# Audio Processing

A fine-tuned speech processing model based on facebook/wav2vec2-base, achieving 99.48% accuracy on the evaluation set

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN dataset for music genre classification tasks.

A speech processing model further fine-tuned based on wav2vec2-base-finetuned, achieving 98.82% accuracy on the evaluation set

A speech processing model fine-tuned based on the facebook/wav2vec2-base model, achieving 99.97% accuracy on the evaluation set

A speech processing model fine-tuned based on the facebook/wav2vec2-base model, achieving 99.97% accuracy on the evaluation set

A chord detection model fine-tuned based on the DistilHuBERT model, trained on the ChordStimation dataset with an evaluation accuracy of 100%

Snoop is an audio-to-audio model based on RVC (Retrieval-based Voice Conversion) technology, primarily used for voice conversion tasks.

This is a voice conversion model based on RVC (Retrieval-based Voice Conversion), which can transform input audio into speech with a specific style.

Drake_RVC is an audio-to-audio model based on RVC (Retrieval-based Voice Conversion) technology, specifically designed for voice conversion tasks.

This is a voice conversion model based on RVC (Retrieval-based Voice Conversion) technology, capable of transforming input audio into speech output with a specific style.

This is a voice conversion model based on RVC (Retrieval-based Voice Conversion) technology, trained for 400 rounds, suitable for audio-to-audio tasks.

This is a voice conversion model based on RVC (Retrieval-based Voice Conversion) technology, which can convert input audio into Andrew Tate's style of voice.

This is an RVC (Retrieval-based Voice Conversion) model designed for audio-to-audio conversion tasks.

A speech classification model fine-tuned on the minds14 dataset based on microsoft/unispeech-sat-base-100h-libri-ft

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset, primarily used for speech recognition tasks, achieving an accuracy of 84.55% on the evaluation set.

A general language identification model fine-tuned based on openai/whisper-small, achieving 88.6% accuracy on the evaluation dataset

A fine-tuned version based on facebook/wav2vec2-base model for specific tasks

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 87.27% on the evaluation set.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base on the Common Voice 3.0 English dataset, achieving a word error rate of 0.247 on the test set.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.1042 on the evaluation set.

This is an audio source separation model based on the RE-SepFormer architecture, implemented by SpeechBrain and trained on the WSJ0-2Mix dataset.

This model is a speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m, supporting automatic speech recognition tasks.

A speech recognition model fine-tuned based on facebook/wav2vec2-base, achieving a word error rate of 0.4499 on the evaluation set, which can be reduced to 0.126 when using a 4-Gram language model

A fine-tuned speech recognition model based on facebook/wav2vec2-base, supporting automatic speech-to-text tasks.

A fine-tuned speech recognition model based on facebook/wav2vec2-base, supporting automatic speech-to-text tasks

A speech recognition model fine-tuned from facebook/wav2vec2-base, achieving a Word Error Rate (WER) of 1.0 on the evaluation set

A fine-tuned speech recognition model based on facebook/wav2vec2-base, primarily used for Automatic Speech Recognition (ASR) tasks

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, primarily used for automatic speech recognition tasks.

A fine-tuned speech recognition model based on facebook/wav2vec2-base with a Word Error Rate (WER) of 1.0

A fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 31.42% on the evaluation set

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-300m, supporting automatic speech recognition (ASR) tasks.

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

A fine-tuned speech recognition model based on facebook/wav2vec2-base, optimized with augmented training data.

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

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

A speech recognition model fine-tuned based on facebook/wav2vec2-xls-r-300m, achieving a word error rate (WER) of 1.2628 on the evaluation set.

A speech recognition model fine-tuned on the Common Voice dataset based on wav2vec2-base-checkpoint-5

A speech recognition model fine-tuned based on facebook/wav2vec2-base, trained on a specific dataset with a word error rate (WER) of 0.3391.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained in a Colab environment

A fine-tuned speech recognition model based on facebook/wav2vec2-base, suitable for English speech-to-text tasks.