Comments (6)
Line 50 in d149bc4
Make sure this line is set to 107 or higher in whichever json file your using as your config file.
If you are able to, setting the CUDA_LAUNCH_BLOCKING=1
environment variable might give you a more accurate error message. The error message you sent doesn't contain the line the error occured (since the error happened asynchronously on the GPU)
from flowtron.
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Speed should be the same for single speaker and multi speaker datasets. You can change n_speakers to 128 and n_text to 256 to slightly improve performance, but it will be a very small improvement.
from flowtron.
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Related Issues (20)
- Inference starting repeat itself. HOT 5
- List index out of range
- Request for clarification on some of the readme scripts. HOT 8
- Custom model resumed from pre-trained model has a stuttering problem.
- How would one keep the model loaded for immediate synthesis? HOT 17
- Inference on pre-trained model (flowtron_ljs) speaking nonsense. HOT 4
- Inference Demo "Hitting gate limit" HOT 2
- .
- inference speed on CPU
- Accelerated inference with TensorRT HOT 2
- Single word input leads to ValueError: Expected more than 1 spatial element when training, got input size torch.Size([1, 512, 1]) HOT 1
- Error on loading training model "_pickle.UnpicklingError: invalid load key, '<'"
- Custom trained model and dataset problem
- Index out of range for custom dataset.
- value error while training custom dataset
- TypeError: guvectorize() missing 1 required positional argument 'signature' HOT 1
- _pickle.UnpicklingError: invalid load key, '<'. in inference.py in colab HOT 3
- What's the filelist used to train LibriTTS2k pretrained embedding?
- Which torch version to use?
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