Trying too run Mozilla TTS on AWS, server problem

Archives TTS (Text-to-Speech)
#1

I am having multiple problems trying to run inference on AWS, when I try to use the server here is what happens:

python -m TTS.server.server --tts_checkpoint checkpoint_670000.pth.tar --tts_config config.json

Loading TTS model …
| > model config: config.json
| > checkpoint file: checkpoint_670000.pth.tar
Setting up Audio Processor…
| > sample_rate:22050
| > num_mels:80
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > num_freq:513
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:True
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:True
| > trim_db:60
| > do_sound_norm:False
| > stats_path:None
| > hop_length:256
| > win_length:1024
| > n_fft:1024
Using model: Tacotron2
Traceback (most recent call last):
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/runpy.py”, line 193, in _run_module_as_main
“main”, mod_spec)
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/runpy.py”, line 85, in _run_code
exec(code, run_globals)
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/TTS-0.0.3+3366328-py3.6.egg/TTS/server/server.py”, line 62, in
synthesizer = Synthesizer(args)
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/TTS-0.0.3+3366328-py3.6.egg/TTS/server/synthesizer.py”, line 36, in init
self.config.use_cuda)
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/TTS-0.0.3+3366328-py3.6.egg/TTS/server/synthesizer.py”, line 73, in load_tts
self.tts_model.load_state_dict(cp[‘model’])
File “/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 830, in load_state_dict
self.class.name, “\n\t”.join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Tacotron2:
Missing key(s) in state_dict: “encoder.convolutions.0.convolution1d.weight”, “encoder.convolutions.0.convolution1d.bias”, “encoder.convolutions.0.batch_normalization.weight”, “encoder.convolutions.0.batch_normalization.bias”, “encoder.convolutions.0.batch_normalization.running_mean”, “encoder.convolutions.0.batch_normalization.running_var”, “encoder.convolutions.1.convolution1d.weight”, “encoder.convolutions.1.convolution1d.bias”, “encoder.convolutions.1.batch_normalization.weight”, “encoder.convolutions.1.batch_normalization.bias”, “encoder.convolutions.1.batch_normalization.running_mean”, “encoder.convolutions.1.batch_normalization.running_var”, “encoder.convolutions.2.convolution1d.weight”, “encoder.convolutions.2.convolution1d.bias”, “encoder.convolutions.2.batch_normalization.weight”, “encoder.convolutions.2.batch_normalization.bias”, “encoder.convolutions.2.batch_normalization.running_mean”, “encoder.convolutions.2.batch_normalization.running_var”, “decoder.prenet.linear_layers.0.linear_layer.weight”, “decoder.prenet.linear_layers.1.linear_layer.weight”, “decoder.attention.location_layer.location_conv1d.weight”, “postnet.convolutions.0.convolution1d.weight”, “postnet.convolutions.0.convolution1d.bias”, “postnet.convolutions.0.batch_normalization.weight”, “postnet.convolutions.0.batch_normalization.bias”, “postnet.convolutions.0.batch_normalization.running_mean”, “postnet.convolutions.0.batch_normalization.running_var”, “postnet.convolutions.1.convolution1d.weight”, “postnet.convolutions.1.convolution1d.bias”, “postnet.convolutions.1.batch_normalization.weight”, “postnet.convolutions.1.batch_normalization.bias”, “postnet.convolutions.1.batch_normalization.running_mean”, “postnet.convolutions.1.batch_normalization.running_var”, “postnet.convolutions.2.convolution1d.weight”, “postnet.convolutions.2.convolution1d.bias”, “postnet.convolutions.2.batch_normalization.weight”, “postnet.convolutions.2.batch_normalization.bias”, “postnet.convolutions.2.batch_normalization.running_mean”, “postnet.convolutions.2.batch_normalization.running_var”, “postnet.convolutions.3.convolution1d.weight”, “postnet.convolutions.3.convolution1d.bias”, “postnet.convolutions.3.batch_normalization.weight”, “postnet.convolutions.3.batch_normalization.bias”, “postnet.convolutions.3.batch_normalization.running_mean”, “postnet.convolutions.3.batch_normalization.running_var”, “postnet.convolutions.4.convolution1d.weight”, “postnet.convolutions.4.convolution1d.bias”, “postnet.convolutions.4.batch_normalization.weight”, “postnet.convolutions.4.batch_normalization.bias”, “postnet.convolutions.4.batch_normalization.running_mean”, “postnet.convolutions.4.batch_normalization.running_var”.
