# English speech processing

An automatic speech recognition model based on Wav2Vec2-Base architecture, specifically fine-tuned for phoneme recognition on the LJSpeech Phonemes dataset

English phoneme recognition model fine-tuned from facebook/wav2vec2-large-lv60, achieving a phoneme error rate of 10.53% on the TIMIT dataset

A phoneme recognition model based on the Wav2Vec2 architecture, fine-tuned on the LJSpeech Phonemes dataset, used to convert speech into phoneme sequences

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base and trained in the Google Colab environment.

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 is a large automatic speech recognition (ASR) model based on the Conformer architecture, supporting English speech transcription and trained using CTC loss function.

Wav2Vec2 is a powerful speech recognition model that extracts features from raw audio through self-supervised learning and achieves high-performance speech recognition with limited labeled data.

HuBERT-Large is a self-supervised speech representation learning model fine-tuned on 960 hours of LibriSpeech data for automatic speech recognition tasks.

A fine-tuned HuBERT extra-large speech recognition model based on 960 hours of Librispeech data, achieving a WER of only 1.8 on the LibriSpeech test set

Data2Vec is a general self-supervised learning framework applicable to speech, vision, and language processing. This model is a speech recognition model pre-trained and fine-tuned on 960 hours of LibriSpeech audio data.

This model is a speech recognition model fine-tuned on the LibriSpeech 100-hour dataset based on facebook/wav2vec2-large-lv60, achieving a low word error rate.