ONNX
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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-4.0
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+ datasets:
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+ - CSTR-Edinburgh/vctk
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+ language:
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+ - en
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+ ---
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+ Trained with Matcha-TTS - [Github](https://github.com/shivammehta25/Matcha-TTS) | [Paper](https://arxiv.org/abs/2309.03199)
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+
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+ How to Infer see [Github page](https://github.com/akjava/Matcha-TTS-Japanese/tree/main/examples)
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+ ## License
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+ You must abide by cc-by-4.0 vctk license.
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+ ### Datasets License
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+ - VCTK Dataset license are cc-by-4.0
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+ ### Tools License
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+
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+ These tools did not effect output license.
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+
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+ - Matcha-TTS - MIT
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+ - ONNX Simplifier - Apache2.0
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+ - onnxruntime - MIT
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+ ### Converted model Owner(me)
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+ I release my output under MIT License.If you want your license ,convert it by yourself
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+
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+ ## How to Convert
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+ ### Export Model
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+ see Matcha-TTS [ONNX export](https://github.com/shivammehta25/Matcha-TTS)
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+ ### simplify model
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+ ```
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+ from onnxsim import simplify
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+ import onnx
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+
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+ import argparse
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+ parser = argparse.ArgumentParser(
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+ description="create simplify onnx"
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+ )
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+ parser.add_argument(
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+ "--input","-i",
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+ type=str,required=True
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+ )
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+ parser.add_argument(
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+ "--output","-o",
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+ type=str
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+ )
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+ args = parser.parse_args()
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+
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+ src_model_path = args.input
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+ if args.output == None:
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+ dst_model_path = src_model_path.replace(".onnx","_simplify.onnx")
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+ else:
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+ dst_model_path = args.output
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+
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+
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+ model = onnx.load(src_model_path)
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+ model_simp, check = simplify(model)
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+
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+ onnx.save(model_simp, dst_model_path)
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+ ```
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+ ### quantize model
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+ ```
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+ from onnxruntime.quantization import quantize_dynamic, QuantType
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+ import argparse
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+ parser = argparse.ArgumentParser(
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+ description="create quantized onnx"
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+ )
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+ parser.add_argument(
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+ "--input","-i",
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+ type=str,required=True
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+ )
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+ parser.add_argument(
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+ "--output","-o",
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+ type=str
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+ )
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+ args = parser.parse_args()
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+
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+ src_model_path = args.input
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+ if args.output == None:
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+ dst_model_path = src_model_path.replace(".onnx","_q8.onnx")
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+ else:
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+ dst_model_path = args.output
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+
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+ # only QUInt8 works well
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+ quantized_model = quantize_dynamic(src_model_path, dst_model_path, weight_type=QuantType.QUInt8)
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+ ```