Comments (5)
Should I reduce batch_size? But Jasper use BN dense.
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What is the maximum duration of audiofiles in your train set? Make a histogram out of the durations and see if you have any outliers (very long audiofiles). Try to remove all audiofiles longer than X seconds (start X with some large value, and then lower it until you stop getting CUDA out of memory exceptions).
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Like @RobertInjac mentioned above, GPU memory usage depends a lot on the max length of the audio during training. For public datasets such as LibriSpeech and Mozilla Common Voice, we cap it at 16.7 seconds during training. So one option is to cut your audio files into smaller pieces (but don't go too small - you still want several words per audio sample).
Another option is to reduce the batch size per GPU. Note that you can still simulate a larger batch size by setting batches_per_step parameter to more than 1 (see https://nvidia.github.io/NeMo/api-docs/nemo.html#module-nemo.core.neural_factory). This may also help with GPU utilization during multi-GPU/multi-node training
from nemo.
What is the maximum duration of audiofiles in your train set? Make a histogram out of the durations and see if you have any outliers (very long audiofiles). Try to remove all audiofiles longer than X seconds (start X with some large value, and then lower it until you stop getting CUDA out of memory exceptions).
Thank you. The max length of the audio in my dataset in only 15S.
from nemo.
Like @RobertInjac mentioned above, GPU memory usage depends a lot on the max length of the audio during training. For public datasets such as LibriSpeech and Mozilla Common Voice, we cap it at 16.7 seconds during training. So one option is to cut your audio files into smaller pieces (but don't go too small - you still want several words per audio sample).
Another option is to reduce the batch size per GPU. Note that you can still simulate a larger batch size by setting batches_per_step parameter to more than 1 (see https://nvidia.github.io/NeMo/api-docs/nemo.html#module-nemo.core.neural_factory). This may also help with GPU utilization during multi-GPU/multi-node training
Thank you. I solve the problem by opening apex O1 and reducing my batch_size to 14. And I will try to set batches_per_step to 2.
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Related Issues (20)
- Issue with pip installing on windows due to youtokentome dependency HOT 1
- Tokenizer suggestion for fine tuning cache aware streaming model HOT 2
- Titanet-L Augmentation HOT 4
- Not found file "convert_mistral_hf_to_nemo.py" in /opt/NeMo/scripts/checkpoint_converters/ for Convert Mistral
- Precision Problem between nemo model and hugging face model HOT 2
- Llama2 70B SFT with FSDP failing HOT 2
- training config used for training stt_en_quartznet15x5 HOT 2
- llama2 training hangs when pp_size > 1
- Integration of Turn-Taking Models into Nemo Framework for Enhanced Realistic Conversations
- FileNotFoundError: Model stt_fa_fastconformer_hybrid_large was not found. HOT 6
- [Feature] Add Support on Multiple Metrics Reporting during Training Progress for Validation
- checkpoints not saved due to wrong loss comparison?
- when "write_predictions_to_file" is true,generate will fail。 HOT 1
- "RuntimeError: start (4) + length (1) exceeds dimension size (4)." when running cache aware streaming inference
- slow validation process HOT 2
- Optimizing Learning Rate Parameters in Model Fine-tuning
- AUDIO FILE SIZE for fine tuning STT En FastConformer Hybrid Transducer-CTC Large Streaming Multi HOT 1
- `EncDecCTCModel.transcribe(audio=...)` changed to `EncDecCTCModel.transcribe(paths2audio_files=...)` HOT 5
- Enormous number of `.nemo` checkpoints produced in training HOT 4
- [Conversion] How to convert Finetuned T5 checkpoint ended with `.ckpt` to `.nemo` checkpoint with NeMo toolkit?
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