README.md

---
License: MIT
language: 
- multilingual

tags:
- wav2vec2
- automatic-speech-recognition
---

# Model Card for vakyansh-wav2vec2-indian-english-enm-700
 
# Model Details
 
## Model Description

The model creators note in the [associated paper](https://arxiv.org/pdf/2107.07402.pdf):

> The model is a self supervised learning based audio pre-trained model which learns cross lingual speech representations from raw audio across 23 Indic languages. It is built on top of wav2vec 2.0 which is solved by training a contrastive task over masked latent speech representations and jointly learns the quantization of latents shared across all languages. 

 
- **Developed by:** Harveen Singh Chadha
- **Shared by [Optional]:** Harveen Singh Chadha

- **Model type:** Automatic Speech Recognition
- **Language(s) (NLP):** More information needed
- **License:** MIT
- **Parent Model:** [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base)
- **Resources for more information:** 
 - [GitHub Repo](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation)
 - [Associated Paper](https://arxiv.org/abs/2107.07402)




# 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

The model creators note in the [associated paper](https://arxiv.org/pdf/2107.07402.pdf):
> All our data has been processed through the open sourced framework called Vakyansh . The basic steps of the process are -
1.) Download and convert audio to wav format with sample rate 16000, number of channels 1 and bit rate per sample of 16.
2.) We split an audio into voiced chunks using voice activity detection . We make sure that all the voiced chunks lie between 1 and 30 seconds.
3.) To detect and reject noisy samples we use a signal to noise ratio (SNR) approach described by [Kim and Stern, 2008]. We consider any audio sample below a SNR value of 25 as noise and do not include them in training data.
4.) We perform speaker and gender identification on our audio data. A high level representation of voice is learnt using a voice encoder based on [Wan et al., 2020]. For each audio sample the voice encoder creates a 256 dimensional encoding that summarizes characteristics of the spoken voice. For gender identification we train a support vector machine algorithm on the embedding with manually labelled data.

> Our goal for speaker identification was to get a sense of the number of speakers in a particular audio source. To estimate we use a hierarchical clustering approach to cluster similar embeddings in the sense of cosine similarity. The number of speakers are thus the number of clusters.
 
 
## Training Procedure

 
### Preprocessing
 
More information needed 


 
### Speeds, Sizes, Times
 
More information needed 


 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed
 
### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **Hardware Type:** 8 Tesla V100 GPUs
- **Hours used:** 10,000
- **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:**
 
More information needed
```bibtex 
@misc{chadha2022vakyansh,
 title={Vakyansh: ASR Toolkit for Low Resource Indic languages},
 author={Harveen Singh Chadha and Anirudh Gupta and Priyanshi Shah and Neeraj Chhimwal and Ankur Dhuriya and Rishabh Gaur and Vivek Raghavan},
 year={2022},
 eprint={2203.16512},
 archivePrefix={arXiv},
 primaryClass={cs.CL}
}
```

 
# Glossary [optional]
 
More information needed

# More Information [optional]
More information needed 

# Model Card Authors [optional]
 
Harveen Singh Chadha 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, AutoModelForCTC

processor = AutoProcessor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")

model = AutoModelForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")
 ```
</details>