--- 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.
Click to expand ```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") ```