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
  • Associated Paper
  • Associated Model Doc

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) and Bender et al. (2021)). 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 presented in Lacoste et al. (2019).

• 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.

Click to expand

from transformers import AutoModel
 
model = AutoModel.from_pretrained("patrickvonplaten/tiny-wav2vec2-no-tokenizer")