Less is More: Accurate Speech Recognition & Translation without Web-Scale Data

• URL: http://arxiv.org/abs/2406.19674v1
• Date: Fri, 28 Jun 2024 06:22:23 GMT
• Title: Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
• Authors: Krishna C. Puvvada, Piotr Żelasko, He Huang, Oleksii Hrinchuk, Nithin Rao Koluguri, Kunal Dhawan, Somshubra Majumdar, Elena Rastorgueva, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Boris Ginsburg,
• Abstract summary: Canary is a multilingual ASR and speech translation model. It outperforms Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German languages.
• Score: 26.461185681285745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without relying on web-scale data. Canary - multilingual ASR and speech translation model, outperforms current state-of-the-art models - Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German languages, while being trained on an order of magnitude less data than these models. Three key factors enables such data-efficient model: (1) a FastConformer-based attention encoder-decoder architecture (2) training on synthetic data generated with machine translation and (3) advanced training techniques: data-balancing, dynamic data blending, dynamic bucketing and noise-robust fine-tuning. The model, weights, and training code will be open-sourced.

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