Comments (1)
This is likely because of the training data we used. During training, we skipped samples that are less than one second (less than 80 frames) because of the pooling layers in the style encoder (i.e., if the input is less than one second, the second last feature map will be too small to go through the last residual layer). The model has never been trained to synthesize speech less than one second, so it may not be able to generate natural speech under that duration.
You need to either retrain the model with padded or repeated input for the style encoder, or you can add some phonemes during inference to make the sentence you want to synthesize longer and trim the synthesized results based on the predicted duration to the part you want.
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Related Issues (20)
- About train on Vietnamese Dataset HOT 1
- 多卡训练的问题 HOT 5
- batch size and number of epochs for large dataset HOT 1
- style encoder inconsistency HOT 1
- First stage alignment training failed when TMA_CEloss=True HOT 2
- what is the mean=-4 and std=4 meannig? HOT 1
- Training the model HOT 1
- Training Model with new Dataset
- The pronunciations of single words or short words is poor? HOT 1
- Question: Fine tuning LibriTTS with StyleTTS HOT 5
- Inference exact time for each word HOT 5
- train on ESD HOT 1
- Pre-training model sound quality issues HOT 5
- question HOT 2
- StyleTTS 2 HOT 1
- S2S HOT 1
- Has anyone had this problem when converting to onnx?
- Is the uv detector trained in the pretrained pitch detector?
- Marathi Support ?
- Voice Quality issue using Librispeech
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from styletts.