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quartznet-15x5-en

Use Case and High-Level Description

QuartzNet model performs automatic speech recognition. QuartzNet’s design is based on the Jasper architecture, which is a convolutional model trained with Connectionist Temporal Classification (CTC) loss. This particular model has 15 Jasper blocks each repeated 5 times. The model was trained in NeMo on multiple datasets: LibriSpeech, Mozilla Common Voice, WSJ, Fisher, Switchboard, and NSC Singapore English. For details see repository, paper.

Specification

Metric

Value

Type

Speech recognition

GFLOPs

2.4195

MParams

18.8857

Source framework

PyTorch*

Accuracy

Metric

Value

WER @ Librispeech test-clean

3.86%

Input

Original model

Normalized Mel-Spectrogram of 16kHz audio signal, name - audio_signal, shape - 1, 64, 128, format is B, N, C, where:

  • B - batch size

  • N - number of mel-spectrogram frequency bins

  • C - duration

Converted model

The converted model has the same parameters as the original model.

Output

Original model

Per-frame probabilities (after LogSoftmax) for every symbol in the alphabet, name - output, shape - 1, 64, 29, output data format is B, N, C, where:

  • B - batch size

  • N - number of audio frames

  • C - alphabet size, including the CTC blank symbol

The per-frame probabilities are to be decoded with a CTC decoder. The alphabet is: 0 = space, 1…26 = “a” to “z”, 27 = apostrophe, 28 = CTC blank symbol. Example is provided <omz_dir>/demos/speech_recognition_deepspeech_demo/python/default_alphabet_example.conf.

Converted model

The converted model has the same parameters as the original model.

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
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