This media is not supported in your browser
VIEW IN TELEGRAM
Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥
> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡
Three-step approach to TTS:
> Audio tokenization using WavTokenizer (75 tok per second).
> CTC forced alignment for word-to-audio token mapping.
> Structured prompt creation w/ transcription, duration, audio tokens.
https://huggingface.co/OuteAI/OuteTTS-0.1-350M
@Machine_learn
> Pure language modeling approach to TTS
> Zero-shot voice cloning
> LLaMa architecture w/ Audio tokens (WavTokenizer)
> BONUS: Works on-device w/ llama.cpp ⚡
Three-step approach to TTS:
> Audio tokenization using WavTokenizer (75 tok per second).
> CTC forced alignment for word-to-audio token mapping.
> Structured prompt creation w/ transcription, duration, audio tokens.
https://huggingface.co/OuteAI/OuteTTS-0.1-350M
@Machine_learn
Constrained Diffusion Implicit Models!
We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!
Paper: arxiv.org/pdf/2411.00359
Demo: https://t.co/m6o9GLnnZF
@Machine_learn
We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!
Paper: arxiv.org/pdf/2411.00359
Demo: https://t.co/m6o9GLnnZF
@Machine_learn
📃 Plant-based anti-cancer drug discovery using computational approaches
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.
📖 book
💠 @Machine_learn
📖 book
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Papers
با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات 2و ۳ خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن. همچنین امکان ریکام‌دادن بعد اتمام کار وجود داره.
💠 💠
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.
🔸 Target Journal:
Nature, Scientific Reports
@Raminmousa
@Machine_learn
https://www.tgoop.com/+SP9l58Ta_zZmYmY0
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.
Nature, Scientific Reports
@Raminmousa
@Machine_learn
https://www.tgoop.com/+SP9l58Ta_zZmYmY0
Please open Telegram to view this post
VIEW IN TELEGRAM
Telegram
Papers
در اين كانال قرار مقالاتي كه كار ميكنيم رو به اشتراك بزاريم.
قرار از هم حمايت كنيم و كارهاي جديدي
ارائه بديم
@Raminmousa
قرار از هم حمايت كنيم و كارهاي جديدي
ارائه بديم
@Raminmousa
Foundations Of The Theory Of Probability by
Andrey Nikolaevich Kolmogorov
🔥🔥🔥
Read the book
@Machine_learn
Andrey Nikolaevich Kolmogorov
🔥🔥🔥
Read the book
@Machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
📃A Comprehensive Review of Propagation Models in Complex Networks: From Deterministic to Deep Learning Approaches
📎 Study paper
🔺 @Machine_learn
📎 Study paper
Please open Telegram to view this post
VIEW IN TELEGRAM
The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 game
🖥 Github: https://github.com/farama-foundation/arcade-learning-environment
📕 Paper: https://arxiv.org/abs/2410.23810v1
⚡️ Dataset: https://paperswithcode.com/dataset/mujoco
@Machine_learn
⚡️ Dataset: https://paperswithcode.com/dataset/mujoco
@Machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM
DeepArUco++: improved detection of square fiducial markers in challenging lighting conditions
🖥 Github: https://github.com/avauco/deeparuco
📕 Paper: https://arxiv.org/pdf/2411.05552v1.pdf
⚡️ Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
⚡️ Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
Please open Telegram to view this post
VIEW IN TELEGRAM