whisper.cpp/models/convert-whisper-to-openvino.py
Ryan Metcalfe 62b81276e0
whisper : add OpenVINO support ()
* 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>
2023-07-04 15:56:11 +03:00

54 lines
1.6 KiB
Python

import argparse
import torch
from whisper import load_model
import os
from openvino.tools import mo
from openvino.runtime import serialize
import shutil
def convert_encoder(hparams, encoder, mname):
encoder.eval()
mel = torch.zeros((1, 80, 3000))
onnx_folder=os.path.join(os.path.dirname(__file__),"onnx_encoder")
#create a directory to store the onnx model, and other collateral that is saved during onnx export procedure
if not os.path.isdir(onnx_folder):
os.makedirs(onnx_folder)
onnx_path = os.path.join(onnx_folder, "whisper_encoder.onnx")
torch.onnx.export(
encoder,
mel,
onnx_path,
input_names=["mel"],
output_names=["output_features"]
)
# use model optimizer to convert onnx to OpenVINO IR format
encoder_model = mo.convert_model(onnx_path, compress_to_fp16=True)
serialize(encoder_model, xml_path='ggml-' + mname + '-encoder-openvino.xml')
#cleanup
if os.path.isdir(onnx_folder):
shutil.rmtree(onnx_folder)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
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)
args = parser.parse_args()
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
raise ValueError("Invalid model name")
whisper = load_model(args.model).cpu()
hparams = whisper.dims
encoder = whisper.encoder
# Convert encoder to onnx
convert_encoder(hparams, encoder, args.model)