# XLSR-53 Fine-tuning

Japanese automatic speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, trained using Common Voice 7.0 Japanese dataset

This model is an automatic speech recognition model based on wav2vec2-large-xlsr-53, fine-tuned on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset, primarily used for singing voice recognition.

This model is an automatic speech recognition (ASR) model fine-tuned on the COMMON_VOICE - TR Turkish dataset based on facebook/wav2vec2-large-xlsr-53, achieving a word error rate of 0.3242 on the evaluation set.

Japanese speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampling rate audio input

A Luganda automatic speech recognition system fine-tuned from Facebook's wav2vec2-large-xlsr-53 model, achieving 7.53% WER on the Common Voice Luganda dataset.

A Wav2Vec2-Large-XLSR-53 model fine-tuned for Arabic speech recognition, trained on the Common Voice and Arabic Speech Corpus datasets

This is an automatic speech recognition (ASR) model fine-tuned on the COMMON_VOICE - TR Turkish dataset based on the facebook/wav2vec2-large-xlsr-53 model.

Gujarati automatic speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, achieving 23.55% WER on OpenSLR dataset

A Chinese speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampling rate audio input.

A Bengali automatic speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, trained with 40,000 speech samples from the OpenSLR dataset

This model is a Hausa speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-large-xlsr-53