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