
# Distil-wav2vec2 
This model is a distilled version of the wav2vec2 model (https://arxiv.org/pdf/2006.11477.pdf). This model is 45% times smaller and twice as fast as the original wav2vec2 base model.

# Evaluation results
This model achieves the following results (speed is mesured for a batch size of 64): 

|Model| Size| WER Librispeech-test-clean |WER Librispeech-test-other|Speed on cpu|speed on gpu| 
|----------| ------------- |-------------|-----------| ------|----|
|Distil-wav2vec2| 197.9 Mb | 0.0983 | 0.2266|0.4006s| 0.0046s|
|wav2vec2-base| 360 Mb | 0.0389 | 0.1047|0.4919s| 0.0082s|


# Usage
notebook (executes seamlessly on google colab) at https://github.com/OthmaneJ/distil-wav2vec2

{"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]}
transformers pytorch wav2vec2 automatic-speech-recognition speech audio en dataset:librispeech_asr arxiv:2006.11477 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

Distil-wav2vec2
===============


This model is a distilled version of the wav2vec2 model (URL This model is 45% times smaller and twice as fast as the original wav2vec2 base model.


Evaluation results
==================


This model achieves the following results (speed is mesured for a batch size of 64):



Usage
=====


notebook (executes seamlessly on google colab) at URL



TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
