Flowise
tortoise-tts
| Flowise | tortoise-tts | |
|---|---|---|
| 21 | 145 | |
| 25,017 | 11,881 | |
| 9.0% | - | |
| 9.9 | 8.0 | |
| 1 day ago | 9 days ago | |
| TypeScript | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Flowise
- FLaNK Stack Weekly 12 February 2024
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Docker Image not running. Error Command failed with exit code 127
The GitHub repo is: https://github.com/FlowiseAI/Flowise
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Show HN: Rivet – open-source AI Agent dev env with real-world applications
- https://github.com/FlowiseAI/Flowise
It's absolutely ok if the answer is "Yes", I think that in this hot market each product will find a place. And competition is also motivate :)
It would be also nice to add Rivet here:
- Show HN: ChainForge, a visual tool for prompt engineering and LLM evaluation
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Bing claims my Yamaha sound bar has a 3.5 mm mini-jack, and when the error is pointed out it doubles down by inventing a reference to a non-existing manual, including a firmware update adding a physical 3.5 mm input port
If you're doing anything more custom or advanced than the regular ChatGPT type of interface can handle, you can use Flowise to build your own bot with any number of advanced plugins like internet search, calculators, recursion, file read/write access, long-term memory, other AI's...
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How to add SystemMessage to ConversationalRetrievalQAChain?
Dove into Flowise Docs for a useful example.
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How to create a Langchain application that can Chat with multiple large JSON files
For rapid prototyping try Flowise
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What exactly is AutoGPT?
Flowise as well, you are right!
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LocalAI v1.18.0 release!
Flowise
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April 2023
Drag & drop UI to build your customized LLM flow using LangchainJS (https://github.com/FlowiseAI/Flowise)
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?
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
llama.go - llama.go is like llama.cpp in pure Golang!
bark - 🔊 Text-Prompted Generative Audio Model
chatbot-ui - AI chat for every model.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
rivet-example
piper - A fast, local neural text to speech system
deepdoctection - A Repo For Document AI
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
hugo-quick-start - Hugo Quick Start on Render
larynx - End to end text to speech system using gruut and onnx