num_beams": 5,
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"pad_token_id": 50257,
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"prev_sot_token_id": 50362,
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"return_timestamps": false,
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