# End-to-end ASR

NVIDIA Parakeet TDT 0.6B V2 is a model based on automatic speech recognition (ASR) tasks, suitable for English speech-to-text tasks.

This is an end-to-end automatic speech recognition system trained on the CommonVoice Spanish dataset, using the wav2vec 2.0 pre-trained model combined with a CTC decoder.

A Whisper medium speech recognition model fine-tuned on the CommonVoice Arabic dataset, developed by the SpeechBrain team

A fine-tuned whisper medium model based on the CommonVoice-14.0 Persian dataset for Persian automatic speech recognition tasks.

A Japanese automatic speech recognition (ASR) model based on Whisper Large V2, optimized with CTranslate2 for efficient inference.

An acoustic model for Spanish automatic speech recognition, fine-tuned for 5 epochs based on facebook/wav2vec2-large-xlsr-53 using approximately 944 hours of Spanish data.

Icefall is an automatic speech recognition (ASR) toolkit based on the k2 framework, focusing on efficient and flexible speech recognition model training and inference.

This is an automatic speech recognition model for Wolof, based on the wav2vec 2.0 architecture, trained on the DVoice dataset, supporting Wolof speech transcription.

This is an automatic speech recognition model for Amharic, trained using wav2vec 2.0 architecture with CTC/Attention mechanism

A speech recognition model fine-tuned on the Common Voice Turkish dataset based on facebook/wav2vec2-large-xlsr-53

A speech recognition model fine-tuned on the CommonVoice Turkish dataset based on facebook/wav2vec2-xls-r-300m

A pre-trained end-to-end automatic speech recognition system for Mandarin based on the SpeechBrain framework, featuring a Transformer encoder + joint decoder architecture

This is an automatic speech recognition model fine-tuned on the Kyrgyz Common Voice dataset, based on the facebook/wav2vec2-large-xlsr-53 model.

This is an automatic speech recognition (ASR) model fine-tuned on the Turkish Common Voice dataset based on Facebook's wav2vec2-large-xlsr-53 model.

This is an Estonian automatic speech recognition (ASR) model fine-tuned from the facebook/wav2vec2-large-xlsr-53 model, trained using the Common Voice dataset.

This is an end-to-end automatic speech recognition system trained on the CommonVoice French dataset, utilizing a CRDNN architecture combined with CTC and attention mechanisms.

A speech recognition model fine-tuned on the Thai Common Voice dataset based on facebook/wav2vec2-large-xlsr-53

wav2vec 2.0 speech recognition model trained on the CommonVoice French dataset, using CTC/Attention architecture without requiring a language model

Turkish automatic speech recognition model based on wav2vec2 architecture, fine-tuned on the Common Voice 7.0 Turkish dataset

An automatic speech recognition model fine-tuned for Lithuanian using the Common Voice dataset, based on the facebook/wav2vec2-large-xlsr-53 model.

An automatic speech recognition model fine-tuned on the Common Voice 8 dataset based on facebook/wav2vec2-xls-r-300m, supporting Basque (bas).

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

A Hindi speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m

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

This is an automatic speech recognition model fine-tuned on the Mongolian Common Voice dataset based on facebook/wav2vec2-large-xlsr-53

Vietnamese automatic speech recognition model based on wav2vec 2.0 architecture, trained on 13,000 hours of unlabeled audio and 250 hours of labeled data