Tacotron2 teacher forcing about tensorflowtts HOT 12 CLOSED

more clear now, thanks again

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@superhg2012 audio quality is good after 60k training steps. before that steps, the model bias on mel-groundtruth so the quality is not good when inference.

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if training to 200K, what about inferenced audio quality? will it be better?

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@superhg2012 i trained to 120k and i saw the valid loss didn't decrease so i stopped it. I think the quality is very good at 120k now, don't u think so ?

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@superhg2012 i trained to 120k and i saw the valid loss didn't decrease so i stopped it. I think the quality is very good at 120k now, don't u think so ?

I trained Tacotron without window when inference, audio quality is statisfying. I want to know that whether window or monotonic constraint during inference help improve audio quanlity or only useful for fastspeech alignment

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@superhg2012 window contraints use in case an alignment explode when inferencing very long sentences. But in my model, somehow it can be inference the samples > 3000 decoder steps ^^. I see the model without window contraint is better. For training fastspeech, you can đecode tacotron without alignment contraint and use output mel spectrogram of tacotron for fastspeech training, it's my FastSpeech V3 (significantly improve over FastSpeech V1 (window contraint + teacher forcing + mel groundtruth))

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Do you mean training Fastspeech V3 use predicted mel-spectrogram from Tacotron2 and alignments from Tacotron2 instead of ground truth mel-spectrorgam?

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@superhg2012 yes. alignment from tacotron-2 120K without window masking trick and use predicted mel for fastspeech training. You can hear a audio samples on valid set, this is a significant improvement.

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thank u , your alignment were generated same way as mel-spectrogram? or genreated with GTA mode?

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no teacher forcing, no window masking, save durations and mels at the same time, you need modify the code a bit :d

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All right, thank you! There are many differences with other open implementions of Fastspeech like Fastspeech. it's alignment are generated with teacher forcing. Also, In Fastspeech2 , they pointed out that using predicted mel-spectrogram from Teacher model(TransformerTTS) have some information loss compared with ground-truth ones, since the quality of the audio synthesized from the generated mel-spectrograms is usually worse than that from the ground-truth ones. So, puzzling....But I will try your idea.

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@superhg2012 you can compared my results with other implementation to make decision :))). there are no puzzling here. On FastSpeech 1 they use alignment extracted from tacotron2 so they use predicted mel from tacotron to train fastspeech, it's make sense. On fastspeech 2, they use duration extracted from mel groundtruth by Montreal Forced Aligner so they use mel groundtruth to train :)). So, a durations and a mels should come from the same source :))).

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