# Speech recognition fine-tuning

English pronunciation assessment model based on wav2vec2-large-xlsr-53 for evaluating English pronunciation quality

A speech processing model fine-tuned based on facebook/wav2vec2-base, with an accuracy of 28.57%

A speech processing model fine-tuned based on facebook/wav2vec2-base, specific purpose not clearly stated

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

This model is a fine-tuned speech processing model based on facebook/wav2vec2-base, with no specific use case explicitly stated

This model is a speech recognition model fine-tuned on an unknown dataset based on facebook/wav2vec2-base, primarily used for Automatic Speech Recognition (ASR) tasks.

Speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, optimized on 10ms audio masked training data

A speech recognition model fine-tuned based on facebook/wav2vec2-base, trained on 10ms masked audio tasks

A speech recognition model fine-tuned from facebook/wav2vec2-base, trained on a toy dataset with 0.1 ratio data augmentation applied

This model is a speech recognition model fine-tuned from facebook/wav2vec2-base, trained using data augmentation techniques

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, utilizing data augmentation techniques (augmentation ratio of 0.1).

An automatic speech recognition model fine-tuned on the TIMIT_ASR dataset based on asapp/sew-d-small-100k

This model is an automatic speech recognition model fine-tuned from asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset, achieving a word error rate of 0.8061 on the evaluation set.