[HN Gopher] A highly efficient, real-time text-to-speech system ...
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A highly efficient, real-time text-to-speech system deployed on
CPUs
Author : moneil971
Score : 84 points
Date : 2020-05-15 16:22 UTC (6 hours ago)
(HTM) web link (ai.facebook.com)
(TXT) w3m dump (ai.facebook.com)
| thelazydogsback wrote:
| Personally, I find I dislike any "emotion" added to TTS -- I find
| Alexa's emo markup, a la:
|
| https://developer.amazon.com/en-US/blogs/alexa/alexa-skills-...
|
| to be disturbing and without much added value. (Such as used with
| games like Jeopardy.)
|
| If used, the application of these tags needs to be both
| meticulous in its proper context, somewhat non-deterministically
| applied, and with randomized prosody. Repeated usage of the same
| overstated emotive content is annoying and unnatural (worse than
| a "flat" presentation) and only serves to underscore the
| underlying inflexible conversational content.
| teilo wrote:
| I agree. All I care about is that the pronunciation is
| contextually correct, and smooth. Accents are fine, and
| necessary. But I don't want a non-human simulating human
| emotions. Now if they aren't simulated, that's another story...
| jandrese wrote:
| Honestly, how do you expect to advance if you don't like
| Genuine People Personalities(r)?
|
| Who wouldn't want a door that is eternally cheerful about
| opening up for you? Or a paranoid android?
| AnimalMuppet wrote:
| _The paranoid android_ , that's who wouldn't like a
| paranoid android. Particularly, wouldn't like _being_ one.
| ge96 wrote:
| I take it you didn't like the movie Her ha
| npunt wrote:
| Agree, there's definitely a risk for uncanny valley in voice
| assistants around tone. Emotion in particular is an area where
| I think we won't have sophisticated enough systems to address
| for some time, which means it's mostly something that makes
| things unintentionally worse. A few of these downsides include:
|
| 1. tone has a different valence than what the subject matter
| requires ('Sure thing! The funeral home number is...'), which
| reminds you of and amplifies negative feelings
|
| 2. tone is used to get something from you, which makes you feel
| manipulated
|
| 3. tone is uniformly applied, which through repeat exposure
| makes the world feel false
|
| Totally agree that randomized prosody would help aid in making
| assistants more natural; tempo and rhythm don't have as much
| emotional weight.
|
| I'm not sure explicit markup is the right way to approach the
| problem, given its lack of understanding of user context and
| state of mind, and the likelihood developers execute poorly. It
| feels like a solution at the wrong level of abstraction...
| getting in sync with a user's emotions is a pretty high bar.
| ge96 wrote:
| Impressive but also still sounds "robotic" like AWS Polly. I
| wonder if they'll fuse that tech where you can sample someone's
| voice from a paragraph and build something. Then you could hire a
| voice actor(ress) and maybe license their voice? I don't know how
| that would work.
| jandrese wrote:
| Speech Synthesis has always baffled me. You could run a
| reasonable (albeit strangely accented) version on 16Mhz Macs
| without major CPU impact. The code including sound data was less
| than a megabyte.
|
| In order to achieve modest improvements in dictation we're
| throwing entire GPU arrays at the problem. What happened in the
| middle? Was there really no room for improvement until we went
| full AI?
| Someone wrote:
| IIRC, it was _with_ major CPU impact. A 8 MHz machine couldn't
| do much else while talking.
|
| Also, the original MacinTalk sounded a lot better if you fed it
| phonemes instead of text. It didn't know how to pronounce that
| many different words, and wasn't really good at making the
| right choice when the pronunciation of a word depends on its
| meaning.
|
| For example, if you gave it the text "Read me", it always
| pronounced "Read" in the past tense. That always seemed the
| wrong bet to me, and I would think the developers had heard
| that, too, but apparently, fixing it was not that simple.
|
| I also think it didn't know to pronounce "Dr" as "Drive" or
| "Doctor", depending on context, or "St" as "Saint" or "Street",
| to mention a few examples, and probably was abysmal when you
| asked it to speak a phone book, with its zillions of rare names
| (back in the eighties, that's an area where AT&T's speech
| synthesizers excelled, I've been told)
|
| And that's just the text-to-phoneme conversion. The arts of
| picking the right intonation and speed of talking are in a
| whole different ball park; they require a kind of sentiment
| analysis.
