Search Results for "deep learning"

ESPnet is a comprehensive end-to-end speech processing toolkit covering a wide spectrum of tasks, including automatic speech recognition (ASR), text-to-speech (TTS), speech translation (ST), speech enhancement, speaker diarization, and spoken language understanding. It uses PyTorch as its deep learning engine and adopts a Kaldi-style data processing pipeline for features, data formats, and experimental recipes. This combination allows researchers to leverage modern neural architectures while still benefiting from the robust data preparation practices developed in the speech community. ESPnet provides many ready-to-run recipes for popular academic benchmarks, making it straightforward to reproduce published results or serve as baselines for new research. ...

MockingBird is an open-source voice cloning and real-time speech generation toolkit that lets you clone a speaker’s voice from a short audio sample (reportedly as little as 5 seconds) and then synthesize arbitrary speech in that voice. It builds on deep-learning based TTS / voice-cloning technology (in the lineage of projects such as Real-Time-Voice-Cloning), but extends it with support for Mandarin Chinese and multiple Chinese speech datasets — broadening its applicability beyond English. The codebase is implemented in Python (with PyTorch) and includes modules for encoder, synthesizer, vocoder, preprocessing, and inference, as well as demo scripts and a web-server interface for easier experimentation or deployment. ...

Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. ...

DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN...