Voice-Cloning-App
tortoise-tts
Voice-Cloning-App | tortoise-tts | |
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18 | 145 | |
1,247 | 11,819 | |
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0.0 | 8.0 | |
about 1 year ago | 4 days ago | |
Python | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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Voice-Cloning-App
- AI-genereeritud Politseikroonika
- Making Voices For System Members
- [Discussion] Is there any open-source alternative to voice.ai ? Looking for open-source speech to speech AI
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Voice actor I need died a decade ago. Is there a program which can create text-to-voice with the voice of a specific person through providing the software voice samples to work from?
I then feed the audio and transcriptions into this tool which handles assembling the dataset, and training s voice from that dataset.
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Trying to get it working
This Voice CLoning App is much easier to use then others, maybe you can give it a try?
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What's the most effective voice-cloning tool these days?
Although I had to fix some errors before I got it to work (mainly due to my data containing characters that do not appear in the alphabet), I achieved exceptional results with https://github.com/BenAAndrew/Voice-Cloning-App. It uses Tacotron2 and has a useful web GUI.
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Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices
Nice to see this here - Silero is also the engine that powers the "dataset builder" for Voice-Cloning-App (https://github.com/BenAAndrew/Voice-Cloning-App), a GUI TTS system that modifies Tacotron2 slightly.
Just sharing the links in case others are new to the space and keen to tinker on some solid open-source offerings.
- Show HN: Voice Clones for Creators
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Is there a guide to train talknet locally?
If you have an Nvidia card you can try this one
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Accel World Post Anime Fan Adaptation Progress Update #2: DeepFake AI Voice Creation Going Well. Looking for help.
Voice cloning app github:https://github.com/BenAAndrew/Voice-Cloning-App
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?
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Voice_cloner - A guide to clone anyone's voice and use it as a text-to-speech with android
bark - π Text-Prompted Generative Audio Model
vall-e - An unofficial PyTorch implementation of the audio LM VALL-E
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
Awesome-DeepFake-Learning - The approach I work on DeepFake.
piper - A fast, local neural text to speech system
silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
larynx - End to end text to speech system using gruut and onnx