Anatomy of Industrial Scale Multilingual ASR
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909171149963264 |
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| author | Ramirez, Francis McCann Chkhetiani, Luka Ehrenberg, Andrew McHardy, Robert Botros, Rami Khare, Yash Vanzo, Andrea Peyash, Taufiquzzaman Oexle, Gabriel Liang, Michael Sklyar, Ilya Fakhan, Enver Etefy, Ahmed McCrystal, Daniel Flamini, Sam Donato, Domenic Yoshioka, Takuya |
| author_facet | Ramirez, Francis McCann Chkhetiani, Luka Ehrenberg, Andrew McHardy, Robert Botros, Rami Khare, Yash Vanzo, Andrea Peyash, Taufiquzzaman Oexle, Gabriel Liang, Michael Sklyar, Ilya Fakhan, Enver Etefy, Ahmed McCrystal, Daniel Flamini, Sam Donato, Domenic Yoshioka, Takuya |
| contents | This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system, designed to meet the requirements of large-scale, multilingual ASR serving various application needs. Our system leverages a diverse training dataset comprising unsupervised (12.5M hours), supervised (188k hours), and pseudo-labeled (1.6M hours) data across four languages. We provide a detailed description of our model architecture, consisting of a full-context 600M-parameter Conformer encoder pre-trained with BEST-RQ and an RNN-T decoder fine-tuned jointly with the encoder. Our extensive evaluation demonstrates competitive word error rates (WERs) against larger and more computationally expensive models, such as Whisper large and Canary-1B. Furthermore, our architectural choices yield several key advantages, including an improved code-switching capability, a 5x inference speedup compared to an optimized Whisper baseline, a 30% reduction in hallucination rate on speech data, and a 90% reduction in ambient noise compared to Whisper, along with significantly improved time-stamp accuracy. Throughout this work, we adopt a system-centric approach to analyzing various aspects of fully-fledged ASR models to gain practically relevant insights useful for real-world services operating at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anatomy of Industrial Scale Multilingual ASR Ramirez, Francis McCann Chkhetiani, Luka Ehrenberg, Andrew McHardy, Robert Botros, Rami Khare, Yash Vanzo, Andrea Peyash, Taufiquzzaman Oexle, Gabriel Liang, Michael Sklyar, Ilya Fakhan, Enver Etefy, Ahmed McCrystal, Daniel Flamini, Sam Donato, Domenic Yoshioka, Takuya Audio and Speech Processing Computation and Language Machine Learning Sound This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system, designed to meet the requirements of large-scale, multilingual ASR serving various application needs. Our system leverages a diverse training dataset comprising unsupervised (12.5M hours), supervised (188k hours), and pseudo-labeled (1.6M hours) data across four languages. We provide a detailed description of our model architecture, consisting of a full-context 600M-parameter Conformer encoder pre-trained with BEST-RQ and an RNN-T decoder fine-tuned jointly with the encoder. Our extensive evaluation demonstrates competitive word error rates (WERs) against larger and more computationally expensive models, such as Whisper large and Canary-1B. Furthermore, our architectural choices yield several key advantages, including an improved code-switching capability, a 5x inference speedup compared to an optimized Whisper baseline, a 30% reduction in hallucination rate on speech data, and a 90% reduction in ambient noise compared to Whisper, along with significantly improved time-stamp accuracy. Throughout this work, we adopt a system-centric approach to analyzing various aspects of fully-fledged ASR models to gain practically relevant insights useful for real-world services operating at scale. |
| title | Anatomy of Industrial Scale Multilingual ASR |
| topic | Audio and Speech Processing Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2404.09841 |