Indic Num2words

Python library for converting numbers to words for all Indian Languages.
Alternatives To Indic Num2words
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Awesome Multimodal Ml5,290
2 months ago8mit
Reading list for research topics in multimodal machine learning
Torchscale2,80482 months ago5October 20, 202318mitPython
Foundation Architecture for (M)LLMs
Multibench356
5 months ago10mitHTML
[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning
Nonautoreggenprogress290
a year ago2cc0-1.0
Tracking the progress in non-autoregressive generation (translation, transcription, etc.)
Speechtransprogress218
5 months agocc0-1.0
Tracking the progress in end-to-end speech translation
Speech_signal_processing_and_classification203
a year ago3mitPython
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
Zzz Retired__openstt146
8 years ago
RETIRED - OpenSTT is now retired. If you would like more information on Mycroft AI's open source STT projects, please visit:
Awesome Speech Translation98
2 years ago
Speechprompt80
8 months ago1Python
**Interspeech 2022** 《SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks》Speech processing with prompting paradigm
Nlp Guide61
3 months agoPython
Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis.
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