Wav2vec2 Base Superb Sv

🚀 wav2vec2-base-superb-sv Model Card

This model card provides detailed information about the wav2vec2-base-superb-sv model, including its development, usage, and technical specifications.

🚀 Quick Start

Use the code below to get started with the model.

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")

✨ Features

• Ported Version: This is a ported version of S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.
• Base Model: The base model is wav2vec2-large-lv60, pretrained on 16kHz sampled speech audio.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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")

📚 Documentation

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: More information needed
• 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

• 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

📄 License

The model is licensed under the Apache 2.0 license.