# wav2vec2 Fine-tuning

A deepfake audio detection model fine-tuned based on wav2vec2-base, achieving 99.66% accuracy

A noise recognition model fine-tuned from facebook/wav2vec2-base with 93.89% accuracy

An audio classification model fine-tuned on the minds14 dataset based on facebook/wav2vec2-base

A fine-tuned English phoneme recognition model based on facebook/wav2vec2-large-lv60 on the TIMIT dataset, achieving a phoneme error rate of 10.53%

This model uses a fine-tuned wav2vec2 (base) architecture, trained on the IEMOCAP dataset for speech emotion recognition tasks.

A speech emotion analysis model fine-tuned based on facebook/wav2vec2-base, achieving 70.87% accuracy on the evaluation set

A fine-tuned Arabic speech emotion recognition model based on facebook/wav2vec2-base, achieving 99.92% accuracy on the evaluation dataset.

A speech emotion recognition model fine-tuned on the RAVDESS dataset based on the wav2vec2-large-xlsr-53-english model

An audio classification model fine-tuned based on facebook/wav2vec2-base, achieving 58.92% accuracy on the evaluation set

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base, achieving 73.98% accuracy on the evaluation set

Audio classification model based on wav2vec2 architecture, fine-tuned on the GTZAN dataset for music and speech classification tasks

This model is an audio classification model fine-tuned on the GTZAN dataset based on facebook/wav2vec2-base, primarily used for music genre classification tasks.

This is a speech emotion recognition model based on the facebook/wav2vec2-base model fine-tuned on the IEMOCAP dataset, achieving 73.9% accuracy on the evaluation set.

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving 73.22% accuracy on the evaluation set

An audio classification model fine-tuned based on facebook/wav2vec2-base for identifying bird sounds

This model is a fine-tuned version of minoosh/wav2vec2-base-finetuned-ie on the shEMO dataset, primarily used for speech emotion recognition tasks.

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, used to predict the speaker's emotions in audio samples.

This model is a fine-tuned audio classification model based on facebook/wav2vec2-base on the superb dataset, achieving an accuracy of 0.6578 on the evaluation set.

A speech recognition model fine-tuned on the superb dataset based on facebook/wav2vec2-base, achieving 98.34% accuracy

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 83.67% on the evaluation set

Fine-tuned English speech recognition model based on facebook/wav2vec2-xls-r-300m

This model is a speech recognition model fine-tuned on the SUPERB dataset based on facebook/wav2vec2-base, demonstrating excellent performance in keyword spotting tasks.

A German speech emotion detection model based on wav2vec2, trained on the emo-DB dataset, capable of recognizing 7 different emotions.

A speech recognition model fine-tuned on the superb dataset based on wav2vec2-base, achieving 98.15% accuracy

This model is a speech intent classification model fine-tuned from facebook/wav2vec2-base, achieving an F1 score of 0.875 on the evaluation set.

Fine-tuned intent classification model based on facebook/wav2vec2-large-960h, achieving 77.08% accuracy on the evaluation set

This model is a fine-tuned version of facebook/wav2vec2-base on the intent-dataset for intent classification tasks.

A speech recognition model fine-tuned on the minds14 dataset based on facebook/wav2vec2-base

A fine-tuned Urdu audio emotion classification model based on facebook/wav2vec2-large-xlsr-53, supporting recognition of four emotions: anger, happiness, calmness, and sadness.

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, featuring a low Word Error Rate (WER).

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, specializing in English speech-to-text tasks

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving a word error rate of 0.3417 on the evaluation set.

A speech recognition model fine-tuned based on facebook/wav2vec2-base, suitable for the TIMIT dataset

A fine-tuned speech recognition model based on facebook/wav2vec2-base, with a Word Error Rate (WER) of 0.283, and 0.126 when using 3-gram

This model is a fine-tuned version of RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab based on the li_singlish dataset, primarily used for Singapore English speech recognition tasks.

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, focusing on English speech-to-text tasks.

A speech recognition model fine-tuned on Filipino speech datasets based on facebook/wav2vec2-xls-r-300m

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, primarily used for English speech-to-text tasks.

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained for 30 epochs on the TIMIT dataset.