107-speech-recognition-quantization

Quantize Speech Recognition Models with OpenVINO™ Post-Training Optimization Tool

This tutorial demonstrates how to apply INT8 quantization to the speech recognition models, using the Post-Training Optimization Tool API (POT API) (part of OpenVINO Toolkit).

Supported models:

• 107-speech-recognition-wav2vec2.ipynb demonstrates how to apply post-training INT8 quantization on a fine-tuned Wav2Vec2-Base-960h PyTorch model, trained on the LibriSpeech ASR corpus.
• 107-speech-recognition-data2vec.ipynb demonstrates how to apply post-training INT8 quantization on a fine-tuned Data2Vec-Audio-Base-960h PyTorch model, trained on the LibriSpeech ASR corpus.

The code of the tutorials is designed to be extendable to custom models and datasets.

Notebook Contents

The tutorial consists of the following steps:

• Downloading and preparing the model and dataset.
• Defining data loading and accuracy validation functionality.
• Preparing the model for quantization.
• Running optimization pipeline.
• Comparing performance of the original and quantized models.
• Compare accuracy of the original and quantized models.

Installation Instructions

If you have not installed all required dependencies, follow the Installation Guide.