Comments (1)
Hi, could you show the attention score? The generated attention does not matter, what's used for duration extraction is the ground truth aligned one. 900 samples is quite few for generating attention with tacotron - what language are the samples in and are you using phonemes? For a small dataset like this one could try to pretrain a tacotron model on a different dataset until attention is built up and then continue training on the smaller dataset. Also, it could make sense to set the trim_long_silences=True and vad_max_silence_length=6 or so for shorter silent parts in the audios, which helps attention to build up.
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Related Issues (20)
- symbols.py for Arabic letters
- Feature request: model compatible to export into onnx
- implement hifigan vocoder?
- Adding pauses to the input text HOT 2
- confuse about duration extract HOT 10
- preprocess.py issues - RAM usage close to 100% but CPU usage is nonexistant HOT 16
- ValueError not enough values to unpack (expected 2 got 0) HOT 2
- making the system available for use with assistive technologies on windows HOT 1
- ValueError: need at least one array to stack train_tacotron.py line 192 HOT 1
- Facing problem at preprocessing
- Need instructions for fine tunning
- Problems with attention for dataset consisting of longer samples
- how to train a dataset using a pre-trained model?
- preprocess.py misuses Espeak backend, resulting in slow performance and memory leak HOT 2
- preprocess.py: list index out of range HOT 5
- Multispeaker and new neural voice creation HOT 12
- Non-Latin alphabets
- Bad Attention!
- Training a model twice using a different dataset
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