Add scripts for exporting Piper TTS models to sherpa-onnx (#2299)

scripts/piper/dynamic_quantization.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import argparse
+
+import onnxmltools
+from onnxmltools.utils.float16_converter import convert_float_to_float16
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input",
+        type=str,
+        required=True,
+    )
+    parser.add_argument(
+        "--output-fp16",
+        type=str,
+        required=True,
+    )
+
+    parser.add_argument(
+        "--output-int8",
+        type=str,
+        required=True,
+    )
+    return parser.parse_args()
+
+
+# for op_block_list, see also
+# https://github.com/microsoft/onnxruntime/blob/089c52e4522491312e6839af146a276f2351972e/onnxruntime/python/tools/transformers/float16.py#L115
+#
+# libc++abi: terminating with uncaught exception of type Ort::Exception:
+# Type Error: Type (tensor(float16)) of output arg (/dp/RandomNormalLike_output_0)
+# of node (/dp/RandomNormalLike) does not match expected type (tensor(float)).
+#
+# libc++abi: terminating with uncaught exception of type Ort::Exception:
+# This is an invalid model. Type Error: Type 'tensor(float16)' of input
+# parameter (/enc_p/encoder/attn_layers.0/Constant_84_output_0) of
+# operator (Range) in node (/Range_1) is invalid.
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(
+        onnx_fp32_model,
+        keep_io_types=True,
+        op_block_list=[
+            "RandomNormalLike",
+            "Range",
+        ],
+    )
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def main():
+    args = get_args()
+    print(args)
+
+    in_filename = args.input
+    output_fp16 = args.output_fp16
+    output_int8 = args.output_int8
+
+    quantize_dynamic(
+        model_input=in_filename,
+        model_output=output_int8,
+        weight_type=QuantType.QUInt8,
+    )
+
+    export_onnx_fp16(in_filename, output_fp16)
+
+
+if __name__ == "__main__":
+    main()