Instructions to use hf-internal-testing/tiny-random-WhisperForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WhisperForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-WhisperForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-WhisperForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#3
by Xenova HF Staff - opened
onnx/decoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:d81c4d7badb1eb1baf27a6a7e7e26052974bcd159a7cf667d3369541f602dec1
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onnx/decoder_model_merged.onnx
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oid sha256:e54b5934199506ed4cb5401ce99f3a3b5e13a96a18d49517812207058d83cddb
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size 3425697
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onnx/decoder_with_past_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:608f79f83580d39d5b6813c081531d5740b429a52fbd20818b1d2b320d45542f
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size 3317195
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onnx/encoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:fdfb24fc30f9265ce27691c4bf9a5c2d3830160dc2f4c3faa5147fc81fde591b
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size 70540
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