# LibriSpeech optimization

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.

A speech-to-text (S2T) model for automatic speech recognition (ASR)

This model incorporates rotary position embedding technology, is pre-trained and fine-tuned on 960 hours of LibriSpeech data sampled at 16kHz, and is suitable for English speech recognition tasks.

A Wav2Vec2-Conformer model based on 16kHz sampled speech audio, using relative positional embedding technology, pre-trained and fine-tuned on 960 hours of Librispeech data

This model is a large-scale speech recognition model based on the Wav2Vec2 architecture, pre-trained and fine-tuned on 10 minutes of data from Libri-Light and Librispeech, using self-training objectives, suitable for 16kHz sampled speech audio.

Wav2Vec2-Large-100h-Lv60 is a large model pre-trained and fine-tuned on 100 hours of Libri-Light and Librispeech speech data, trained with self-training objectives, suitable for speech recognition tasks with 16kHz sampling rate.

A speech-to-text (S2T) model for automatic speech recognition (ASR), based on a sequence-to-sequence transformer architecture

An automatic speech recognition model fine-tuned on the LIBRISPEECH_ASR - CLEAN dataset based on microsoft/wavlm-base

The Wav2Vec2 base model developed by Facebook, pre-trained and fine-tuned on 960 hours of LibriSpeech audio for English automatic speech recognition tasks.

Wav2Vec2 Base is an automatic speech recognition model pre-trained and fine-tuned on 16kHz sampled LibriSpeech audio for 100 hours.

SEW-D is a compressed and efficient speech pre-training model developed by ASAPP Research, pre-trained on 16kHz sampled speech audio, suitable for various downstream speech tasks.

This model is a speech recognition model trained on the librispeech_asr dataset, achieving a word error rate (WER) of 2.0133 on the evaluation set.

Audio enhancement model trained based on the Asteroid framework, specifically designed for mono speech enhancement tasks

An audio enhancement model trained on the Asteroid framework, specifically designed for single-channel speech enhancement tasks, trained on the Libri1Mix dataset.

Audio enhancement model trained based on the Asteroid framework, focusing on mono speech enhancement tasks