switching to high quality piper tts and added label translations
This commit is contained in:
+37
@@ -0,0 +1,37 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import pathlib
|
||||
|
||||
import onnx
|
||||
|
||||
|
||||
def optimize_qdq_model():
|
||||
parser = argparse.ArgumentParser(
|
||||
os.path.basename(__file__),
|
||||
description="Update a QDQ format ONNX model to ensure optimal performance when executed using ONNX Runtime.",
|
||||
)
|
||||
|
||||
parser.add_argument("input_model", type=pathlib.Path, help="Provide path to ONNX model to update.")
|
||||
parser.add_argument("output_model", type=pathlib.Path, help="Provide path to write updated ONNX model to.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
model = onnx.load(str(args.input_model.resolve(strict=True)))
|
||||
|
||||
# run QDQ model optimizations here
|
||||
|
||||
# Originally, the fixing up of DQ nodes with multiple consumers was implemented as one such optimization.
|
||||
# That was moved to an ORT graph transformer.
|
||||
print("As of ORT 1.15, the fixing up of DQ nodes with multiple consumers is done by an ORT graph transformer.")
|
||||
|
||||
# There are no optimizations being run currently but we expect that there may be in the future.
|
||||
|
||||
onnx.save(model, str(args.output_model.resolve()))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
optimize_qdq_model()
|
||||
Reference in New Issue
Block a user