# Fine-tuned wav2vec2

A Thai speech emotion recognition model fine-tuned from airesearch/wav2vec2-large-xlsr-53-th, supporting anger, happiness, and calm emotion classification

A Turkish gender classification model fine-tuned from facebook/wav2vec2-base, trained on the common_voice_17_0 dataset with a test set accuracy of 84.79%

A voice anti-spoofing detection model fine-tuned based on wav2vec2-base, achieving 88.89% accuracy on the evaluation set

An audio classification model fine-tuned from facebook/wav2vec2-base-960h for detecting violent sounds

A fine-tuned voice gender classification model based on facebook/wav2vec2-base, achieving 98.92% accuracy on the evaluation set

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base for analyzing emotional states in speech

A wav2vec2-based speech recognition model, fine-tuned at the phoneme level for Amharic

A binary audio classification model fine-tuned from facebook/wav2vec2-base, specifically designed to distinguish between the pronunciations of 'down' and 'on'

An audio classification model fine-tuned based on facebook/wav2vec2-base for distinguishing between music and speech

An audio classification model fine-tuned based on facebook/wav2vec2-base, trained on the superb dataset with 99.99% accuracy

A speech processing model fine-tuned based on facebook/wav2vec2-base, demonstrating excellent performance on the evaluation set

An audio classification model fine-tuned based on facebook/wav2vec2-base for music genre recognition

A fine-tuned speech classification model based on facebook/wav2vec2-base for audio folder dataset classification tasks

This model is a fine-tuned audio classification model based on facebook/wav2vec2-base on the superb dataset, achieving 99.87% accuracy on the evaluation set.

An audio classification model fine-tuned on the SUPERB dataset based on facebook/wav2vec2-base, achieving 99.98% accuracy

An audio emotion classification model fine-tuned based on wav2vec2-base, used to identify the speaker's emotional state.

A fine-tuned speech emotion recognition model based on facebook/wav2vec2-base, achieving 84.71% accuracy on the evaluation set

An audio classification model fine-tuned based on wav2vec2-base, used to detect scratches in vinyl record audio.

Age group classification model fine-tuned on the COSCAN-speech dataset based on wav2vec2-base, achieving 99.8% accuracy on the validation set

An English speech recognition model fine-tuned based on facebook/wav2vec2-large-nl-voxpopuli, trained using the Common Voice 7.0 training set.

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving a word error rate of 0.4499 on the evaluation set

A Kinyarwanda speech recognition model fine-tuned on the common_voice dataset based on facebook/wav2vec2-xls-r-300m

This model is an automatic speech recognition model fine-tuned on the ONSET-SINGING dataset based on wav2vec2-large-lv60, focusing on singing voice recognition tasks.

This model is an automatic speech recognition (ASR) model based on the wav2vec2-large-xls-r-300m architecture, fine-tuned on the Guarani speech dataset.

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

A fine-tuned speech keyword recognition model based on wav2vec2-base on the superb dataset, achieving 98.43% accuracy

A fine-tuned adult-child voice classification model based on facebook/wav2vec2-xls-r-300m, achieving 94.04% accuracy

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on a general speech dataset, primarily used for speech recognition tasks.