model documentation

README.md

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+---
+language: en
+datasets:
+- superb
+tags:
+- speech
+- audio
+- wav2vec2
+- audio-classification
+license: apache-2.0
+---
+# 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>