| microtherion wrote:
| There _was_ steady improvement for decades (e.g. on a Mac, you
| can compare Fred, Victoria, Vicki, and Alex, as representatives
| of 4 generations of TTS, and there were year-over-year
| improvements as well).
|
| But the latest neural techniques have added quite a bit of
| naturalness, and their computational requirements, while high,
| are within the reach of consumer level devices.
| sdenton4 wrote:
| Lots of the usual vocoder methods were developed in the 80s,
| and kinda approximate voice as a collection of linear systems.
| The neural systems allow nonlinear outputs, and are quickly
| getting much cheaper. This is common in codecs: you get a
| computationally expensive jump on quality, followed by years of
| work making that quality jump computationally cheap.
|
| You can also combine the linear and nonlinear approaches.
| LPCNet uses a bit of each, for example: a linear lpc
| prediction, which is then corrected by a neutral net.
| IshKebab wrote:
| > a reasonable (albeit strangely accented) version
|
| I think that's a little rose tinted. Classical speech synthesis
| was _awful_ in comparison to this.
| beagle3 wrote:
| You can go even backwards - SAM (Software Automated Mouth) on
| the C64 produced understandable weirdly accented speech with
| something like 10K code+data IIRC, and a 1Mhz 6502; see e.g.
| https://discordier.github.io/sam/ (though it did take all the
| CPU, because it did software based PCM on non-PCM supporting
| hardware.... if the there was just little more control of the
| waveform generators, it would likely have been possible with 1%
| CPU or so ...)
| godelski wrote:
| That video at the end really is deep in the uncanny valley.
| microtherion wrote:
| Meh. The synthesis quality is not terrible, but calling it
| "state of the art", quality-wise, is a bit of a stretch.
| ekelsen wrote:
| Exciting to see our research making broad impact across the
| industry! https://arxiv.org/abs/1802.08435
| ajtulloch wrote:
| Absolutely, it's super impressive work (as is your later work
| with Marat :) ).
| blickentwapft wrote:
| It's a pity that all the best text to speech and speech to text
| systems are cloud based with heavy vendor lock in.
| Avi-D-coder wrote:
| Any chance of a open source implementation of this?
|
| I could really use a better tts for Linux.
| brutt wrote:
| No, it cannot be open sourced. It literally has no source to
| open.
| jandrese wrote:
| Huh? It appears to be written in PyTorch according to the
| article?
|
| The training data could also be considered source.
|
| And I agree that this is of limited use if I have to access
| it by uploading and downloading everything from Facebook
| servers. Not only do I have privacy implications, but there's
| the need for a solid fast low latency internet connection
| that I can't guarantee.
| brutt wrote:
| AI is not written, AI is trained using dataset, PyTorch,
| and lot of computer time (and manpower).
|
| Dataset is not a big problem (if you can speak, you can
| create your own). PyTorch is already open.
| qchris wrote:
| Depending on the architecture, though, it's possible to
| export the trained model into a stand-alone file that can
| be imported by somebody else's program, de-coupling the
| network's training data from model it produces.
|
| This is done pretty frequently in areas like computer
| vision and speech recognition, with the pre-trained
| weights for YOLO and Mozilla Deepspeech[0] being
| available for download. I'm not sure if the word "open-
| source" totally applies here, since as you pointed out,
| apart from downloading the dataset source might be
| tought, but OP's question might be answered by having the
| resulting models made publicly available with the source
| code of the networks they used to train and deploy it?
|
| [0] https://github.com/mozilla/DeepSpeech/releases/v0.6.0
| birdyrooster wrote:
| How long until computers can brainstorm all sorts of exciting new
| voices for characters removing the need for pesky contracts and
| royalties paid?
| bergstromm466 wrote:
| Yeah, awesome! This proprietary transcription algorithm must make
| it a hell of a lot easier for NSA databases. If this is deployed
| and used by FB so they send the finished and full transcripts of
| calls and other voice traffic [1] instead of the original audio
| to be transcribed later, it will all be more efficienct! //
| sarcasm
|
| [1] https://theintercept.com/2015/05/05/nsa-speech-
| recognition-s...
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