# wav2vec2 fine-tuning

A music genre classification model based on the wav2vec2 architecture, fine-tuned on the GTZAN dataset with 98% accuracy

A wav2vec2-based Serbian smart home voice command recognition model capable of identifying 7 control commands

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, used to identify synthetic or tampered speech content

A wav2vec 2.0 Base model fine-tuned on the large-scale Japanese automatic speech recognition corpus ReazonSpeech v2.0, suitable for Japanese automatic speech recognition tasks.

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 for audio emotion detection, capable of recognizing 7 emotional states

Amharic speech recognition model fine-tuned on the common_voice_16_1 dataset based on facebook/wav2vec2-xls-r-300m

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

This model is a Korean automatic speech recognition model fine-tuned on the zeroth_korean dataset based on facebook/wav2vec2-xls-r-300m, with a word error rate of 0.2433.

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, achieving 95.45% accuracy on the evaluation set

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, achieving 95.45% accuracy on the evaluation set

A speech recognition model fine-tuned on the speech_commands dataset based on facebook/wav2vec2-base, achieving 97.35% accuracy

Vietnamese tone recognition model fine-tuned on wav2vec2-base-vietnamese-250h, accuracy 59.72%

This is an end-to-end automatic speech recognition system trained on the CommonVoice Chinese dataset, using wav2vec2.0 and CTC architecture, supporting Chinese speech recognition.

This is an end-to-end automatic speech recognition system trained on the CommonVoice Spanish dataset, using the wav2vec 2.0 pre-trained model combined with a CTC decoder.

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

A cross-language and cross-age group speech emotion recognition model fine-tuned on wav2vec2-large-xlsr-53, supporting English and Chinese

A fine-tuned voice command classification model based on facebook/wav2vec2-base, trained on the superb dataset with an accuracy of 98.09%

This model is a voice command recognition model fine-tuned on the speech_commands dataset based on facebook/wav2vec2-base, achieving an accuracy of 97.59%.

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 75.73% on the evaluation set

A multi-class audio classification model fine-tuned based on facebook/wav2vec2-base for recognizing drum instrument sounds

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base and trained in the Google Colab environment.

This model is an automatic speech recognition (ASR) model based on the wav2vec2-large-lv60 architecture, fine-tuned on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset, primarily used for singing voice recognition tasks.

This is an automatic speech recognition model for Amharic, trained using wav2vec 2.0 architecture with CTC/Attention mechanism

This is an automatic speech recognition model for the Moroccan Arabic dialect (Darija), fine-tuned on the DVoice dataset based on the wav2vec 2.0 architecture.

This is an end-to-end automatic speech recognition system trained on the LibriSpeech dataset, combining the wav2vec 2.0 pre-trained model and CTC technology, excelling in English speech recognition tasks.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base for Persian language datasets, primarily used for Persian speech-to-text tasks.

This is a speech recognition model fine-tuned on Filipino speech datasets based on the wav2vec2-large-xlsr-53-english model, supporting English and Filipino speech-to-text tasks.

This model is a fine-tuned speech processing model based on kingabzpro/wav2vec2-urdu, suitable for specific speech recognition tasks.

This model is a speech recognition model fine-tuned on the filipino_voice dataset based on facebook/wav2vec2-xls-r-300m, supporting Filipino language.

A speech recognition model fine-tuned using 72 hours of MI diary data, based on Facebook's pre-trained wav2vec2 large 960H lv60 self-supervised model

This model is a Persian speech recognition model fine-tuned based on facebook/wav2vec2-base, suitable for Persian speech-to-text tasks.

An automatic speech recognition system fine-tuned on the Common Voice 8 Belarusian dataset based on facebook/wav2vec2-base model

This model is a fine-tuned Spanish automatic speech recognition model based on facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - ES dataset.

wav2vec 2.0 speech recognition model trained on the CommonVoice French dataset, using CTC/Attention architecture without requiring a language model

A speech recognition model fine-tuned based on facebook/wav2vec2-base, supporting Latin language speech processing

A fine-tuned Bengali automatic speech recognition (ASR) model based on Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200

A Croatian automatic speech recognition model fine-tuned from facebook/wav2vec2-xls-r-300m, trained on 300 hours of parliamentary speech data

This is an end-to-end model for automatic speech recognition in Rwandan, based on the wav2vec 2.0 pre-trained model combined with CTC and attention mechanisms, fine-tuned on the CommonVoice dataset.

This is a Spanish speech recognition model based on the wav2vec2 architecture, fine-tuned on the Common Voice dataset with a word error rate (WER) of 0.2105.

An automatic speech recognition model fine-tuned on the Basque Common Voice dataset based on facebook/wav2vec2-xls-r-300m