Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bgstud/whisper-small-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-small-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-small-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-small-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-small-en") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 20
Browse files
config.json
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 51865
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}
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.26.0.dev0",
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"use_cache": true,
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"vocab_size": 51865
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}
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pytorch_model.bin
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runs/Dec04_13-56-14_e11b5e005f63/1670163284.3396375/events.out.tfevents.1670163284.e11b5e005f63.76.1
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training_args.bin
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