This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit
Sound event detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is…
We have formalized this work as a research challenges at IEEE AASP workshop DCASE. 1. In the first version of the sound event localization and detection (SELD) challenge at DCASE 2019. We provided dataset with stationary sources in multiple reverberant scenarios. 2. In the second version of the…
Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web
Sound event localization and detection (SELD) is the combined task of identifying the temporal onset and offset of a sound event, tracking the spatial location when active, and further associating a textual label describing the sound event. As part of DCASE 2019, we are organizing an SELD task…
The main features of sharathadavanne/seld-dcase2019 are: Sound Event Detection.
Open-source alternatives to sharathadavanne/seld-dcase2019 include: apple/turicreate — This project is an automated machine learning framework and toolkit designed for training and tuning custom models for… sharathadavanne/sed-crnn — Sound event detection (SED) is the task of recognizing the sound events and their respective temporal start and end… sharathadavanne/seld-net — We have formalized this work as a research challenges at IEEE AASP workshop DCASE. 1. In the first version of the… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device…