Update README.md

README.md

@@ -1,6 +1,13 @@
 ---
+language: en
 datasets:
- - librispeech_asr
+- superb
+tags:
+- speech
+- audio
+- wav2vec2
+- audio-classification
+license: apache-2.0
 ---
 # Model Card for wav2vec2-base-superb-sv
  
@@ -10,16 +17,16 @@ datasets:
 ## Model Description
  
  
-- **Developed by:** Anton Lozhkov
-- **Shared by [Optional]:** More information needed
-- **Model type:** More information needed
-- **Language(s) (NLP):** More information needed
-- **License:** More information needed
+- **Developed by:** Shu-wen Yang et al.
+- **Shared by:** Anton Lozhkov
+- **Model type:** Wav2Vec2 with an XVector head
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
 - **Related Models:**
-  - **Parent Model:** wav2vec2
+  - **Parent Model:** wav2vec2-large-lv60
 - **Resources for more information:** 
-    - [GitHub Repo](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20)
- 	 - [Associated Paper](https://arxiv.org/abs/2006.11477)
+    - [GitHub Repo](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1)
+ 	 - [Associated Paper](https://arxiv.org/abs/2105.010517)
  
  
 # Uses
@@ -27,11 +34,13 @@ datasets:
  
 ## Direct Use
  
-More information needed
- 
-## Downstream Use [Optional]
- 
-More information needed
+This is a ported version of 
+[S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1).
+
+The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), which is pretrained on 16kHz 
+sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 
+
+For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
  
 ## Out-of-Scope Use
  
@@ -51,7 +60,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
  
 ## Training Data
  
-See the [librispeech_asr dataset card](https://huggingface.co/datasets/librispeech_asr)
+See the [superb dataset card](https://huggingface.co/datasets/superb)
  
 ## Training Procedure
  
@@ -71,7 +80,7 @@ More information needed
  
 ### Testing Data
  
-See the [librispeech_asr dataset card](https://huggingface.co/datasets/librispeech_asr)
+See the [superb dataset card](https://huggingface.co/datasets/superb)
  
 ### Factors
  
@@ -133,6 +142,24 @@ More information needed
   
   publisher = {arXiv},
 
+
+@misc{https://doi.org/10.48550/arxiv.2105.01051,
+  doi = {10.48550/ARXIV.2105.01051},
+  
+  url = {https://arxiv.org/abs/2105.01051},
+  
+  author = {Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y. and Liu, Andy T. and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and Huang, Tzu-Hsien and Tseng, Wei-Cheng and Lee, Ko-tik and Liu, Da-Rong and Huang, Zili and Dong, Shuyan and Li, Shang-Wen and Watanabe, Shinji and Mohamed, Abdelrahman and Lee, Hung-yi},
+  
+  keywords = {Computation and Language (cs.CL), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
+  
+  title = {SUPERB: Speech processing Universal PERformance Benchmark},
+  
+  publisher = {arXiv},
+  
+  year = {2021},
+}
+
+
 ```