Speed perturbation versus extended epochs for low-resource Kaldi nnet3 chain training under GPU memory constraints

Recommendation

For low-resource Kaldi nnet3 chain training, speed perturbation is the superior strategy for reducing Word Error Rate (WER). Increasing epochs on a small corpus typically leads to overfitting without the generalization gains provided by synthetic data augmentation.

Analysis of Trade-offs

WER and Generalization

Speed perturbation (typically factors of 0.9, 1.0, and 1.1) effectively triples the training data. This forces the model to learn a more robust representation of phonetic content across different speaking rates. In contrast, extended epochs simply expose the model to the same acoustic patterns repeatedly, which often leads to memorization of the training set rather than improved generalization.

GPU Memory and Throughput

It is important to distinguish between GPU memory (VRAM) and storage I/O:

• GPU Memory: VRAM usage in nnet3 is primarily determined by the model architecture and the batch size. The total size of the training corpus on disk does not increase the VRAM footprint per iteration.
• Throughput: Speed perturbation increases the volume of data to be read from disk. If your storage subsystem (e.g., slow HDD or network mount) cannot keep up with the GPU's consumption rate, you will see increased I/O wait times and a drop in overall training throughput.

Overfitting Thresholds

While there is no universal corpus size where overfitting begins, low-resource settings (e.g., <10 hours of audio) are highly susceptible. Overfitting typically manifests when the training loss continues to decline while the validation loss begins to plateau or rise. Speed perturbation delays this inflection point by increasing the diversity of the training distribution.

Implementation Steps

1. Pre-compute Perturbations: Generate the 0.9 and 1.1 speed versions of your training data using the Kaldi utilities.
2. Strict Data Splitting: Ensure that the original, fast, and slow versions of a single utterance stay within the same set (all in training or all in validation) to prevent data leakage.
3. Monitor I/O: Use iostat or vmstat during the first epoch to ensure the CPU/Disk is not bottlenecking the GPU.
4. Validation Tracking: Plot the validation loss curve. Stop training when validation loss stops improving, regardless of whether you used perturbation or extended epochs.

Missing Diagnostic: To refine this recommendation, please specify your current storage medium (e.g., NVMe SSD vs. NFS/HDD), as this determines if the I/O overhead of perturbation will significantly impact your training wall-clock time.