Universal Speech Enhancement With Score-based Diffusion
Universal Speech Enhancement With Score-based Diffusion This is the companion page of UNIVERSE, the universal speech enhancer described in the paper “Universal Speech Enhancement With Score-based Diffusion” by Joan Serrà, Santiago Pascual, Jordi Pons, R. Oguz Araz, and Davide Scaini. To access the paper, click here. In this page you will find basic information about the paper, three sets of speech
VOSK Offline Speech Recognition API
РУС 中文 Vosk is a speech recognition toolkit. The best things in Vosk are: Supports 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish, Uzbek, Korean, Breton, Gujarati. More to come. Works offlin
GitHub - xinntao/Real-ESRGAN: Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
🔥 AnimeVideo-v3 model (动漫视频小模型). Please see [anime video models] and [comparisons] 🔥 RealESRGAN_x4plus_anime_6B for anime images (动漫插图模型). Please see [anime_model] 💥 Update online Replicate demo: Online Colab demo for Real-ESRGAN: | Online Colab demo for for Real-ESRGAN (anime videos): Portable Windows / Linux / MacOS executable files for Intel/AMD/Nvidia GPU. You can find more information here
google-research/scann at master · google-research/google-research
ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale. This code release implements [1], which includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is designed for x86 processors with AVX2 support. ScaNN achieves state-of-the-art performance
GitHub - tensorflow/similarity: TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
Tensorflow Similarity offers state-of-the-art algorithms for metric learning along with all the necessary components to research, train, evaluate, and serve similarity and contrastive based models. These components include models, losses, metrics, samplers, visualizers, and indexing subsystems to make this quick and easy. With Tensorflow Similarity you can train two main types of models: Self-supe
Using PyTorch + NumPy? You're making a mistake.
Update: The problem is now fixed in Pytorch but can still happen in tensorflow-keras. Discussion on reddit. Bugs in ML code are notoriously hard to fix - they don’t cause compile errors but silently regress accuracy. Once you have endured the pain and fixed one of these, the lesson is forever etched into your brain, right? Wrong. Recently, an old foe made a comeback - a familiar bug bit me again!
2021年最強になるか！？最新の画像認識モデルEfficientNetV2を解説 - Qiita
その他層の数も探索空間に入れています。ここで拡張率とは、MBConvの最初のConvでチャネル数を何倍にするかの係数のことで、こちらでより詳しく解説しています。 探索は精度$A$、ステップごとの学習時間$S$、パラメータサイズ$P$を用いて、$A\cdot S^w\cdot P^v$を最大化するように行われます。ここで$w=-0.07, v=-0.05$であり、これらの値は実験的に決定されています。 1.3.2 EfficientNetV2のアーキテクチャ 下表がEfficientNetV2のSサイズのモデルになります。 画像: "EfficientNetV2: Smaller Models and Faster Training", Tan, M., Le, Q., (2021) 比較のためにEfficientNet-B0(i.e. V1)のアーキテクチャも下に載せます。 画像: "Ef
PyTorch, ONNX, Caffe, OpenVINO (NCHW) のモデルをTensorflow / TensorflowLite (NHWC) へお手軽に変換する - Qiita
PyTorch, ONNX, Caffe, OpenVINO (NCHW) のモデルをTensorflow / TensorflowLite (NHWC) へお手軽に変換するDeepLearningCaffeTensorFlowPyTorchONNX 日本語 English 1. はじめに いつも左中間を狙うようなプチニッチなふざけた記事ばかりを量産しています。 この記事の手順を実施すると、 最終的に PyTorch製 高精度Semantic Segmentation の U^2-Net を TensorFlow Lite へ変換することができます。 下図のような感じです。 TensorFlow めちゃくちゃ扱いにくいです。 日々公開される最新のとても面白いモデルは軒並みPyTorch実装ですし、なんでTensorFlowで実装してくれないんだ！！ と、常日頃思っています。 論文のベンチマ
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GitHub - mosaicml/composer: Supercharge Your Model Training
GitHub - magenta/mt3: MT3: Multi-Task Multitrack Music Transcription
深層学習時代の文字認識とその周辺 / OCR and related technologies in the Deep Learning era
GitHub - CorentinJ/Real-Time-Voice-Cloning: Clone a voice in 5 seconds to generate arbitrary speech in real-time
GitHub - openai/CLIP: CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
GitHub - AsuharietYgvar/AppleNeuralHash2ONNX: Convert Apple NeuralHash model for CSAM Detection to ONNX.
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
NN-512
Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription
NVIDIA L4T Base | NVIDIA NGC
Identifying Similar Images with TensorFlow
GitHub - elcorto/imagecluster: Cluster images based on image content using a pre-trained deep neural network, optional time distance scaling and hierarchical clustering.