README.md

---
license: cc-by-4.0
datasets:
- CSTR-Edinburgh/vctk
language:
- en
---
Trained with Matcha-TTS - [Github](https://github.com/shivammehta25/Matcha-TTS) | [Paper](https://arxiv.org/abs/2309.03199)

How to Infer see [Github page](https://github.com/akjava/Matcha-TTS-Japanese/tree/main/examples)
## License
You must abide by cc-by-4.0 vctk license.
### Datasets License
- VCTK Dataset license are cc-by-4.0
### Tools License

These tools did not effect output license.

- Matcha-TTS - MIT
- ONNX Simplifier - Apache2.0
- onnxruntime - MIT
### Converted model Owner(me)
I release my output under MIT License.If you want your license ,convert it by yourself

## How to Convert
### Export Model
see Matcha-TTS [ONNX export](https://github.com/shivammehta25/Matcha-TTS)

```
python -m matcha.onnx.export matcha_vctk.ckpt vctk_t2.onnx --vocoder-name "hifigan_T2_v1" --vocoder-checkpoint "generator_v1"
```

### simplify model
```
from onnxsim import simplify
import onnx

import argparse
parser = argparse.ArgumentParser(
        description="create simplify onnx"
    )
parser.add_argument(
        "--input","-i",
        type=str,required=True
    )
parser.add_argument(
        "--output","-o",
        type=str
    )
args = parser.parse_args()

src_model_path = args.input
if args.output == None:
    dst_model_path = src_model_path.replace(".onnx","_simplify.onnx")
else:
    dst_model_path = args.output


model = onnx.load(src_model_path)
model_simp, check = simplify(model)

onnx.save(model_simp, dst_model_path)
```
### quantize model
```
from onnxruntime.quantization import quantize_dynamic, QuantType
import argparse
parser = argparse.ArgumentParser(
        description="create quantized onnx"
    )
parser.add_argument(
        "--input","-i",
        type=str,required=True
    )
parser.add_argument(
        "--output","-o",
        type=str
    )
args = parser.parse_args()

src_model_path = args.input
if args.output == None:
    dst_model_path = src_model_path.replace(".onnx","_q8.onnx")
else:
    dst_model_path = args.output
    
# only QUInt8 works well
quantized_model = quantize_dynamic(src_model_path, dst_model_path, weight_type=QuantType.QUInt8)
```