Update quick start instructions

README.md

@@ -48,7 +48,34 @@ Two pretrained ECAPA-TDNN speaker embeddings are available:
 ## Quick Start
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-If you wish to use fairseq framework, the following code snippet provides two functions of loading our pretrained W2V2 model and extracting features. 
+To extract features from pretrained W2V2 model, first install fairseq and speechbrain framework 
+  <pre><code>
+    pip install fairseq
+    pip install speechbrain
+  </code></pre>
+Download [this code snippet (fairseq_wav2vec.py)](https://huggingface.co/lijialudew/wav2vec_LittleBeats_LENA/blob/main/fairseq_wav2vec.py) in this repo.
+Download the pretrained or fine-tuned model weights.
+Run the following sample code by importing the **FairseqWav2Vec2** class.
+
+  <pre><code>
+  from fairseq_wav2vec import FairseqWav2Vec2
+  import torch 
+  inputs = torch.rand([10, 6000]) # input wav B x T
+  save_path = "your/path/to/LL_4300/checkpoint_best.pt"
+  # extract features from all transformer layers
+  model = FairseqWav2Vec2(save_path) # Output all features from 12 transformer layers with shapes of 12 x B x T' x D
+  # To extract features from a certain transformer layer
+  # model = FairseqWav2Vec2(save_path, output_all_hiddens = False, tgt_layer = [1]) # Output features from the first transformer layer
+  # to load W2V2 model fine-tuned on LENA and LB audio data
+  fine_tuned_path = "your/path/to/LL_4300_fine_tuned/save/CKPT+2022-11-26+14-06-17+00/wav2vec2.ckpt"
+  model._load_sb_pretrained_w2v2_parameters(fine_tuned_path)
+  # To extract wav2vec features
+  outputs = model(inputs)
+  print(outputs.shape)
+  </code></pre>
+
+
+<!-- previous comments If you wish to use fairseq framework, the following code snippet provides two functions of loading our pretrained W2V2 model and extracting features. 
 
   <pre><code>
   import torch
@@ -133,7 +160,7 @@ If you wish to use fairseq framework, the following code snippet provides two fu
   audio = audio.transpose(0,1).squeeze(1)
   features = extract_features(model, audio)
   </code></pre>
-  
+  -->
 # Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
@@ -155,11 +182,13 @@ For more details of experiments and results, please refer to our paper.
 If you found this model helpful to you, please cite us as
 
 <pre><code>
-@article{li2023towards,
-  title={Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio},
-  author={Li, Jialu and Hasegawa-Johnson, Mark and McElwain, Nancy L},
-  journal={Interspeech},
-  year={2023}
+@inproceedings{li23e_interspeech,
+  author={Jialu Li and Mark Hasegawa-Johnson and Nancy L. McElwain},
+  title={{Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio}},
+  year=2023,
+  booktitle={Proc. INTERSPEECH 2023},
+  pages={1035--1039},
+  doi={10.21437/Interspeech.2023-460}
 }
   </code></pre>