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tacotron2

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NVIDIA / tacotron2

tacotron2

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

AI & Machine Learning

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What it is

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

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Also in these discussions

mozilla/tts · linked in 3 of the same discussions
corentinj/real-time-voice-cloning · linked in 2 of the same discussions
keithito/tacotron · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year7 dated mentions

Observed mentions: 2018-11-19 20:58:31 UTC to 2024-05-02 08:02:58 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	1	1
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	1	1
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	1
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	2	2
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	1
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	1	1

What Hacker News says

7 sources

albertzeyer·May 2, 2024

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_...

In ESpeak-ng: speech synthesizer with more than one hundred languages and accents

CSSer·Feb 11, 2023

I think your last point may hold for this genre of music but not necessarily for pop. Pop musicians are extremely versatile. They sort of have to be to stay relevant. Take Ariana Grande for example. She could sing basically anything in any style you want[0], but you may not even have to ask her because we have efficient zero-shot voice cloning now[1]. Before you wonder if this will work for music, check out this video of AI generated Eminem track[3]. [0] https://www.youtube.com/watch?v=ss9ygQqqL2Q [1] https://arxiv.org/pdf/2301.02111.pdf [2] https://aka.ms/valle (demos from one) [3] https://www.youtube.com/watch?v=WtFNOSTTPYg [4] https://github.com/NVIDIA/tacotron2 (used to create audio for 3)

In I had ChatGPT write a Decemberists song and recorded it

pizza·Sep 25, 2021

Cool article, I do like the extensibility provided via the unix philosophy. Another thing you can do nowadays is use off-the-shelf deep neural networks to do tts eg https://github.com/NVIDIA/tacotron2 https://github.com/mozilla/TTS https://github.com/CorentinJ/Real-Time-Voice-Cloning https://github.com/coqui-ai/TTS They're not all easy to setup however

In Speech Synthesis on Linux (2020)

mm983·Jul 11, 2021

https://github.com/NVIDIA/tacotron2 https://github.com/keithito/tacotron https://github.com/NVIDIA/WaveGlow

In AI voice actors sound more human than ever–and are ready to hire

jonas21·May 16, 2020

There have been quite a few recent advances in open-source TTS, such as: https://github.com/NVIDIA/tacotron2 https://github.com/CorentinJ/Real-Time-Voice-Cloning https://github.com/mozilla/TTS I think a lot of the remaining gap is due to a lack of high-quality training data -- most of the open-source models are trained on public-domain audiobooks (e.g. LJ Speech). However, good training data (large amounts of annotated recordings by professional voice actors) is expensive to create, and unlike code, there's not a tradition of people sharing it.

In Emotionally Expressive Text to Speech

deepblue129·May 16, 2019

> have produced the most realistic AI simulation of a voice we’ve heard to date. No you didn't. Please do not lie. There are a number of projects replicating Google's Tacotron 2 research from December 2017 that achieved human parity in text-to-speech as measured by MOS score. Google's Tacotron 2 model was then successfully deployed by Google in a service called Duplex. Following up on this research, there are a number of open source and commercial projects that have used Google's Tacotron 2 human-parity TTS research: Open source projects: - https://github.com/mozilla/TTS - https://github.com/Rayhane-mamah/Tacotron-2 - https://github.com/NVIDIA/tacotron2 Commercial TTS projects: - http://deepzen.io/ - https://wellsaidlabs.com/ - https://ai.googleblog.com/2018/05/duplex-ai-system-for-natur... ------------------------------------------------

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-- > he didn’t actually endorse our work like this, it’s a clip from the video the team created featuring their work. Video and more after the jump! It's absolutely unresponsible/illegal to clone a person's voice without consent. To use Joe Rogan's likeness for your publicity stunt without his consent is unethical in its self. It's Joe Rogan's legal right to control his own likeness. Furthermore, this presents a number of safety risks to Joe Rogan including the possibility of identity fraud. -------------------------------------------------- Finally, at this time, TTS human parity technology is at human-parity when tested on phrases and sentences similar to those in the training set. Google's Tacotron-2 models showed a significant decrease in performance reading 37 news headlines. They mentioned in their evaluation: > This result points to a challenge for end-to-end approaches – they require training on data that cover intended usage.

In RealTalk Speech Synthesis

kastnerkyle·Nov 19, 2018

Worth noting that a big chunk of the core TTS code here is built on tools from other researchers like Ryuichi Yamamoto and Keith Ito, and they have great implementations to check out as well. The best quality I have heard in OSS is probably [1] from Ryuichi using the Tacotron 2 implementation of Rayhane Mamah, which is loosely what NVidia based some of their baseline code on recently as well [3][4]. There's also a colab notebook for this stuff, so you can try it directly without any pain https://colab.research.google.com/github/r9y9/Colaboratory/b... I also have my own pipeline for this (using some utilities from the above authors + a lot of my own hacks), for a forthcoming paper release here https://github.com/kastnerkyle/representation_mixing/tree/ma... , see the minimal demo. It has pretty fast sampling, but the audio quality is not a

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s high as WaveNet. I'd really like to tie in with WaveGlow, but it's work in progress for me so far. NOTE: None of these have voice adaptivity per se, but given a model which trains well already + a multispeaker dataset with IDs such as VCTK, a lot of things become possible as getting a baseline model and data pipeline for TTS is quite difficult. [0] https://github.com/keithito/tacotron [1] https://r9y9.github.io/blog/2018/05/20/tacotron2/ [2] https://github.com/Rayhane-mamah/Tacotron-2 [3] https://github.com/NVIDIA/waveglow [4] https://github.com/NVIDIA/tacotron2

In Varying Speaking Styles with Neural Text-to-Speech