MCD DTW tutorial #96
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MCD DTW tutorial #96
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blisc
reviewed
Nov 4, 2022
tts-evaluation-MCD-DTW.ipynb
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| "## Conclusion\n", | ||
| "<img src=\"imgs/riva-tts-MCD_DTW_final_comparision.jpeg\">\n", | ||
| "\n", | ||
| "From the graph above the value of MCD is greater for radtts audios than radtts audios, this is also reflected in the average MCD value for both models. Therefore we can conclude that fastpitch has better convergence than radtts. However we cannot evaluate the quality of audios generated by these models using MCD. MCD is a great tool for testing model convergence, but generated audios may have pronunciation and quality artefacts. Therefore MCD evaluation should be followed by a MOS(Mean opinion score) and CMOS(Comparative mean opinion scores) evaluation." |
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Can you clarify this sentence?
blisc
reviewed
Nov 8, 2022
tts-evaluation-MCD-DTW.ipynb
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| "sr = 22050\n", | ||
| "\n", | ||
| "## Mfcc params\n", | ||
| "n_mfcc=n_mels" |
tts-evaluation-MCD-DTW.ipynb
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| "source": [ | ||
| "def mel2mfcc(mels):\n", | ||
| " mfcc = librosa.feature.mfcc(S=mels, n_mfcc=n_mfcc)\n", | ||
| " mfcc = librosa.power_to_db(mfcc, ref=np.max)\n", |
| @@ -0,0 +1,495 @@ | |||
| { | |||
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How did you get the mel spectrograms from the models?
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For radTTS I used: https://github.com/NVIDIA/radtts
For fastpitch, I used the method described here: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_fastpitch
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Do have a code snippet that you can add to the notebook?
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I can add for fastpitch, for radTTS I had to do some changes in their inference script. That wont be possible in the notebook.
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Mcd tutorial