Instructions to use optimum-internal-testing/tiny-random-whisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny-random-whisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="optimum-internal-testing/tiny-random-whisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("optimum-internal-testing/tiny-random-whisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("optimum-internal-testing/tiny-random-whisper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload WhisperForConditionalGeneration
Browse files- config.json +14 -1
- model.safetensors +1 -1
config.json
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"encoder_layerdrop": 0.0,
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"encoder_layers": 1,
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"eos_token_id": 50257,
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"forced_decoder_ids":
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"mask_feature_length": 10,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 1,
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"eos_token_id": 50257,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"mask_feature_length": 10,
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model.safetensors
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size 871760
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version https://git-lfs.github.com/spec/v1
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size 871760
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