Unexpected key(s) in state_dict: “encoder.convolutions.0.net.0.weight”, “encoder.convolutions.0.net.0.bias”, “encoder.convolutions.0.net.1.weight”, “encoder.convolutions.0.net.1.bias”, “encoder.convolutions.0.net.1.running_mean”, “encoder.convolutions.0.net.1.running_var”, “encoder.convolutions.0.net.1.num_batches_tracked”, “encoder.convolutions.1.net.0.weight”, “encoder.convolutions.1.net.0.bias”, “encoder.convolutions.1.net.1.weight”, “encoder.convolutions.1.net.1.bias”, “encoder.convolutions.1.net.1.running_mean”, “encoder.convolutions.1.net.1.running_var”, “encoder.convolutions.1.net.1.num_batches_tracked”, “encoder.convolutions.2.net.0.weight”, “encoder.convolutions.2.net.0.bias”, “encoder.convolutions.2.net.1.weight”, “encoder.convolutions.2.net.1.bias”, “encoder.convolutions.2.net.1.running_mean”, “encoder.convolutions.2.net.1.running_var”, “encoder.convolutions.2.net.1.num_batches_tracked”, “decoder.prenet.layers.0.linear_layer.weight”, “decoder.prenet.layers.0.bn.weight”, “decoder.prenet.layers.0.bn.bias”, “decoder.prenet.layers.0.bn.running_mean”, “decoder.prenet.layers.0.bn.running_var”, “decoder.prenet.layers.0.bn.num_batches_tracked”, “decoder.prenet.layers.1.linear_layer.weight”, “decoder.prenet.layers.1.bn.weight”, “decoder.prenet.layers.1.bn.bias”, “decoder.prenet.layers.1.bn.running_mean”, “decoder.prenet.layers.1.bn.running_var”, “decoder.prenet.layers.1.bn.num_batches_tracked”, “decoder.attention.location_layer.location_conv.weight”, “postnet.convolutions.0.net.0.weight”, “postnet.convolutions.0.net.0.bias”, “postnet.convolutions.0.net.1.weight”, “postnet.convolutions.0.net.1.bias”, “postnet.convolutions.0.net.1.running_mean”, “postnet.convolutions.0.net.1.running_var”, “postnet.convolutions.0.net.1.num_batches_tracked”, “postnet.convolutions.1.net.0.weight”, “postnet.convolutions.1.net.0.bias”, “postnet.convolutions.1.net.1.weight”, “postnet.convolutions.1.net.1.bias”, “postnet.convolutions.1.net.1.running_mean”, “postnet.convolutions.1.net.1.running_var”, “postnet.convolutions.1.net.1.num_batches_tracked”, “postnet.convolutions.2.net.0.weight”, “postnet.convolutions.2.net.0.bias”, “postnet.convolutions.2.net.1.weight”, “postnet.convolutions.2.net.1.bias”, “postnet.convolutions.2.net.1.running_mean”, “postnet.convolutions.2.net.1.running_var”, “postnet.convolutions.2.net.1.num_batches_tracked”, “postnet.convolutions.3.net.0.weight”, “postnet.convolutions.3.net.0.bias”, “postnet.convolutions.3.net.1.weight”, “postnet.convolutions.3.net.1.bias”, “postnet.convolutions.3.net.1.running_mean”, “postnet.convolutions.3.net.1.running_var”, “postnet.convolutions.3.net.1.num_batches_tracked”, “postnet.convolutions.4.net.0.weight”, “postnet.convolutions.4.net.0.bias”, “postnet.convolutions.4.net.1.weight”, “postnet.convolutions.4.net.1.bias”, “postnet.convolutions.4.net.1.running_mean”, “postnet.convolutions.4.net.1.running_var”, “postnet.convolutions.4.net.1.num_batches_tracked”.
size mismatch for embedding.weight: copying a param with shape torch.Size([129, 512]) from checkpoint, the shape in current model is torch.Size([181, 512]).

#2

I see you’ve posted multiple times in quick succession but there seems to be no update on each of the earlier posts.

If anyone is going to help you it might be useful for you to fill people in on what has happened with the earlier ones and generally for something like this maybe try to keep it to one thread given that it seems like a continued effort.

That’s just a well intentioned suggestion :slightly_smiling_face: I hope you get the issues resolved!

1 Like
#3

I’m guessing you are using the latest version from the master branch? It won’t work with the previously released model.
See here: https://github.com/mozilla/TTS/issues/423

You will have to use the branch the model was released under.

#4

OK thank you sanjaesc, understood.