# English speech recognition

Lightweight audio model, excelling in speech recognition, audio understanding, and executing audio instructions among other diverse tasks

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, achieving 95.45% accuracy on the evaluation set

Parakeet TDT-CTC 1.1B is an automatic speech recognition model capable of transcribing English speech with punctuation and capitalization, jointly developed by NVIDIA NeMo and Suno.ai.

This is the CTranslate2 converted version of the OpenAI Whisper medium.en model, used for efficient automatic speech recognition tasks.

A sequence-to-sequence model supporting English automatic speech recognition (ASR), capable of outputting normalized text, timestamp annotations, and multi-speaker segmentation.

An English speech recognition model fine-tuned based on facebook/wav2vec2-large-nl-voxpopuli, trained using the Common Voice 7.0 training set.

This is a model designed for English speech recognition tasks, based on a randomly initialized wav2vec2 architecture and fine-tuned using the Common Voice 7.0 dataset.

This model is an automatic speech recognition (ASR) model trained on the LibriSpeech ASR dataset, capable of converting English speech into text.

An automatic speech recognition model trained on the LibriSpeech ASR dataset, designed to convert English speech into text.

An English speech recognition model fine-tuned from the facebook/wav2vec2-large-xlsr-53 model, trained on the Common Voice 6.1 dataset

A Wav2Vec2 base model pre-trained on a 10K unlabeled subset of the VoxPopuli corpus and fine-tuned on English transcription data, suitable for English speech recognition tasks.

This model is an automatic speech recognition model fine-tuned on the TIMIT_ASR dataset based on microsoft/unispeech-sat-base, achieving a word error rate of 41.01% on the evaluation set.

This is an end-to-end automatic speech recognition system trained on the CommonVoice English dataset, combining the wav2vec 2.0 pre-trained model and CTC decoder.

A speech recognition model based on Wav2Vec 2.0 architecture, fine-tuned on the TIMIT dataset, suitable for English speech-to-text tasks

Fine-tuned automatic speech recognition model based on facebook/wav2vec2-large-xlsr-53 on the English Common Voice dataset, supporting punctuation prediction

This model is an automatic speech recognition model fine-tuned on the TIMIT_ASR - NA dataset, based on patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps.

An automatic speech recognition (ASR) model fine-tuned on the TIMIT_ASR dataset based on microsoft/unispeech-sat-base-plus

An automatic speech recognition (ASR) model trained on the LibriSpeech dataset for converting English speech to text

An automatic speech recognition model fine-tuned on the TIMIT_ASR dataset based on the wav2vec2-base-random model

An automatic speech recognition model fine-tuned on English based on facebook/wav2vec2-large, trained using the Common Voice 6.1 dataset

This is an English speech recognition model based on the XLS-R 1B architecture, fine-tuned on multiple English speech datasets.

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.

A speech recognition model fine-tuned on the timit_asr dataset based on facebook/wav2vec2-large-lv60