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Wav2Vec2.0 is a state-of-the-art model which learns speech representationsthrough unlabeled speech data, aka, self supervised learning. The pretrainedmodel is then fine tuned on small amounts of labeled data to use it forspeech-to-text and machine translation tasks. Wav2Vec 2.0 is a transformativesolution for low resource languages as it is mainly developed using unlabeledaudio data. Getting large amounts of labeled data is resource intensive andespecially challenging to do for low resource languages such as Swahilli,Tatar, etc. Furthermore, Wav2Vec2.0 word-error-rate(WER) matches or surpassesthe very recent supervised learning algorithms while using 100x less labeleddata. Given its importance and enormous potential in enabling speech basedtasks on world's 7000 languages, it is key to evaluate the accuracy, latencyand efficiency of this model on low resource and low power edge devices andinvestigate the feasibility of using it in such devices for private, secure andreliable speech based tasks. On-device speech tasks preclude sending audio datato the server hence inherently providing privacy, reduced latency and enhancedreliability. In this paper, Wav2Vec2.0 model's accuracy and latency has beenevaluated on Raspberry Pi along with the KenLM language model for speechrecognition tasks. How to tune certain parameters to achieve desired level ofWER rate and latency while meeting the CPU, memory and energy budgets of theproduct has been discussed.

Wav2Vec2.0 on the Edge: Performance Evaluation