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:
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
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.
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")