Vakyansh: ASR Toolkit for Low Resource Indic languages

• URL: http://arxiv.org/abs/2203.16512v1
• Date: Wed, 30 Mar 2022 17:50:18 GMT
• Title: Vakyansh: ASR Toolkit for Low Resource Indic languages
• Authors: Harveen Singh Chadha, Anirudh Gupta, Priyanshi Shah, Neeraj Chhimwal, Ankur Dhuriya, Rishabh Gaur, Vivek Raghavan
• Abstract summary: Vakyansh is an end to end toolkit for Speech Recognition in Indic languages. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages.

Related papers

• Navigating Text-to-Image Generative Bias across Indic Languages [53.92640848303192]
  This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English.
  arXiv  Detail & Related papers  (2024-08-01T04:56:13Z)
• CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
  This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
  arXiv  Detail & Related papers  (2024-04-03T02:21:46Z)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• Scaling Speech Technology to 1,000+ Languages [66.31120979098483]
  The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. Main ingredients are a new dataset based on readings of publicly available religious texts. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, and a language identification model for 4,017 languages.
  arXiv  Detail & Related papers  (2023-05-22T22:09:41Z)
• Towards Building Text-To-Speech Systems for the Next Billion Users [18.290165216270452]
  We evaluate the choice of acoustic models, vocoders, supplementary loss functions, training schedules, and speaker and language diversity for Dravidian and Indo-Aryan languages. We train and evaluate TTS models for 13 languages and find our models to significantly improve upon existing models in all languages as measured by mean opinion scores.
  arXiv  Detail & Related papers  (2022-11-17T13:59:34Z)
• ASR2K: Speech Recognition for Around 2000 Languages without Audio [100.41158814934802]
  We present a speech recognition pipeline that does not require any audio for the target language. Our pipeline consists of three components: acoustic, pronunciation, and language models. We build speech recognition for 1909 languages by combining it with Crubadan: a large endangered languages n-gram database.
  arXiv  Detail & Related papers  (2022-09-06T22:48:29Z)
• IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages [16.121708272597154]
  We release the IndicSUPERB benchmark for speech recognition in 12 Indian languages. We train and evaluate different self-supervised models alongside a commonly used baseline benchmark. We show that language-specific fine-tuned models are more accurate than baseline on most of the tasks.
  arXiv  Detail & Related papers  (2022-08-24T20:14:52Z)
• Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users [32.23606056944172]
  We introduce Aksharantar, the largest publicly available transliteration dataset for Indian languages created by mining from monolingual and parallel corpora. The dataset contains 26 million transliteration pairs for 21 Indic languages from 3 language families using 12 scripts. Aksharantar is 21 times larger than existing datasets and is the first publicly available dataset for 7 languages and 1 language family.
  arXiv  Detail & Related papers  (2022-05-06T05:13:12Z)
• Towards Building ASR Systems for the Next Billion Users [15.867823754118422]
  We make contributions towards building ASR systems for low resource languages from the Indian subcontinent. First, we curate 17,000 hours of raw speech data for 40 Indian languages. Using this raw speech data we pretrain several variants of wav2vec style models for 40 Indian languages.
  arXiv  Detail & Related papers  (2021-11-06T19:34:33Z)
• CLSRIL-23: Cross Lingual Speech Representations for Indic Languages [0.0]
  CLSRIL-23 is a self supervised learning based 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. We compare the language wise loss during pretraining to compare effects of monolingual and multilingual pretraining.
  arXiv  Detail & Related papers  (2021-07-15T15:42:43Z)
• CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus [57.641761472372814]
  CoVoST is a multilingual speech-to-text translation corpus from 11 languages into English. It diversified with over 11,000 speakers and over 60 accents. CoVoST is released under CC0 license and free to use.
  arXiv  Detail & Related papers  (2020-02-04T14:35:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.