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ddxk's Projects

keras-self-attention

Attention mechanism for processing sequential data that considers the context for each timestamp.

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

neural_sp

End-to-end ASR implementation with pytorch.

octaveconv_pytorch

Pytorch implementation of Octave convolution

python_speech_features

This library provides common speech features for ASR including MFCCs and filterbank energies.

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

scene-classification

未来杯高校AI挑战赛

scikit-learn

scikit-learn: machine learning in Python

sincnet

SincNet is a neural architecture for efficiently processing raw audio samples.

speaker_verification

Tensorflow implementation of generalized end-to-end loss for speaker verification

speech_recognition

中文语音识别

speech_signal_processing_and_classification

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].

tencent-open-source

腾讯开源作品整理

tensorflow_end2end_speech_recognition

End-to-End speech recognition implementation base on TensorFlow (CTC, Attention, and MTL training)

triplet-loss-train-for-speaker-recognition

It is a complete project of voiceprint recognition or speaker recognition.Before, I upload a very classic VGG based model for speaker recognition . The model simply use softmax-loss to train super-parameters. But during testing stage,we found the model is not very reliable。for example, the model can easily distinguish man-man group, and man-woman group, but difficultly in woman-woman. So, we try another method called triplet-group to retrain our model, of course, we use triplet-loss as the loss for back propagation. The I upload our core code, and training curve for the two training stage. Why, I refer to "two training stage"? That need you to understand the triplet-group method. And very very welcome to my mailbox: [email protected]

vgg-speaker-recognition

Utterance-level Aggregation For Speaker Recognition In The Wild

voiceprint-recognition-speaker-recognition

It is a complete project of voiceprint recognition or speaker recognition.

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