flowtron
tortoise-tts
flowtron | tortoise-tts | |
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6 | 145 | |
881 | 11,819 | |
0.3% | - | |
0.0 | 8.0 | |
10 months ago | 5 days ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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flowtron
- [D] What is the best open source text to speech model?
- A thought: we need language and voice synthesis models as free as Stable Diffusion
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Ask HN: Best FOSS software to read text allowed
If you want free (as open source) software, the NVIDIA research GitHub also has some good tools. For example : https://github.com/NVIDIA/flowtron
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Visas Marr on the tragedy of Darth Plagueis
Voice in this video was synthesized using a Flowtron trained on Visas' speech patterns.(https://github.com/NVIDIA/flowtron)
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Bastila Shan reads the Sith and Jedi Codes
The voicelines in this video was created using a Flowtron Text-to-Speech (TTS) model trained on Bastila's voice patterns to read the Sith and Jedi Codes. For more information: https://github.com/NVIDIA/flowtron I created a small tutorial for how to use it on Google Colab: https://www.youtube.com/watch?v=1Bmg1c5U5Bg
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I created a Text-to-Speech model based on Bastila's voice patterns.
For more information on Flowtron: https://github.com/NVIDIA/flowtron/
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?
TensorFlowTTS - :stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
tacotron - A TensorFlow implementation of Google's Tacotron speech synthesis with pre-trained model (unofficial)
bark - π Text-Prompted Generative Audio Model
espnet - End-to-End Speech Processing Toolkit
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
WaveRNN - WaveRNN Vocoder + TTS
piper - A fast, local neural text to speech system
espeak-ng - eSpeak NG is an open source speech synthesizer that supports more than hundred languages and accents.
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
larynx - End to end text to speech system using gruut and onnx