# FastConformer architecture

MLX format automatic speech recognition model converted from NVIDIA Parakeet TDT 0.6B v2, supporting efficient speech-to-text tasks.

This is a large-scale Uzbek speech recognition model based on the FastConformer architecture, supporting both Transducer and CTC decoding, and demonstrating excellent performance across multiple test sets.

Parakeet TDT-CTC 0.6B is an automatic speech recognition (ASR) model capable of transcribing Japanese speech with punctuation, developed by the NVIDIA NeMo team.

Canary-1B is a multilingual multi-task model developed by NVIDIA NeMo, supporting automatic speech recognition and speech translation tasks in English, German, French, and Spanish.

Parakeet CTC 1.1B is an automatic speech recognition model jointly developed by NVIDIA NeMo and Suno.ai, based on the FastConformer architecture with approximately 1.1 billion parameters, supporting English speech transcription.

Parakeet RNNT 1.1B is an automatic speech recognition model jointly developed by NVIDIA NeMo and Suno.ai, based on the FastConformer Transducer architecture with approximately 1.1 billion parameters, supporting English speech transcription.

The NVIDIA FastConformer-Transducer is a high-performance model for English automatic speech recognition (ASR), utilizing an optimized FastConformer architecture and Transducer decoder with approximately 618 million parameters.

NVIDIA FastConformer-CTC XLarge is an Automatic Speech Recognition (ASR) model with approximately 600 million parameters, designed specifically for English speech transcription and trained using the FastConformer architecture and CTC loss.

This is a large automatic speech recognition (ASR) model based on the FastConformer architecture, specifically designed for transcribing English speech into text.

This is a large automatic speech recognition (ASR) model based on the FastConformer architecture, specifically designed for transcribing English speech into text.

This is a FastConformer hybrid model for Russian automatic speech recognition, combining Transducer and CTC decoders with approximately 115 million parameters.

This is a large-scale Belarusian automatic speech recognition model based on the FastConformer architecture, combining Transformer and CTC decoder loss, trained on 1,500 hours of Belarusian speech data.

NVIDIA FastConformer-Hybrid Large (ua) is a hybrid model for Ukrainian speech recognition, which combines the training of two loss functions, Transducer and CTC, with approximately 115 million parameters.