# 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>

{"language": ["en"]}
transformers pytorch tf wav2vec2 en arxiv:2006.11477 arxiv:1910.09700 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #wav2vec2 #en #arxiv-2006.11477 #arxiv-1910.09700 #endpoints_compatible #region-us 

# 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
 
BibTeX:
 
 

# 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>


</details>

# 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
 
BibTeX: # 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>


</details>
TAGS
#transformers #pytorch #tf #wav2vec2 #en #arxiv-2006.11477 #arxiv-1910.09700 #endpoints_compatible #region-us 
 # 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
 
BibTeX: # 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>


</details>
passage: TAGS
#transformers #pytorch #tf #wav2vec2 #en #arxiv-2006.11477 #arxiv-1910.09700 #endpoints_compatible #region-us 
# 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]