Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Awesome Multimodal Ml | 5,290 | 2 months ago | 8 | mit | ||||||
Reading list for research topics in multimodal machine learning | ||||||||||
Torchscale | 2,804 | 8 | 2 months ago | 5 | October 20, 2023 | 18 | mit | Python | ||
Foundation Architecture for (M)LLMs | ||||||||||
Multibench | 356 | 5 months ago | 10 | mit | HTML | |||||
[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning | ||||||||||
Nonautoreggenprogress | 290 | a year ago | 2 | cc0-1.0 | ||||||
Tracking the progress in non-autoregressive generation (translation, transcription, etc.) | ||||||||||
Speechtransprogress | 218 | 5 months ago | cc0-1.0 | |||||||
Tracking the progress in end-to-end speech translation | ||||||||||
Speech_signal_processing_and_classification | 203 | a year ago | 3 | mit | Python | |||||
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__openstt | 146 | 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 Translation | 98 | 2 years ago | ||||||||
Speechprompt | 80 | 8 months ago | 1 | Python | ||||||
**Interspeech 2022** 《SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks》Speech processing with prompting paradigm | ||||||||||
Nlp Guide | 61 | 3 months ago | Python | |||||||
Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis. |