Wavlm Large Finetuned SER

A language identification model fine-tuned from Facebook's Massively Multilingual Speech project, supporting audio classification for 126 languages

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 language identification model fine-tuned on OpenAI Whisper-medium, achieving 88.05% accuracy on the FLEURS dataset

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

A speech language identification model based on the SpeechBrain framework and ECAPA-TDNN architecture, supporting recognition and speech embedding extraction for 107 languages.

The Audio Spectrogram Transformer (AST) is a model fine-tuned on AudioSet, which converts audio into spectrograms and applies a vision transformer for audio classification.

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

Speech emotion recognition using fine-tuned wav2vec2 model, trained on IEMOCAP dataset

An audio spectrogram transformer fine-tuned on the AudioSet dataset, which converts audio into spectrograms and processes them using a vision transformer architecture, achieving excellent performance in audio classification tasks.

Gender recognition model fine-tuned on Librispeech-clean-100 dataset, achieving an F1 score of 0.9993 on the test set