But obviously also attend to the human matters as well, eg spend time.
On the upside, your father can choose any celebrity he wants to voice him! Tons of celeb data is publicly available (VoxCeleb 1 & 2).
Something like: - Download these texts - Record in WAV at least 48 kHz - Record each line in a separate file. - Do 3 takes of each line: flat, happy, despair
Maybe even a minimal set and a full set depending on how much effort you are willing to put in.
A plain description on how to capture a raw base which within reason and technology could be used as a baseline for the most common toolkits.
I have myself looked into this (for fun) but I felt I needed a very good understanding of the toolkits before even starting to feed in data. And for my admittedly unimportant use it seemed a huge investment to create a corpus I was not even confident would work. I ended up taking the low road and used an existing voice.
https://github.com/daanzu/speech-training-recorder
The recorder works with Python 3.6.10. Need to pip install webrtcvad also.
(Not using his voice synth, reconstructed using ML, because it should sound more natural that way ;-)
https://clinton.presidentiallibraries.us/items/show/16112 https://youtu.be/orPUQm1ZRSI
And his voice was his - even with the American accent.
https://www.news.com.au/technology/innovation/why-stephen-ha...
> “It is the best I have heard, although it gives me an accent that has been described variously as Scandinavian, American or Scottish.”
> ...
> “It has become my trademark and I wouldn’t change it for a more natural voice with a British accent.
> “I am told that children who need a computer voice want one like mine.”
Somewhere, I recall a NOVA(?) program from the mid 80s where it showed him using the speech synthesizer and the thing that he said with it that still sticks in my mind is the "please excuse my American accent". In later years he was given the opportunity to upgrade it to a more natural sounding voice - but that voice was his.
https://theweek.com/articles/769768/saving-stephen-hawkings-...
It would not surprise me if SGI’s software implementation were similar to the he most popular hardware of the 1980s.
I've been wanting to create a TTS of myself so I can take phone calls using headphones and type back what I want to say so that I don't have to yell private information out loud in public locations. Would be nice if during non-COVID times I could sit in a train seat and take phone calls completely silently.
Here's a recent work that has a good comparison of some vocoders: https://wavenode-example.github.io/
Edit: WaveRNN struck a good balance for me in the past but is not shown in the link. Tons of new work coming out though!
I'm not sure what's up with the WaveGLOW (17.1M) example in the linked wavenode comparison... The base WaveGLOW sounds reasonable, though. They're also using all female voices, which strikes me as dodgy; lower male voice pitch tracking is often harder to get right, and a bunch of comparisons without getting into harder cases or failure modes makes it seem like they're covering something up.
(I've run into a bunch of comparisons for papers in the past where they clearly just did a bad job of implementing the prior art. There should be a special circle of hell...)
I'm looking at you GAN papers.
You can set it up yourself with a bit of Python knowledge from this branch: https://github.com/talonvoice/noise/tree/speech-dataset
There are keyboard shortcuts - up/down/space to move through the list and record quickly.
If you want to use it on arbitrary text prompts, you can modify this function to return each line from a text file: https://github.com/talonvoice/noise/blob/speech-dataset/serv...
If you use this, before recording too much, do some test recordings and make sure they sound ok. Web audio can be unreliable in some browsers.
The uploaded files are named after the short name, so make sure you can correspond the short name with the original text prompts, eg with string_to_shortname().
If you aren’t easily able to do this yourself, I’d be happy to spin up an instance of it for you with text prompts of your choosing.
Also, I noted the VLC demo says it doesn't use DNS! That's awesome...
The VLC demo was using macos speech recognition. In the beta now I’m shipping my own engine+trained models based on Facebook’s wav2letter, which is going pretty well.
Is that something that would be useful to a researcher in any context? I am intrigued by the idea of having my voice preserved (you know, ego), but also am happy to donate the sound files if they would help researchers in any way for datasets.
If so: [email protected]
In general, yes, this is probably useful data in some way for speech recognition or TTS.
I would say also consider recording a variety of honest utterances of all kinds, situations, and emotions. Anger outbursts, apathetic grunts, sexual even if you so desire (hence throwaway account)... Please dont be offended by this, just thinking of all scenarios for you to decide for yourself...
https://github.com/daanzu/speech-training-recorder
Originally intended for recording data for training speech recognition models [0], it should work just as well for recording to be used for speech synthesis.
If you want to read up on the basics, check out the SV2TTS paper: https://arxiv.org/pdf/1806.04558.pdf Basically you use a speaker encoding to condition the TTS output. This paper/idea is used all over, even for speech-to-speech translation, with small changes.
There's a few open-source version implementations but mostly outdated--the better ones are either private for business or privacy reasons.
There's a lot of work on non-parallel transfer learning (aka subjects are saying different things) so TTS has progressed rapidly and most public implementations lag a bit behind the research. If you're willing to grok speech processing, I'd start with NeMo for overall simplicity--don't get distracted by Kaldi.
Edit: Important note! Utterances are usually clipped of silence before/after so take that into account when analyzing corpus lengths. The quality of each utterance is much much more important than the length--fifteen.ai's TTS is so good primarily because they got fans of each character to collect the data.