tortoise-tts
SillyTavern
tortoise-tts | SillyTavern | |
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145 | 76 | |
11,819 | 6,017 | |
- | 8.5% | |
8.0 | 10.0 | |
6 days ago | about 18 hours ago | |
Jupyter Notebook | JavaScript | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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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
SillyTavern
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Claude 3 beats GPT-4 on Aider's code editing benchmark β aider
Right, but it's certainly easier for people who might not even know what "API" stands for, and that's quite nifty. As far as self-hosted frontends go, I can personally recommend SillyTavern[1] in the browser, ChatterUI[2] on mobile, and ShellGPT[3] for CLI. LobeChat looks pretty cool, though! I'll definitely check it out.
[1] https://github.com/SillyTavern/SillyTavern
[2] https://github.com/Vali-98/ChatterUI
[3] https://github.com/TheR1D/shell_gpt
- FLaNK AI for 11 March 2024
- Show HN: I made an app to use local AI as daily driver
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Group chats vs online defined characters, token efficiency question
I don't think there is any enumeration for {{char}} macros. Here is some good discussion on the subject.
- SillyTavern 1.11.0 has been released
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Is possible to run local voice chat agent? If yes what GPU do i Need with 500β¬ budget?
As for SillyTavern, you need the main SillyTavern frontend and SillyTavern-extras (for TTS, STT, etc.) They're pretty easy to install. SillyTavern connects to oobabooga and SillyTavern-extras via API.
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What do you use to run your models?
Finally, no matter what backend I use, I need it to be compatible with my power-user frontend, SillyTavern. That way I always use the same UI, with the characters I created and extensions I want, e. g. web search, XTTS text-to-speech and Whisper speech recognition for real-time voice chat - and all of that local!
- SillyTavern 1.10.10 has been released
- LM Studio β Discover, download, and run local LLMs
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πΊπ¦ββ¬ LLM Comparison/Test: Mistral 7B Updates (OpenHermes 2.5, OpenChat 3.5, Nous Capybara 1.9)
SillyTavern v1.10.5 frontend (not the latest as I don't want to upgrade mid-test)
What are some alternatives?
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
bark - π Text-Prompted Generative Audio Model
character-editor - Create, edit and convert AI character files for CharacterAI, Pygmalion, Text Generation, KoboldAI and TavernAI
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
piper - A fast, local neural text to speech system
SillyTavern-extras - Extensions API for SillyTavern [Moved to: https://github.com/SillyTavern/SillyTavern-extras]
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
larynx - End to end text to speech system using gruut and onnx
SillyTavern-Extras - Extensions API for SillyTavern.