Monitoring metric #284
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Monitoring metric #284
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merge latest works
ㄴ attention alignment diagonality ㄴ average max attention weight ㄴ f0 RMSE ㄴ MCD
ㄴ re-implementing and solving errors.
ㄴ Audio processing: trimming silence(if it < 23 db), preemphasis, amplitude normalization ㄴ Remove short clip(if it < 14847 samples, It maybe percentile 0.10 my own dataset)
ㄴ Replacing MCD(metric name) to log_MCD
# Conflicts: # train.py
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ㄴ debugging
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Summary
You can see monitoring metrics for the Seq2Seq TTS model on the Tensorboard.
Monitoring metric
Attention Alignment Diagonality(AAD):
AAD is defined as the length of attention alignment path divided by the length of the diagonal path.
The attention alignment path is the line connecting maximum values for each time-step in the attention weight matrix.
The meaning of this metric is the degree of learning of the relationship between the encoder and the decoder.
Average max attention weight:
The meaning of this metric is the degree of learning of the relationship between the encoder and the decoder.
log Mel Cepstrum Distortion (MCD)
The acoustic similarity between the synthesized audio and the target audio.
f0 RMSE
The similarity of fundamental frequency between the synthesized audio and the target audio.
Future work
Example
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