Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Wav2Vec2 model was trained using connectionist temporal classification (CTC) so …

Jun 20, 2020 · Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 …

Jul 23, 2025 · Self-supervised learning, exemplified by models like Wav2Vec2, offers a robust approach for representation learning in domains with limited labeled data. Fine-tuning on specific tasks further …

Sep 24, 2020 · Our new model, wav2vec 2.0, uses self-supervision to push the boundaries by learning from unlabeled training data to enable speech recognition systems for many more languages, …

🧠 A full PyTorch implementation of wav2vec2.0, designed to learn powerful audio representations from raw waveform input using contrastive learning — built completely from scratch.

First, we will create a Wav2Vec2 model that performs the feature extraction and the classification. There are two types of Wav2Vec2 pre-trained weights available in torchaudio.

When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of …

3 days ago · Tl;dr: This post explains the right way to use the specificities of the Connectionist Temporal Classification (CTC) architecture with a purpose to achieve superb quality automatic speech …

To pre-train the model, Wav2Vec2 masks certain portions of time steps in the feature encoder which is similar to masked language model. The aim is to teach the model to predict the correct quantized …

Jun 20, 2020 · Wav2vec was created by Facebook AI Research in 2021 and published in this paper: wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. The official …