54 lines
1.7 KiB
Python
54 lines
1.7 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, hparams.n_mels, 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=os.path.join(os.path.dirname(__file__),"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-v1, large-v2, large-v3)", required=True)
|
|
args = parser.parse_args()
|
|
|
|
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3"]:
|
|
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)
|