wav2vec2 large xlsr 53 ukrainian

Introduction

WAV2VEC2-LARGE-XLSR-53-UKRAINIAN is a fine-tuned model for automatic speech recognition, specifically for the Ukrainian language. It is built upon the facebook/wav2vec2-large-xlsr-53 model and trained using the Common Voice dataset. The model processes speech input sampled at 16kHz.

Architecture

This model utilizes the Wav2Vec2 architecture, which is designed for automatic speech recognition tasks. The Wav2Vec2 model is known for its capability to handle various languages and accents by employing a large-scale speech representation learning framework.

Training

The training process employed the Common Voice train and validation datasets to fine-tune the base Wav2Vec2 model. The model leverages a 16kHz resampling rate, and its performance is evaluated using the Word Error Rate (WER) metric, achieving a test WER of 32.29%.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Necessary Packages: Ensure that torch, torchaudio, datasets, and transformers are installed.
2. Load the Dataset: Utilize the datasets library to load the Common Voice dataset.
3. Initialize Processor and Model: Load the Wav2Vec2Processor and Wav2Vec2ForCTC classes from the transformers library using the model's identifier.
4. Preprocess Audio: Resample audio files to a 16kHz sampling rate.
5. Run Inference: Use the model to predict the transcriptions from the audio inputs.
6. Evaluate Output: Compare predictions against the reference sentences to check accuracy.

For optimal performance, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure.

License

This model is distributed under the Apache 2.0 license, which permits usage, distribution, and modification under defined conditions.