Comments (1)
Hi, yes that would be possible. As you said you would need to modify the loss function and also the evaluation. You could also use the loss function as it is and then just quantize the output of the trained model
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Related Issues (20)
- Do we need any other calibration before predicting MOS? HOT 1
- ValueError: n_wins 2072 > max_length 1300 HOT 3
- MOS results change depending on the audio sequence HOT 1
- Can the quality of front-end signal processing be evaluated? HOT 1
- modular version HOT 1
- Usage of PackedSequence class HOT 1
- Bug in Alignment HOT 1
- Prediction runs slowly HOT 4
- n_wins HOT 2
- Making .exe from anaconda HOT 1
- What is the best performance for overall quality ? Higher? Lower? And the range ? HOT 4
- Script quits unexpectedly (without errors) when trying to export model to ONNX HOT 6
- Is it possible to use the model as a function? HOT 1
- RuntimeError: Could not infer dtype of numpy.float32 HOT 10
- Using NISQA as a loss function HOT 2
- NISQA Corpus download issue HOT 1
- Continuous metrics? HOT 4
- TTS naturalness prediction based on which model
- Could you tell me if the MOS rating is objective or subjective? HOT 4
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