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This is a Malayalam speech recognition model based on the Wav2Vec2 architecture, designed to convert Malayalam speech into text.

This is a Tamil speech recognition model based on the Wav2Vec2 architecture, capable of converting Tamil speech into text.

A Portuguese speech recognition model fine-tuned based on Facebook's wav2vec2-large-xlsr-53 model, trained on the Common Voice dataset

A Nepali speech recognition model based on the Wav2Vec2 architecture, capable of directly converting Nepali speech into text

An automatic speech recognition model fine-tuned on Hebrew speech data based on facebook/wav2vec2-large-xlsr-53

An automatic speech recognition model fine-tuned on English based on facebook/wav2vec2-large, trained using the Common Voice 6.1 dataset

A Nepali automatic speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, trained on OpenSLR and Common Voice datasets, achieving a test WER of 5.97%.

This is an Icelandic automatic speech recognition (ASR) model fine-tuned based on Facebook's wav2vec2-large-xlsr-53 model, trained using the Malromur dataset.

An Arabic speech recognition model fine-tuned on the Arabic speech corpus dataset, based on the facebook/wav2vec2-large-xlsr-53 model

This is an automatic speech recognition model for Portuguese, fine-tuned using the Common Voice dataset and based on the facebook/wav2vec2-large-100k-voxpopuli model.

A Marathi automatic speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, trained on OpenSLR Marathi dataset with a test set word error rate of 12.7%.

A Portuguese automatic speech recognition model fine-tuned based on Facebook's wav2vec2-large-xlsr-53 architecture, trained on the Common Voice dataset with a word error rate of 13.77%.

This is a Punjabi speech recognition model based on the Wav2Vec2 architecture, capable of converting Punjabi speech into text.

This model is based on Facebook's VoxPopuli-sv large model, additionally pre-trained and fine-tuned using Swedish radio programs, NST, and Common Voice data.