Update README.md

README.md

@@ -14,7 +14,7 @@ pipeline_tag: audio-classification
 We explore benefits of unsupervised pretraining of wav2vec 2.0 (W2V2) using large-scale unlabeled home recordings collected using LittleBeats (LB) and LENA (Language Environment Analysis) devices.
 LittleBeats is a new infant wearable multi-modal device that we developed, which simultaneously records audio, movement of the infant, as well as heart-rate variablity.
 We use W2V2 to advance LB audio pipeline such that it automatically provides reliable labels of speaker diarization and vocalization classifications for family members, including infants, parents, and siblings, at home. 
-We show that W2V2 pretrained on thousands hours of large-scale unlabeled home audio outperforms oracle W2V2 pretrained on 52k-hours released by Facebook/Meta in terms of automatic family audio analysis tasks.
+We show that W2V2 pretrained on thousands hours of large-scale unlabeled home audio outperforms oracle W2V2 pretrained on 960 hours Librispeech released by Facebook/Meta in terms of automatic family audio analysis tasks.
 
 For more details about LittleBeats, check out **https://littlebeats.hdfs.illinois.edu/**
 
@@ -165,7 +165,7 @@ Run the following sample code by importing the **FairseqWav2Vec2** class.
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 We test 4 unlabeled datasets on unsupervised pretrained W2V2-base models:
-- **base (oracle version):** originally released version pretrained on ~52k-hour unlabeled audio
+- **base (oracle version):** originally released version pretrained on ~960-hour unlabeled Librispeech audio
 - **Libri960h:** oracle version fine-tuned using 960h Librispeech
 - **LB1100h:** pretrain W2V2 using 1100h LB home recordings
 - **LL4300h:** pretrain W2V2 using 4300h LB+LENA home recordings