| --- |
| language: |
| - en |
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| --- |
| # Model Card for tiny-wav2vec2-no-tokenizer |
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| # Model Details |
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| ## Model Description |
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| - **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) |
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| # Uses |
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| ## Direct Use |
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| This model can be used for the task of Automatic Speech Recognition |
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| ## Downstream Use [Optional] |
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| More information needed |
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| ## Out-of-Scope Use |
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| The model should not be used to intentionally create hostile or alienating environments for people. |
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| # Bias, Risks, and Limitations |
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| 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. |
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| ## Recommendations |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| # Training Details |
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| ## Training Data |
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| More information needed |
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| ## Training Procedure |
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| ### Preprocessing |
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| More information needed |
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| ### Speeds, Sizes, Times |
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| More information needed |
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| # Evaluation |
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| ## Testing Data, Factors & Metrics |
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| ### Testing Data |
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| More information needed |
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| ### Factors |
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| ### Metrics |
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| More information needed |
| ## Results |
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| More information needed |
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| # Model Examination |
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| More information needed |
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| # Environmental Impact |
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| 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). |
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| - **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 |
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| # Technical Specifications [optional] |
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| ## Model Architecture and Objective |
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| More information needed |
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| ## Compute Infrastructure |
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| More information needed |
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| ### Hardware |
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| More information needed |
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| ### Software |
| More information needed |
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| # Citation |
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| **BibTeX:** |
| ``` |
| @misc{https://doi.org/10.48550/arxiv.2006.11477, |
| doi = {10.48550/ARXIV.2006.11477}, |
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| url = {https://arxiv.org/abs/2006.11477}, |
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| author = {Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael}, |
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| 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}, |
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| title = {wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations}, |
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| publisher = {arXiv}, |
| ``` |
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| # Glossary [optional] |
| More information needed |
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| # More Information [optional] |
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| More information needed |
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| # Model Card Authors [optional] |
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| Patrick von Platen in collaboration with the Hugging Face team |
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| # Model Card Contact |
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| More information needed |
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| # How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| <details> |
| <summary> Click to expand </summary> |
|
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| ```python |
| from transformers import AutoModel |
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| model = AutoModel.from_pretrained("patrickvonplaten/tiny-wav2vec2-no-tokenizer") |
| ``` |
| </details> |
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