Speech Recognition QuartzNet Python* Demo¶

This demo demonstrates Automatic Speech Recognition (ASR) with pretrained QuartzNet model.

How It Works¶

After computing audio features, running a neural network to get character probabilities, and CTC greedy decoding, the demo prints the decoded text.

Preparing to Run¶

The list of models supported by the demo is in <omz_dir>/demos/speech_recognition_quartznet_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO IR format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

An example of using the Model Converter:

omz_converter --list models.lst

Supported Models¶

quartznet-15x5-en

NOTE: Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.

Running Demo¶

Run the application with -h option to see help message.

usage: speech_recognition_quartznet_demo.py [-h] -m MODEL -i INPUT [-d DEVICE]

optional arguments:
  -h, --help            Show this help message and exit.
  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model.
  -i INPUT, --input INPUT
                        Required. Path to an audio file in WAV PCM 16 kHz mono format
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on, for
                        example: CPU, GPU, HDDL, MYRIAD or HETERO. The
                        demo will look for a suitable OpenVINO Runtime plugin for this
                        device. Default value is CPU.

The typical command line is:

python3 speech_recognition_quartznet_demo.py -m quartznet-15x5-en.xml -i audio.wav

NOTE: Only 16-bit, 16 kHz, mono-channel WAVE audio files are supported.

An example audio file can be taken from OpenVINO test data folder.

Demo Output¶

The application prints the decoded text for the audio file. The demo reports

Latency: total processing time required to process input data (from reading the data to displaying the results).