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:
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
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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
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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")