62b81276e0
* openvino: use OpenVINO encoder inference * openvino: add python script for OpenVINO model generation * whisper: Fix 'unused' warnings when OpenVINO isn't enabled in build * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * whisper: Fix compilation error * whisper: revert whisper_get_openvino_path_encoder & whisper_get_openvino_path_cache to non-const func signatures * cmake: Add openvino-encoder as separate object target * whisper : minor style fixes * minor : indentation fixes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import argparse
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import torch
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from whisper import load_model
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import os
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from openvino.tools import mo
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from openvino.runtime import serialize
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import shutil
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def convert_encoder(hparams, encoder, mname):
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encoder.eval()
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mel = torch.zeros((1, 80, 3000))
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onnx_folder=os.path.join(os.path.dirname(__file__),"onnx_encoder")
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#create a directory to store the onnx model, and other collateral that is saved during onnx export procedure
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if not os.path.isdir(onnx_folder):
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os.makedirs(onnx_folder)
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onnx_path = os.path.join(onnx_folder, "whisper_encoder.onnx")
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torch.onnx.export(
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encoder,
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mel,
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onnx_path,
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input_names=["mel"],
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output_names=["output_features"]
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)
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# use model optimizer to convert onnx to OpenVINO IR format
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encoder_model = mo.convert_model(onnx_path, compress_to_fp16=True)
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serialize(encoder_model, xml_path='ggml-' + mname + '-encoder-openvino.xml')
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#cleanup
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if os.path.isdir(onnx_folder):
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shutil.rmtree(onnx_folder)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
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args = parser.parse_args()
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if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
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raise ValueError("Invalid model name")
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whisper = load_model(args.model).cpu()
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hparams = whisper.dims
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encoder = whisper.encoder
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# Convert encoder to onnx
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convert_encoder(hparams, encoder, args.model)
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