# High accuracy transcription

This is a large-scale Uzbek speech recognition model based on the FastConformer architecture, supporting both Transducer and CTC decoding, and demonstrating excellent performance across multiple test sets.

Nepali speech recognition model fine-tuned based on Facebook's wav2vec2 model

This model is an automatic speech recognition (ASR) model fine-tuned on Urdu speech datasets based on facebook/wav2vec2-xls-r-300m

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

This model is an automatic speech recognition model fine-tuned on the Galician dataset based on facebook/wav2vec2-xls-r-300m, achieving a WER of 11.31% on the Common Voice 8.0 test set.

This is an automatic speech recognition model fine-tuned on the Romansh-Vallader dataset based on the facebook/wav2vec2-xls-r-300m model.

This is a Hindi automatic speech recognition (ASR) model based on the wav2vec2 architecture, trained on datasets like Common Voice.