DALLE2-pytorch
tortoise-tts
DALLE2-pytorch | tortoise-tts | |
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65 | 144 | |
10,826 | 11,755 | |
- | - | |
6.8 | 8.2 | |
3 months ago | 19 days ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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DALLE2-pytorch
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One year ago I got access to closed beta DALL-E 2.
I was showing people Dalle2 last year and telling them how much of an impact an open source solution was going to have on, well, everything to do with art and design. (At the time Stable Diffusion had not released, not even the leak, and all hopes was on https://github.com/lucidrains/DALLE2-pytorch)
- [Machinelearning] [D] Quelqu'un travaille-t-il sur l'open-sourcing de Dall-E 2 ?
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AMA (Emad here hello)
Stable diffusion is the model, MJ will use a variant and DALL-E is the old version (we have our own implementation from our distinguished fellow Lucidrains here: https://github.com/lucidrains/DALLE2-pytorch)
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An impressionist painting of an floating raccoon god, 4k, digital painting, trending on artstation
Sadly I don't think so. From what I understand the architecture is fixed to 1024x1024 pictures.
- I asked AI to turn P&R characters into muppets..
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Comparison of AI text-to-image generators
The code is open source, the model is not I believe. https://github.com/lucidrains/DALLE2-pytorch
- Protests erupt outside of DALL-E offices after pricing implementation, press photograph
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$15 for 115 “generation increments” Very expensive Beta pricing announcement. Dissapointed
Phil Wang has been fairly prolific at creating open source implementations of these text to image models. For example, here is the dalle-2 repo https://github.com/lucidrains/DALLE2-pytorch
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DALL·E Now Available in Beta
There's already an open-source implementation of DALL-E 2 (https://github.com/lucidrains/DALLE2-pytorch) and a pretrained model for it should be released within this year.
Also true for Google's Imagen, which should be even better than DALLE-2 (and faster) https://github.com/lucidrains/imagen-pytorch.
This is possible because the original research papers behind both DALLE-2 and Imagen were publicly released.
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would love to know what portion of this prompt is not allowed
The paper describing the model is public and has been implemented here, but that's not the hard part. The model likely requires months of compute and dozens of gigabytes of VRAM to train and run, likely costing several hundred thousand dollars.
tortoise-tts
- 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
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Show HN: Gdańsk AI – full stack AI voice chatbot (STT, LLM, TTS, auth, payments)
TorToiSe (https://github.com/neonbjb/tortoise-tts) produces the best quality speech of any freely available model. However, its long inference times makes it impractical for voice chatbots like Gdansk.
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
disco-diffusion
bark - 🔊 Text-Prompted Generative Audio Model
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
piper - A fast, local neural text to speech system
dalle-2-preview
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
larynx - End to end text to speech system using gruut and onnx