switching to high quality piper tts and added label translations

2026-01-29 23:48:19 +01:00
commit d80c619df9
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+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation.  All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+# An offline standalone script to declassify an ONNX model by randomizing the tensor data in initializers.
+# The ORT Performance may change especially on generative models.
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+from onnx import load_model, numpy_helper, onnx_pb, save_model
+
+# An experimental small value for differentiating shape data and weights.
+# The tensor data with larger size can't be shape data.
+# User may adjust this value as needed.
+SIZE_THRESHOLD = 10
+
+
+def graph_iterator(model, func):
+    graph_queue = [model.graph]
+    while graph_queue:
+        graph = graph_queue.pop(0)
+        func(graph)
+        for node in graph.node:
+            for attr in node.attribute:
+                if attr.type == onnx_pb.AttributeProto.AttributeType.GRAPH:
+                    assert isinstance(attr.g, onnx_pb.GraphProto)
+                    graph_queue.append(attr.g)
+                if attr.type == onnx_pb.AttributeProto.AttributeType.GRAPHS:
+                    for g in attr.graphs:
+                        assert isinstance(g, onnx_pb.GraphProto)
+                        graph_queue.append(g)
+
+
+def randomize_graph_initializer(graph):
+    for i_tensor in graph.initializer:
+        array = numpy_helper.to_array(i_tensor)
+        # TODO: need to find a better way to differentiate shape data and weights.
+        if array.size > SIZE_THRESHOLD:
+            random_array = np.random.uniform(array.min(), array.max(), size=array.shape).astype(array.dtype)
+            o_tensor = numpy_helper.from_array(random_array, i_tensor.name)
+            i_tensor.CopyFrom(o_tensor)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Randomize the weights of an ONNX model")
+    parser.add_argument("-m", type=str, required=True, help="input onnx model path")
+    parser.add_argument("-o", type=str, required=True, help="output onnx model path")
+    parser.add_argument(
+        "--use_external_data_format",
+        required=False,
+        action="store_true",
+        help="Store or Save in external data format",
+    )
+    parser.add_argument(
+        "--all_tensors_to_one_file",
+        required=False,
+        action="store_true",
+        help="Save all tensors to one file",
+    )
+    args = parser.parse_args()
+
+    data_path = None
+    if args.use_external_data_format:
+        if Path(args.m).parent == Path(args.o).parent:
+            raise RuntimeError("Please specify output directory with different parent path to input directory.")
+        if args.all_tensors_to_one_file:
+            data_path = Path(args.o).name + ".data"
+
+    Path(args.o).parent.mkdir(parents=True, exist_ok=True)
+    onnx_model = load_model(args.m, load_external_data=args.use_external_data_format)
+    graph_iterator(onnx_model, randomize_graph_initializer)
+    save_model(
+        onnx_model,
+        args.o,
+        save_as_external_data=args.use_external_data_format,
+        all_tensors_to_one_file=args.all_tensors_to_one_file,
+        location=data_path,
+    )
+
+
+if __name__ == "__main__":
+    main()