Comments (1)
jaywalnut310
commented on September 28, 2024
10000 epochs is meaningless. You can reduce number of epochs, or just cancel during training.
I trained my model with the base config on 2 V100 gpus, and it took ~ 3.3 days.
I think using large batch size might cause the slow training problem. The monotonic alignment search always operates on CPU cores, which means if the number of CPU cores does not increase with the number of gpus proportionally, CPU could take much time to search alignments for the 4 times larger batch than base setting.
And according to the 4x increased batch size, I think it would make sense to quit training at less than 24k steps.
from glow-tts.
Related Issues (20)
- Runtime Error: Multi speaker HOT 1
- GPU required or CPU-compatible? HOT 1
- Different Languages us different amount of GPU memory
- multi speaker
- Output compared to Fastspeech2
- Models for finetuning
- Could not create monotonic_align HOT 3
- Glowtts melspectrogram to fine tune hifigan HOT 2
- RuntimeError: CUDA error: invalid device function
- ImportError: /glow-tts/monotonic_align/monotonic_align/core.cpython-38-x86_64-linux-gnu.so: failed to map segment from shared object HOT 1
- Error using mel generated from glow-tts for hifi-gan training HOT 1
- Can I apply MAS method to other model ? HOT 1
- Query : How is the Model training different from the Model training of wave glow
- Multi speaker training error HOT 11
- With out Training DDI
- An explanation for the source code of finding the alignment path in GlowTTS? HOT 2
- DDI training compared to not DDI training HOT 1
- [Question] How many iterations for the available pretrained model?
- [Question] about `intersperse` function. HOT 2
- [CONTRIBUTION] Speech Dataset Generator
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from glow-tts.