How to train with 18 hours of annotated audio

[JRMeyer]

>>> joshua.eisenberg
[June 10, 2019, 4:23pm]

Hey all,

Love the mozilla TTS library. Previously I've used the 185k iterations
model, but now I want to train my own model. Given
's suggestion,
mozilla/TTS#165, I had 18 hours of audio
produced by a voice actor, recorded to WAV files, that have been
annotated, and they are each one or two sentences long.

My question is, how do I actually use the TTS library to train on my
corpus? Can someone point me to the right approach? I'm guessing it's
not as simple as pointing to a corpus manifest, but I could be wrong. I
couldn't find anything about how to set up my workspace for training, so
I wanted to see if anyone here had any tips for training a new model
with mozilla TTS.

Thanks!

[This is an archived TTS discussion thread from discourse.mozilla.org/t/how-to-train-with-18-hours-of-annotated-audio]

[JRMeyer]

>>> aolney
[June 11, 2019, 12:36am]

I'm still working on this myself, but the easiest way (IMHO) is to
reformat the data using the conventions of a well-known dataset. I'm
using LJSpeech. Just put your data in that format and point TTS at it.
Also see the wiki post on
this topic. The diagnostic notebooks it references are very useful for
finding problems in your dataset. Since yours was carefully curated, you
probably don't have many problems, but I'd still recommend checking.

[Archived Post]

[JRMeyer]

>>> joshua.eisenberg
[June 11, 2019, 2:47pm]

Good to know other people are working on this too! I'll report back with
what I tried. Time to reformat / repackage the data into the right holes
/ names / manifests. I'm also curious how long training will take. I
think that I read around 185K iterations of learning has led to some of
the best tts models for this system. I wonder how long each iteration is
!

[Archived Post]

[JRMeyer]

>>> aolney
[June 11, 2019, 2:55pm]

I'm doing a run on a 1080ti right now, and it's done about 60K
iterations in 15 hours. The speech is somewhat intelligible but not
great yet
![:slight_smile:](

[Archived Post]

[JRMeyer]

>>> joshua.eisenberg
[June 11, 2019, 3:04pm]

Whew. That's reassuring. I'm just glad it doesn't take a day for 3
iterations hehehe. good news.

[Archived Post]

[JRMeyer]

>>> nmstoker
[June 11, 2019, 3:40pm]

I agree with Andrew that following LJSpeech is a good way to go.

If you have time it might be worth actually trying to train with the
LJSpeech dataset first as then you'll have ironed out basic issues and
know what's a reasonable time / outcome on your hardware - if you jump
straight into a new dataset with a process you're not familiar with, it
compounds the challenges of figuring out where problems are! The CoLab
here (link from Readme) is a good start for that:
https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b slash
Plus you'll then have an example of LJSpeech format right there in front
of you, so you know what you're aiming for.

[Archived Post]

[JRMeyer]

>>> aolney
[June 18, 2019, 7:20pm]

FWIW I've decided I have to manually clean my data, so I've built a
web-based tool to facilitate that process. Here's the blog post with
links to GitHub and walk-through video:
>

[Archived Post]

[JRMeyer]

>>> joshua.eisenberg
[June 18, 2019, 8:01pm]

{.avatar

> built a web-based tool to facilitate that process. Here's the blog
> post with links to GitHub and walk-through video:
>

Awesome. I will definitely test this out in a couple weeks. I'm actually
having two data sets being created now. One with a voice actor, and one
with clips of speech harvested from the internet. I think this tool will
be super useful for the speech harvested from the internet

!!! slash
cheers

[
[[guitarplayersachin

4:15am

Hey, Any updates on this, were you able to get good results ?

[Archived Post]