readme: ---
  language:
  - en
  
  ---
  # Model Card for tiny-wav2vec2-no-tokenizer 
   
   
  # Model Details
   
  ## Model Description
   
   
  - **Developed by:** More information needed
  - **Shared by [Optional]:** Patrick von Platen
  - **Model type:**  Automatic Speech Recognition
  - **Language(s) (NLP):** en
  - **License:** More information needed
  - **Related Models:**
    - **Parent Model:** Wav2Vec2
  - **Resources for more information:** 
      - [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec#wav2vec-20)
       - [Associated Paper](https://arxiv.org/abs/2006.11477)
      - [Associated Model Doc](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2)
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Automatic Speech Recognition
   
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## 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
   
  More information needed
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### 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},
  ```
   
  
  # Glossary [optional]
  More information needed
   
  # More Information [optional]
   
  More information needed
   
  # Model Card Authors [optional]
   
   
  Patrick von Platen in collaboration with the Hugging Face team
   
  # Model Card Contact
   
  More information needed
   
  # How to Get Started with the Model
   
  Use the code below to get started with the model.
   
  <details>
  <summary> Click to expand </summary>
  
  ```python
  from transformers import AutoModel
   
  model = AutoModel.from_pretrained("patrickvonplaten/tiny-wav2vec2-no-tokenizer")
  ```
  </details>
  