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  # Whisper-Tiny-En: Optimized for Mobile Deployment
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  ## Automatic speech recognition (ASR) model for English transcription as well as translation
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  OpenAI’s Whisper ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a mean decoded length specified below.
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- This model is an implementation of Whisper-Tiny-En found [here]({source_repo}).
 
 
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  This repository provides scripts to run Whisper-Tiny-En on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/whisper_tiny_en).
 
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  # Whisper-Tiny-En: Optimized for Mobile Deployment
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  ## Automatic speech recognition (ASR) model for English transcription as well as translation
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  OpenAI’s Whisper ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a mean decoded length specified below.
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+ This model is an implementation of Posenet-Mobilenet found [here](https://github.com/openai/whisper/tree/main).
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  This repository provides scripts to run Whisper-Tiny-En on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/whisper_tiny_en).