switching to high quality piper tts and added label translations

2026-01-29 23:48:19 +01:00
commit d80c619df9
3934 changed files with 1451600 additions and 0 deletions
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+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+"""Provide entry point to preprocess ONNX model especially for QNN."""
+
+import argparse
+import pathlib
+
+import onnx
+
+from onnxruntime.quantization.execution_providers import qnn
+
+
+def _parse_arguments():
+    """Parse cmdline arguments."""
+    parser = argparse.ArgumentParser(description="Arguments for QNN model preprocess.")
+
+    parser.add_argument("--input_model_path", "-i", required=True, help="Path to the input ONNX model.")
+    parser.add_argument("--output_model_path", "-o", required=True, help="Path to the output ONNX model.")
+
+    # Save preprocessed model with external data.
+    parser.add_argument(
+        "--save_as_external_data",
+        action="store_true",
+        help="Whether the output model would be saved with external data.",
+    )
+    parser.add_argument(
+        "--all_tensors_to_one_file",
+        action="store_true",
+        help="Whether to save all external data in one file or save each tensor to a file named with the tensor name.",
+    )
+    parser.add_argument(
+        "--external_data_location",
+        help="Filename of the external file where all tensors are saved. The path is relative to the model path.",
+    )
+    parser.add_argument(
+        "--external_data_size_threshold",
+        default=1024,
+        type=int,
+        help="Tensors with data size larger than this threshold are converted to external data.",
+    )
+    parser.add_argument(
+        "--external_data_convert_attribute",
+        action="store_true",
+        help="Whether to save all tensors, including attribute tensors, to external data.",
+    )
+
+    # Preprocess options.
+    parser.add_argument(
+        "--fuse_layernorm",
+        action="store_true",
+        help="Whether to fuse matched sequences into LayerNormalization nodes if possible.",
+    )
+
+    # I/O layouts.
+    parser.add_argument(
+        "--inputs_to_make_channel_last",
+        nargs="+",
+        default=None,
+        help="List of graph input names to be transposed into channel-last.",
+    )
+
+    parser.add_argument(
+        "--outputs_to_make_channel_last",
+        nargs="+",
+        default=None,
+        help="List of graph output names to be transposed into channel-last.",
+    )
+
+    # Fix dynamic input shapes.
+    parser.add_argument(
+        "--dynamic_input_shapes",
+        nargs=2,
+        action="append",
+        type=str,
+        default=None,
+        help="Model input name and desired static shape in comma seprated format, for example: 'input' 1,3,256,256",
+    )
+
+    # Exclude initializer from input
+    parser.add_argument(
+        "--exclude_initializer_from_input",
+        action="store_true",
+        help="Whether to exclude initializer from input if model.ir_version >= 4",
+    )
+
+    return parser.parse_args()
+
+
+def qnn_preprocess_model(
+    model_input: str | pathlib.Path | onnx.ModelProto,
+    model_output: str | pathlib.Path,
+    fuse_layernorm: bool = False,
+    save_as_external_data: bool = False,
+    all_tensors_to_one_file: bool = False,
+    external_data_location: str | None = None,
+    external_data_size_threshold: int = 1024,
+    external_data_convert_attribute: bool = False,
+    inputs_to_make_channel_last: list[str] | None = None,
+    outputs_to_make_channel_last: list[str] | None = None,
+    dynamic_input_shapes: list[tuple[str, str]] | None = None,
+    exclude_initializer_from_input: bool = False,
+) -> bool:
+    """Preprocess ONNX model for QNN.
+
+    Args:
+        model_input: A path or ONNX ModelProto specifiying the model to be preprocessed.
+        model_output: A path specifying where the preprocessed model to be saved.
+        fuse_layernorm: A bool specifying whether to fuse the matched sequence into a single LayerNormalization node.
+            Defaults to False.
+        save_as_external_data: A bool specifying whether to save model with external data. Defaults to False.
+        all_tensors_to_one_file: A bool specifying whether to save all external data in one file or save each tensor to
+            a file named with the tensor name. This argument is effective only when `save_as_external_data` is True.
+            Defaults to False.
+        external_data_location: A str specifying where to save the external data. The path is relative to the model
+            path. This argument is effective only when `save_as_external_data` is True. Defaults to the model name.
+        external_data_size_threshold: An int specifying the threshold of data size for tensors be saved as external
+            data. This argument is effective only when `save_as_external_data` is True. Defaults to 1024.
+        external_data_convert_attribute: A bool specifying whether to save all tensors including attributes as external
+            data. This argument is effective only when `save_as_external_data` is True. Defaults to False.
+        inputs_to_make_channel_last: A list of strs specifying graph input names to be transposed into channel-last.
+            Defaults to None.
+        outputs_to_make_channel_last: A list of strs specifying graph output names to be transposed into channel-last.
+            Defaults to None.
+        dynamic_input_shapes: A list of tuples specifying model input name to and its static shape in comma seprated
+            format, for example: [('input', '1,3,256,256')]. Defaults to None.
+        exclude_initializer_from_input: A bool specifying whether to exclude initializer from input. Defaults to False.
+
+    Returns:
+        A bool indicating whether the model is modified.
+    """
+    return qnn.qnn_preprocess_model(
+        model_input,
+        model_output,
+        fuse_layernorm=fuse_layernorm,
+        save_as_external_data=save_as_external_data,
+        all_tensors_to_one_file=all_tensors_to_one_file,
+        external_data_location=external_data_location,
+        external_data_size_threshold=external_data_size_threshold,
+        external_data_convert_attribute=external_data_convert_attribute,
+        inputs_to_make_channel_last=inputs_to_make_channel_last,
+        outputs_to_make_channel_last=outputs_to_make_channel_last,
+        dynamic_input_shapes=dynamic_input_shapes,
+        exclude_initializer_from_input=exclude_initializer_from_input,
+    )
+
+
+if __name__ == "__main__":
+    args = _parse_arguments()
+    qnn_preprocess_model(
+        args.input_model_path,
+        args.output_model_path,
+        fuse_layernorm=args.fuse_layernorm,
+        save_as_external_data=args.save_as_external_data,
+        all_tensors_to_one_file=args.all_tensors_to_one_file,
+        external_data_location=args.external_data_location,
+        external_data_size_threshold=args.external_data_size_threshold,
+        external_data_convert_attribute=args.external_data_convert_attribute,
+        inputs_to_make_channel_last=args.inputs_to_make_channel_last,
+        outputs_to_make_channel_last=args.outputs_to_make_channel_last,
+        dynamic_input_shapes=args.dynamic_input_shapes,
+        exclude_initializer_from_input=args.exclude_initializer_from_input,
+    )