Instructions to use vipul22/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vipul22/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vipul22/whisper-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vipul22/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("vipul22/whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 100
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- runs/May13_18-17-09_anaconda/events.out.tfevents.1684001834.anaconda +2 -2
- runs/May24_15-19-08_anaconda/1684941554.730367/events.out.tfevents.1684941554.anaconda.8059.1 +3 -0
- runs/May24_15-19-08_anaconda/events.out.tfevents.1684941554.anaconda.8059.0 +3 -0
- training_args.bin +2 -2
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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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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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": "4.27.1",
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"use_cache": true,
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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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pytorch_model.bin
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runs/May13_18-17-09_anaconda/events.out.tfevents.1684001834.anaconda
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runs/May24_15-19-08_anaconda/1684941554.730367/events.out.tfevents.1684941554.anaconda.8059.1
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training_args.bin
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