Comments (2)
Closing issue since the solution is provided.
from wavegrad.
Hello, @RahulBhalley . Thank you for your feedback. You already have an answer on your question! :) It is hidden in cell code that you provided:
# model.load_state_dict(torch.load('../logs/default/checkpoint_180630.pt)['model'], strict=False)
model, _, _ = utils.load_latest_checkpoint(config.training_config.logdir, model)
Use the commented line to load the weights (set the path to the downloaded checkpoint and that's it). The second line searches for the latest checkpoint by iteration ID from training. You don't need it.
from wavegrad.
Related Issues (20)
- ValueError: low >= high HOT 2
- inference.py seems not loading the specified checkpoint HOT 1
- Exponents calculation in positional encoding HOT 9
- Were your `generated_samples` generated using a model trained with AMP? HOT 2
- predict_start_from_noise HOT 2
- best noise schedule HOT 1
- schedules model for other dataset and different sample rate HOT 4
- TTS without Text? HOT 2
- Training so slow HOT 3
- How to make it work with TPU? HOT 2
- Audio quality improvements HOT 6
- The order of upsampling_dilations HOT 1
- Interpolation and Conv order in Upsample module HOT 1
- slow training in single GPU HOT 1
- Using NVIDIA RTX 3090 GPU?
- Static Noise with f_max = 10000
- Poor Synthesis Quality on 44k Sample Rate HOT 1
- Evaluation tools
- Matplotlib API change & NaNs for short clips & new hop_length HOT 27
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from wavegrad.