Inference/recipe not working properly.

Discussed in https://github.com/coqui-ai/TTS/discussions/639

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^{Originally posted by BillyBobQuebec July 10, 2021} I am training Tacotron2-DDC (LJ) from scratch using the recipe provided with no changes, tensorboard looks good to my eyes, but the alignment and duration seem to be way off when I actually try inferencing the audio, I’m suspecting it’s a problem with it being inferenced improperly. specifically, the r-value that it is attempting to inference with. Here is the command that I used to initiate training:

cd ~/repo/coqui-clean
bash recipes/ljspeech/tacotron2-DDC/run.sh

Since the recipe uses gradual training which uses “r” as the starting value for the fine decoder (if I understand it correctly), but then changes it over time during training, I suspect it’s using the starting r value during inference, instead of the latest r-value the fine decoder was at during training, when I try to force a different r value for inference (by passing through a recipe config with "r": 2, instead of "r": 6, ) it gives me this error:

RuntimeError: Error(s) in loading state_dict for Tacotron2:
size mismatch for decoder.linear_projection.linear_layer.weight: copying a param with shape torch.Size([480, 1536]) from checkpoint, the shape in current model is torch.Size([160, 1536]).
size mismatch for decoder.linear_projection.linear_layer.bias: copying a param with shape torch.Size([480]) from checkpoint, the shape in current model is torch.Size([160]).
size mismatch for decoder.stopnet.1.linear_layer.weight: copying a param with shape torch.Size([1, 1504]) from checkpoint, the shape in current model is torch.Size([1, 1184]).

Here’s the command used for inferencing and here’s how it sounds at different points:

cd ~
cp ~/repo/coqui-clean/recipes/ljspeech/tacotron2-DDC/scale_stats.npy .
cp ~/repo/coqui-clean/recipes/ljspeech/tacotron2-DDC/tacotron2-DDC.json config.json
CUDA_VISIBLE_DEVICES="" tts \
  --text "Hello I bought this T.V. today, and it's cold outside. I should probably grab my sweater and go to your moms house." \
  --model_path ~/repo/coqui-clean/recipes/ljspeech/tacotron2-DDC/ljspeech-ddc-July-06-2021_09+10AM-8fbadad6/checkpoint_280000.pth.tar \
  --config_path config.json \
  --out_path output.wav

https://user-images.githubusercontent.com/74849975/125181759-267e6280-e1d6-11eb-8e81-1de96b682245.mp4

https://user-images.githubusercontent.com/74849975/125181760-28e0bc80-e1d6-11eb-90fc-8392e5223fc7.mp4

https://user-images.githubusercontent.com/74849975/125181761-2aaa8000-e1d6-11eb-8477-9a6c1b59843f.mp4

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[BillyBobQuebec]

Ah it seems like that may be the problem, looks like I was using a slightly different config, I also noticed some things that might’ve affected training. I’m going to train from scratch again and make sure everything is correct, i’ll post an update of how inferencing sounds with hifi-gan within the next few days.

[BillyBobQuebec]

@erogol Thank you for pushing the new update! I’m currently training with the same config used at the beginning of this issue, except I’m now training using your new V0.1.3 to see if the stop net was the problem and if I can finally replicate the inference quality in the current pretrained voice.