jukebox
tortoise-tts
jukebox | tortoise-tts | |
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129 | 145 | |
7,580 | 11,819 | |
0.6% | - | |
0.0 | 8.0 | |
3 days ago | 5 days ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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jukebox
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Open Source Libraries
openai/jukebox: Music Generation
- Will AI be able to create similar sounding music based off input?
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Best model for music generation?
https://github.com/openai/jukebox The demo code is there.
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Why didn't OpenAI MIT license Jukebox the same way they did CLIP?
I didn't even know about it until I heard Sam Altman casually mention it in an interview, I was expecting some basic tunes generator, but this is so amazing! I mean yeah the voices are not clear, it's muffled, but look at how far have image models progressed, if you applied the same amount of collaborative effort here, the results could be amazing! ElevenLabs showed how good and clear can AI-created voices sound. The only reason I can think of is that the Jukebox code is under view license only.
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[R] [N] Noise2Music - Diffusion models for generating high quality music audio from text prompts, by Google Research
OpenAI had this figured out 3 years ago: https://openai.com/blog/jukebox/ . You could then even define your own text. Model is open source too.
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Is music next?
They've had jukebox for a few years now, so I'm sure some new model will get released and explode overnight, like what chatGPT did.
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Mongolian Gabba Goat Techno
That already exists
- El éxito continuo de OpenAI: Y como llegaron a crear la IA más avanzada del 2023. ChatGPT.
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Implementation of Google's MusicLM in PyTorch
This model is designed to output raw audio.
However, there are many models which do output midi. That's actually much simpler, and has been done already a few years ago.
I thought OpenAI did this. But then, I might misremember, because their Jukebox actually also seems to produce raw audio (https://openai.com/blog/jukebox/).
However, midi generation is so easy, you even find it in some tutorials: https://www.tensorflow.org/tutorials/audio/music_generation
- FREE AI THINGS
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?
lucid-sonic-dreams
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
bark - 🔊 Text-Prompted Generative Audio Model
spleeter - Deezer source separation library including pretrained models.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
music-demixing-challenge-starter-kit - Starter kit for getting started in the Music Demixing Challenge.
piper - A fast, local neural text to speech system
dalle-mini - DALL·E Mini - Generate images from a text prompt
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