:link: Some useful websites for programmers.
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mpariente Repositories
This repo contains the scripts, models and required files for the Interspeech 2020 Deep Noise Suppression (DNS) Challenge. We are open sourcing clean speech and noise files as well. Participants of this challenge will use the scripts from this repo to create data to train their noise suppressors. They will compare their method with our baseline noise suppressor and report the results.
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Implementation for paper "iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning"
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mpariente/LibriMixPython
An open source dataset for source separation
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mpariente/MytestPython
First repository
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mpariente/OrdNMFPython
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Ranger - a synergistic optimizer using RAdam (Rectified Adam) and LookAhead in one codebase
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mpariente/SoundCardPython
A Pure-Python Real-Time Audio Library
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A must-read paper for speech separation based on neural networks
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mpariente/angelJava
A Flexible and Powerful Parameter Server for large-scale machine learning
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mpariente/audioPython
simple audio I/O for pytorch
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Build the Linux Kernel and Modules on board the NVIDIA Jetson Nano Developer Kit
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mpariente/data-engineering-workshops-2026Jupyter Notebook
https://github.com/DataTalksClub
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mpariente/data-engineering-zoomcampJupyter Notebook
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼
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mpariente/datasetsPython
🤗 Fast, efficient, open-access datasets and evaluation metrics in PyTorch, TensorFlow, NumPy and Pandas
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mpariente/denoiserPython
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
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mpariente/espnetPython
End-to-End Speech Processing Toolkit
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mpariente/fastmpJupyter Notebook
We'll see what this becomes
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Header-only library for using Keras models in C++.
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This is the code for the "How to Deploy a Keras Model to Production" by Siraj Raval on Youtube
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