model documentation

README.md

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+---
+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>