audiocraft
tortoise-tts
audiocraft | tortoise-tts | |
---|---|---|
37 | 145 | |
19,746 | 11,944 | |
2.2% | - | |
8.3 | 8.0 | |
16 days ago | 3 days ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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audiocraft
- [N] MusicGen - Meta's response to Google's MusicLM for text-to-music is freely available for non-commercial usage
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Open Source Libraries
facebookresearch/audiocraft/MUSICGEN: Music Generation
- Audiocraft: a library for audio processing and generation with deep learning.
- Audiocraft is a library for audio processing and generation with deep learning
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Meta Open Sources AudioCraft: Generative AI for Audio
https://github.com/facebookresearch/audiocraft/blob/main/LIC...
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This is not an infinite zoom.
I asked Audiocraft to make me a "chill hip hop beat", I used framesync.xyz to make keyframes for A1111 Deforum extension. Unfortunately, I don't have the settings file anymore, but it was pretty much just a 26s clip at 15fps (440 frames) with a single prompt "a surreal painting by Magritte" and the usual negative prompt magic voodoo. Then, for every clip I used the last frame of the previous clip as init frame. I render at 512x512 and then use ESRGAN4x to upscale to 2048x2048
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[Frostveil Series] A monk channeling its inner Ønd
However, the music was 100% AI-generated by MusicGen.
Music was entirely generated by AI using MusicGen. Video was generated using PhotoVibrance.
- Try Meta's new MusicGen text-to-audio generator here, free, up to 30 seconds in length. | Text Prompt: Van Halen Style Catchy Electric Guitar Melody Hook for intro of song with distortion
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I connected my Roland Digital Piano to GPT and MusicGen...
If you want to know more about MusicGen, https://github.com/facebookresearch/audiocraft
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?
llama - Inference code for Llama models
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
bark - 🔊 Text-Prompted Generative Audio Model
audiocraft-infinity-webui
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
gpt-producer
piper - A fast, local neural text to speech system
Stable-Diffusion - Stable Diffusion, SDXL, LoRA Training, DreamBooth Training, Automatic1111 Web UI, DeepFake, Deep Fakes, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya LoRA, Kandinsky 2, DeepFloyd IF, Midjourney
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
stable-diffusion-webui - Stable Diffusion web UI
larynx - End to end text to speech system using gruut and onnx