# Model Card for wav2vec2-base-superb-sv
 
 
# Model Details
 
## Model Description
 
 
- **Developed by:** Shu-wen Yang et al.
- **Shared by:** Anton Lozhkov
- **Model type:** Wav2Vec2 with an XVector head
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:**
  - **Parent Model:** wav2vec2-large-lv60
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1)
    - [Associated Paper](https://arxiv.org/abs/2105.010517)
 
 
# Uses
 
 
## Direct Use
 
This is a ported version of 
[S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1).

The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), which is pretrained on 16kHz 
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 

For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
 
## 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
 
See the [superb dataset card](https://huggingface.co/datasets/superb)
 
## Training Procedure
 
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
See the [superb dataset card](https://huggingface.co/datasets/superb)
 
### 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},


@misc{https://doi.org/10.48550/arxiv.2105.01051,
  doi = {10.48550/ARXIV.2105.01051},
  
  url = {https://arxiv.org/abs/2105.01051},
  
  author = {Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y. and Liu, Andy T. and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and Huang, Tzu-Hsien and Tseng, Wei-Cheng and Lee, Ko-tik and Liu, Da-Rong and Huang, Zili and Dong, Shuyan and Li, Shang-Wen and Watanabe, Shinji and Mohamed, Abdelrahman and Lee, Hung-yi},
  
  keywords = {Computation and Language (cs.CL), 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 = {SUPERB: Speech processing Universal PERformance Benchmark},
  
  publisher = {arXiv},
  
  year = {2021},
}


```

 
# Glossary [optional]
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
 
Anton Lozhkov in collaboration with Ezi Ozoani and 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 AutoProcessor, AutoModelForAudioXVector
 
processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-superb-sv")
 
model = AutoModelForAudioXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv")
 
```
</details>

{"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"]}
transformers pytorch wav2vec2 audio-xvector speech audio audio-classification en dataset:superb arxiv:2105.01051 arxiv:1910.09700 arxiv:2006.11477 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #audio-xvector #speech #audio #audio-classification #en #dataset-superb #arxiv-2105.01051 #arxiv-1910.09700 #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

# Model Card for wav2vec2-base-superb-sv
 
 
# Model Details
 
## Model Description
 
 
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
  - Parent Model: wav2vec2-large-lv60
- Resources for more information: 
    - GitHub Repo
    - Associated Paper
 
 
# Uses
 
 
## Direct Use
 
This is a ported version of 
S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.

The base model is wav2vec2-large-lv60, which is pretrained on 16kHz 
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 

For more information refer to SUPERB: Speech processing Universal PERformance Benchmark
 
## 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
 
See the superb dataset card
 
## Training Procedure
 
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
See the superb dataset card
 
### 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]
 
 
Anton Lozhkov in collaboration with Ezi Ozoani and 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 wav2vec2-base-superb-sv # Model Details ## Model Description
 
 
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
  - Parent Model: wav2vec2-large-lv60
- Resources for more information: 
    - GitHub Repo
    - Associated Paper # Uses ## Direct Use
 
This is a ported version of 
S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.

The base model is wav2vec2-large-lv60, which is pretrained on 16kHz 
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 

For more information refer to SUPERB: Speech processing Universal PERformance Benchmark ## 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
 
See the superb dataset card ## Training Procedure ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
 
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
 
See the superb dataset card ### 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]
 
 
Anton Lozhkov in collaboration with Ezi Ozoani and 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 #wav2vec2 #audio-xvector #speech #audio #audio-classification #en #dataset-superb #arxiv-2105.01051 #arxiv-1910.09700 #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # Model Card for wav2vec2-base-superb-sv # Model Details ## Model Description
 
 
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
  - Parent Model: wav2vec2-large-lv60
- Resources for more information: 
    - GitHub Repo
    - Associated Paper # Uses ## Direct Use
 
This is a ported version of 
S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.

The base model is wav2vec2-large-lv60, which is pretrained on 16kHz 
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 

For more information refer to SUPERB: Speech processing Universal PERformance Benchmark ## 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
 
See the superb dataset card ## Training Procedure ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
 
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
 
See the superb dataset card ### 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]
 
 
Anton Lozhkov in collaboration with Ezi Ozoani and 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 #wav2vec2 #audio-xvector #speech #audio #audio-classification #en #dataset-superb #arxiv-2105.01051 #arxiv-1910.09700 #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# Model Card for wav2vec2-base-superb-sv# Model Details## Model Description
 
 
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
  - Parent Model: wav2vec2-large-lv60
- Resources for more information: 
    - GitHub Repo
    - Associated Paper# Uses## Direct Use
 
This is a ported version of 
S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.

The base model is wav2vec2-large-lv60, which is pretrained on 16kHz 
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 

For more information refer to SUPERB: Speech processing Universal PERformance Benchmark## 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
 
See the superb dataset card## Training Procedure### Preprocessing
 
More information needed### Speeds, Sizes, Times
 
More information needed# Evaluation