QuartzNet checkpoint (PyTorch, AMP, LibriSpeech)

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

• Original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

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.