GPEN
tortoise-tts
GPEN | tortoise-tts | |
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18 | 145 | |
2,296 | 11,819 | |
- | - | |
1.1 | 8.0 | |
5 months ago | 2 days ago | |
Jupyter Notebook | Jupyter Notebook | |
- | Apache License 2.0 |
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GPEN
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Best FREE AI tool for improving old photos?
I like GPEN. If you have a GPU, it should run through those 500 photos in no time.
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π SD 1.5 + Thin Plate Spline + GPEN + Voice.ai
Found it today - so I thought I'd give it a go. https://github.com/yangxy/GPEN
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Scarlett Johansen. I just discovered the eye fix, and it makes all the difference.
https://github.com/yangxy/GPEN is very good, I recommend everyone to try it out.
- Use ARC to repair your goofy faces.
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GFP-GAN is an AI for restoring images of human faces that may be useful for DALL-E 2 human face images. The first image was generated by GFP-GAN, using as input the 2nd image, which was generated by another person using DALL-E 2. Links in a comment.
A similar one is GPEN
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upscappling old Tita spirite with GPEN AI. Lol
Github: https://github.com/yangxy/GPEN
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I ran the 'cursed image' through a GAN Face Restoration Deeplearning AI and... I'm so sorry.
Thank you! It's a free deep learning repository on Github called: "GAN Prior Embedded Network for Blind Face Restoration in the Wild". It's not the most user-friendly if you have no prior experience with Python Anaconda, but once you understand the basics on working with AI's in Python it becomes a lot easier! There's always the Google Colab version too, which some people prefer. I'd recommend Bycloud on Youtube if you want to learn more about AI's like thisπ
- can someone pls make this old photo of my mom dad photo from the 60s new again? only one I got and that too faded. pls pls pls.
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Can anyone fix the color distortion? Old photo of my grandmother I want to have framed
Thanks. I used GPEN. Had to do some tweaking afterwards but then again you almost always have to:)
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How can I fix this image ? Itβs very low resolution.
I upscaled it for you using GPEN
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
What are some alternatives?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
CodeFormer - [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
bark - π Text-Prompted Generative Audio Model
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
piper - A fast, local neural text to speech system
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
larynx - End to end text to speech system using gruut and onnx
TensorFlowTTS - :stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
SillyTavern - LLM Frontend for Power Users.
vits - VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
StarGANv2-VC - StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion