# Wav2Vec2 Fine-tuning

A voice tone classifier specifically designed for corporate disclosure scenarios, based on a two-phase training of the Wav2Vec2 architecture

Audio classification model based on Wav2Vec2 for music genre recognition

A speech emotion recognition system fine-tuned on Wav2Vec2 Large XLSR-53 model, capable of identifying 7 common emotions

This model is used to recognize six basic emotions from English audio: anger, disgust, fear, happiness, sadness, and surprise, trained on the RAVDESS dataset.

This model, fine-tuned from Wav2Vec2-Large-Robust, can predict the speaker's age and gender from raw audio.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-300m, primarily used for English speech-to-text tasks.

An air traffic control speech recognition model fine-tuned on wav2vec2-xls-r-300m, supporting English

This model is a speech recognition model fine-tuned on the Singapore English (uob_singlish) dataset based on wav2vec2-xls-r-300m-mixed

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving a word error rate (WER) of 0.3355 on the evaluation set.

This is an automatic phoneme recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-xls-r-300m model, primarily used for phoneme-level recognition of English speech.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.4348 on the TIMIT dataset.

This model is a fine-tuned speech recognition model based on Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 on an unknown dataset, supporting Hindi and achieving a word error rate of 10.08% on the evaluation set.

This model is fine-tuned from Wav2Vec2-Large-Robust for speech emotion recognition, predicting values in three dimensions: arousal, dominance, and valence.

Indian English speech recognition model fine-tuned based on Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700

An automatic speech recognition (ASR) model fine-tuned from facebook/wav2vec2-base, achieving a word error rate of 0.3423 on the evaluation set

This is an automatic speech recognition (ASR) model fine-tuned on the Turkish Common Voice dataset based on Facebook's wav2vec2-large-xlsr-53 model.

This model is a fine-tuned English Automatic Speech Recognition (ASR) model based on facebook/wav2vec2-base, achieving a word error rate of 0.3397 on the evaluation set.

A speech recognition model fine-tuned on the timit_asr dataset based on facebook/wav2vec2-base, supporting 16kHz sampled audio input

A fine-tuned Egyptian Arabic automatic speech recognition model based on facebook/wav2vec2-large-xlsr-53, trained using the arabicspeech.org MGB-3 dataset.

This is a Wav2vec 2.0 speech recognition model optimized for Brazilian Portuguese, fine-tuned on multiple Brazilian Portuguese datasets

A Hausa speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m

Wav2vec 2.0 model fine-tuned for Brazilian Portuguese speech recognition based on Common Voice 7.0 dataset

Wav2vec2 model fine-tuned for Brazilian Portuguese using the CETUC dataset, trained with approximately 145 hours of Brazilian Portuguese speech data

HIYACCENT is a speech recognition system optimized for Nigerian English accents, built upon an enhanced Wav2Vec2 architecture with over 20% performance improvement.

This is a Wav2vec 2.0 model fine-tuned for Brazilian Portuguese, trained using the Sidney dataset, suitable for automatic speech recognition tasks in Brazilian Portuguese.

This is a Wav2vec 2.0 model fine-tuned for Brazilian Portuguese, trained on the Common Voice 7.0 dataset, supporting Portuguese speech recognition tasks.

This model is a fine-tuned German speech recognition model based on flozi00/wav2vec-xlsr-german, primarily used for German speech-to-text tasks.

This is an automatic speech recognition (ASR) model fine-tuned on the Arabic Common Voice 7 dataset based on the facebook/wav2vec2-xls-r-300m model.