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Browse files- README.md +15 -7
- config.json +4 -0
- preprocessor_config.json +0 -15
- whisper-base_beamsearch.int8.onnx +3 -0
- whisper-base_beamsearch.onnx +3 -0
README.md
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## Code for models export
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Download model and export with Beam Search and Forced Decoder Input Ids
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```shell
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python3 -m onnxruntime.transformers.models.whisper.convert_to_onnx -m openai/whisper-base --output whisper-onnx --
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```
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Save
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```py
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from transformers import
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processor =
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processor.save_pretrained("whisper-onnx")
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```
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## Code for models export
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Read onnxruntime [instruction](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/whisper/README.md) for convert Whisper to ONNX.
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Download model and export with *Beam Search* and *Forced Decoder Input Ids*:
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```shell
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python3 -m onnxruntime.transformers.models.whisper.convert_to_onnx -m openai/whisper-base --output ./whisper-onnx --use_forced_decoder_ids --optimize_onnx --precision fp32
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```
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Save tokenizer config
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```py
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from transformers import WhisperTokenizer
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processor = WhisperTokenizer.from_pretrained("openai/whisper-base")
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processor.save_pretrained("whisper-onnx")
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```
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Create `config.json`
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```json
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{
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"model_type": "whisper-ort",
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"features_size": 80
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}
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```
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config.json
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{
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"model_type": "whisper-ort",
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"features_size": 80
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}
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preprocessor_config.json
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{
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"chunk_length": 30,
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"dither": 0.0,
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 80,
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"hop_length": 160,
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"n_fft": 400,
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"n_samples": 480000,
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"nb_max_frames": 3000,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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whisper-base_beamsearch.int8.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:537719570b1b859d5d805283f509bce621d8ebbba8189609d8fb12064a68530c
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size 107166530
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whisper-base_beamsearch.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:732180688d163f91283769140db2a63a47da21ca1c1379a6c686b4bd62f2c412
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size 398028367
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