Comments (11)
dathudeptrai
commented on May 15, 2024
1
@linhld0811 cay you try
pretrained_config.vocab_size = NEW_VOCAB_SIZE
tacotron2 = TFTacotron2(pretrained_config, training=True, name='tacotron2')
tacotron2._build()
tacotron2.summary()
tacotron2.load_weights(path, by_name=True, skip_mismatch=True)
....
btw, what is ur tensorflow version ?
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dathudeptrai
commented on May 15, 2024
1
@linhld0811 no i mean this code, i just test and it worked.
pretrained_config = Tacotron2Config(**config["tacotron2_params"])
pretrained_config.vocab_size = NEW_VOCAB_SIZE
tacotron2 = TFTacotron2(config=pretrained_config, training=True, name='tacotron2')
tacotron2._build()
tacotron2.summary()
tacotron2.load_weights("./examples/tacotron2/exp/train.tacotron2.v1/checkpoints/model-120000.h5", by_name=True, skip_mismatch=True)
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dathudeptrai
commented on May 15, 2024
1
@sujeendran no, the code should be like this, do not need re build and redefine embedding layer
pretrained_config = Tacotron2Config(**config["tacotron2_params"])
pretrained_config.vocab_size = NEW_VOCAB_SIZE
tacotron2 = TFTacotron2(config=pretrained_config, training=True, name='tacotron2')
tacotron2._build()
tacotron2.summary()
tacotron2.load_weights("./examples/tacotron2/exp/train.tacotron2.v1/checkpoints/model-120000.h5", by_name=True, skip_mismatch=True)
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linhld0811
commented on May 15, 2024
Here my code:
tacotron2 = TFTacotron2(config=Tacotron2Config(**config["tacotron2_params"]), training=True, name='tacotron2')
tacotron2._build()
#tacotron2.summary()
if args.pretrained_model != None:
print("use pretrained_model ljspeech", args.pretrained_model)
tacotron2.load_weights(args.pretrained_model)
pretrained_config = Tacotron2Config(**config["tacotron2_params"])
pretrained_config.set_params(len(symbols))
print("vocab_size of new model:", pretrained_config.vocab_size)
new_embedding_layers = TFTacotronEmbeddings(pretrained_config, name='embeddings')
tacotron2.encoder.embeddings = new_embedding_layers
tacotron2._build()
tacotron2.summary()
I use TF version 2.2.0
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linhld0811
commented on May 15, 2024
Thanks, i fixed this error. I loaded model but did not use the param skip_mismatch=True of the function tacotron2.load_weights
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dathudeptrai
commented on May 15, 2024
@sujeendran Fixed.
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linhld0811
commented on May 15, 2024
I have a question, can you help me to figured out:
- Can i use your pretrained tacotron2 model to extract duration of my own dataset(not english) to teach fastspeech model?! Or i have to training tacotron2 model on my dataset and use best_model to extract duration?!
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dathudeptrai
commented on May 15, 2024
You cannot do that :))
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sujeendran
commented on May 15, 2024
@dathudeptrai I tried the same. Didn't work. I'm also running Tensorflow 2.2.0. To be clear, here is the code after your fix:
pretrained_config = Tacotron2Config(**config["tacotron2_params"])
tacotron2 = TFTacotron2(pretrained_config, training=True, name='tacotron2')
tacotron2._build()
tacotron2.summary()
tacotron2.load_weights("./examples/tacotron2/exp/train.tacotron2.v1/checkpoints/model-1400.h5", by_name=True, skip_mismatch=True)
pretrained_config.vocab_size = len(symbols)
new_embedding_layers = TFTacotronEmbeddings(pretrained_config, name='embeddings')
tacotron2.encoder.embeddings = new_embedding_layers
# re-build model
tacotron2._build()
tacotron2.summary()
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sujeendran
commented on May 15, 2024
@dathudeptrai Oh, in that case my fix was doing the same. Thanks! :) Maybe you can update the documentation for fine-tuning now that this is clear so others don't fall into this issue again.
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dathudeptrai
commented on May 15, 2024
Yeah i will update it asap :))) maybe add some flags :3
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Related Issues (20)
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