readme: ---
  language: en
  datasets:
  - superb
  tags:
  - speech
  - audio
  - wav2vec2
  - audio-classification
  license: apache-2.0
  ---
  # Model Card for wav2vec2-base-superb-sv
   
   
  # Model Details
   
  ## Model Description
   
   
  - **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-large-lv60
  - **Resources for more information:** 
      - [GitHub Repo](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1)
      - [Associated Paper](https://arxiv.org/abs/2105.010517)
   
   
  # Uses
   
   
  ## Direct Use
   
  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
   
  The model should not be used to intentionally create hostile or alienating environments for people.
   
  # Bias, Risks, and Limitations
   
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
   
   
  # Training Details
   
  ## Training Data
   
  See the [superb dataset card](https://huggingface.co/datasets/superb)
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  See the [superb dataset card](https://huggingface.co/datasets/superb)
   
  ### Factors
   
   
  ### Metrics
   
  More information needed
  ## Results 
   
  More information needed
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
  More information needed
   
  # Citation
   
   
  **BibTeX:**
   ```
  @misc{https://doi.org/10.48550/arxiv.2006.11477,
    doi = {10.48550/ARXIV.2006.11477},
    
    url = {https://arxiv.org/abs/2006.11477},
    
    author = {Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael},
    
    keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), 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 = {wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations},
    
    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},
  }
  
  
  ```
  
   
  # Glossary [optional]
  More information needed
   
  # More Information [optional]
   
  Mor