QuartzNet PyTorch checkpoint trained on LibriSpeech (test-other 10.41% WER)
Model Overview
End-to-end neural acoustic model for automatic speech recognition providing high accuracy at a low memory footprint.
Model Architecture
QuartzNet is an end-to-end neural acoustic model that is based on efficient, time-channel separable convolutions (Figure 1). In the audio processing stage, each frame is transformed into mel-scale spectrogram features, which the acoustic model takes as input and outputs a probability distribution over the vocabulary for each frame.
Figure 1. Architecture of QuartzNet (source)
Training
This model was trained using script available on NGC and in GitHub repo.
Dataset
The following datasets were used to train this model:
- LibriSpeech - Corpus of approximately 1000 hours of 16kHz read English speech derived from audiobooks from the LibriVox project, carefully segmented and aligned.
Performance
Performance numbers for this model are available in NGC.
References
License
This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.