# Conformer architecture

This is an end-to-end automatic speech recognition system trained using the SpeechBrain framework, employing the Conformer architecture on 25,000 hours of English speech data.

IndicConformer is a Conformer-based automatic speech recognition model with hybrid CTC-RNNT architecture, specifically optimized for Nepali language

IndicConformer is a Conformer-based automatic speech recognition (ASR) model with a hybrid CTC-RNNT architecture, supporting Hindi speech transcription.

A speech encoder based on the Conformer architecture, pretrained on 4.5 million hours of unlabeled audio data, supporting over 143 languages

A text-to-speech model integrating FastSpeech2Conformer with HiFi-GAN, providing efficient and high-quality speech synthesis

FastSpeech2Conformer is a non-autoregressive text-to-speech (TTS) model that combines the advantages of FastSpeech2 and the Conformer architecture, enabling fast and efficient generation of high-quality speech from text.

This is a large Conformer-Transducer model for Kinyarwanda speech recognition, which can transcribe speech into lowercase Latin letters, supporting spaces and apostrophes.

This is a large Conformer-Transducer model for transcribing Mandarin speech, with approximately 120 million parameters, trained on the AISHELL-2 dataset.

This is an Automatic Speech Recognition (ASR) model developed by NVIDIA, based on the Conformer-Transducer architecture, with approximately 600 million parameters, specifically designed for English speech transcription.

This is a large-scale Korean automatic speech recognition model based on the Conformer-Transducer architecture, trained on the Ksponspeech dataset, suitable for Korean speech transcription tasks.

Wav2Vec2 Conformer model fine-tuned on 100 hours of Librispeech data, incorporating rotary position embedding technology

An automatic speech recognition (ASR) model trained on the ESPnet framework, using the Conformer architecture and the WavLM large pre-trained model, trained on the LibriSpeech dataset.

A text-to-speech model trained using the ESPnet framework, utilizing the VCTK dataset, supporting multi-speaker speech synthesis