FastPitchFormant – PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Dependencies You can install the Python dependencies with pip3 install -r requirements.txt Inference You have to download the pretrained models and put them in output/ckpt/LJSpeech/. For English single-speaker TTS, run python3 synthesize.py –text “YOUR_DESIRED_TEXT” –restore_step 1000000 –mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml The generated utterances will be put in output/result/. Batch Inference Batch inference is also supported, try python3 synthesize.py –source preprocessed_data/LJSpeech/val.txt –restore_step […]

robust_cvd This repository contains Python and C++ implementation of Robust Consistent Video Depth, as described in the paper Johannes Kopf, Xuejian Rong, and Jia-Bin Huang. Robust Consistent Video Despth Estimation. CVPR 2021 Project | Paper | Video | Colab We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate […]

locate-and-label Code for Two-stage Identifier: “Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition”, accepted at ACL 2021. Setup Requirements conda create –name acl python=3.8 conda activate acl pip install -r requirements.txt Datasets The datasets used in our experiments: Data format: { “tokens”: [“2004-12-20T15:37:00”, “Microscopic”, “microcap”, “Everlast”, “,”, “mainly”, “a”, “maker”, “of”, “boxing”, “equipment”, “,”, “has”, “soared”, “over”, “the”, “last”, “several”, “days”, “thanks”, “to”, “a”, “licensing”, “deal”, “with”, “Jacques”, “Moret”, “allowing”, “Moret”, “to”, “buy”, “out”, “their”, “women”, “‘s”, […]

DFM Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baseline for Deep Feature Matching at CVPR 2021 Image Matching Workshop. Setup Environment We strongly recommend using Anaconda. Open a terminal in ./python folder, and simply run the following lines to create the environment: conda env create -f environment.yml conda activte dfm DependenciesIf you do not use conda, DFM needs the following dependencies:(Versions are not strict; however, we have tried DFM with these specific versions.) python=3.7.1 pytorch=1.7.1 […]