# LibriSpeech Fine-tuning

A speech recognition model fine-tuned from facebook/hubert-large-ls960-ft, trained on the LibriSpeech dataset

This model is a speech recognition model fine-tuned on the LibriSpeech dataset based on facebook/wav2vec2-base, achieving a word error rate of 0.3174 on the evaluation set.

This is the Italian fine-tuned version of the F5-TTS model, trained on the facebook/multilingual_librispeech dataset, specializing in Italian text-to-speech tasks.

Wav2Vec2 is a self-supervised learning-based speech recognition model, pre-trained and fine-tuned on 960 hours of LibriSpeech audio data, supporting English speech transcription.

Speech recognition model fine-tuned from Kuray107/librispeech-5h-supervised, trained on 100 hours of LibriSpeech dataset

This model is an automatic speech recognition (ASR) model fine-tuned on the German subset of the multilingual LibriSpeech dataset, based on facebook/wav2vec2-xls-r-100m

A speech recognition model fine-tuned on the LibriSpeech ASR clean dataset, based on wav2vec2-base and bart-base

Automatic speech recognition model based on the WavLM architecture, fine-tuned on the LibriSpeech CLEAN dataset (100 hours)

This model is an automatic speech recognition model fine-tuned from asapp/sew-d-mid-400k on the LIBRISPEECH_ASR - CLEAN dataset, achieving a word error rate (WER) of 1.0536 on the evaluation set.