I assume that the capabilities of text-to-speech continue to increase, but I've been unable to find something that can do a decent job of interpreting scientific units and notation and therefore dictate a scientific article, to a quality comparable to that you would get if you were attending a talk for example.
I would also want something that can create mp3s, and works for Mac .. and this presumably narrows the options.
Related
I need a minimum of 3/4 different tts voice but unfortunatenly I have only one voice.
This because I have only one Italian neural voice (Diego) and the others are all standard voice and the quality is much worse.
The final objective is create a voice over for 3/4 persons minimum and I can't use the some exact voice.
For this reason, I like to create some variant started by the only one neural voice that I have, that gives the impression of a voice of other people all of this without seem unnatural.
Actually I have Adobe Audition, Audacity , Ircam Trax, ffmpeg and apart this I can use SSML with API (in this case microsoft Azure).
I don't known what are the effects and in what measure use it without damage the voices.
In short I ask what is the best way to do using the software that I have or other if I will get better results.
Thanks !
what language are you using? If you are using English, I am sure you can find more than 3-4 neural voices. There are en-US, en-GB, en-CA, en-AU neural voices and all sound natural.
You can also tune the pitch using SSML to make the voice sound different.
If you would like to create different voices, try customvoice.ai with your speech data (or your voice talents).
or, what are the particular 'variances' you are looking for?
I went through the documentation of Google Text to Speech SSML.
https://developers.google.com/assistant/actions/reference/ssml#prosody
So there is a tag called <Prosody/> which as per the documentation of W3 Specification can accept an attribute called duration which is a value in seconds or milliseconds for the desired time to take to read the contained text.
So <speak><prosody duration='6s'>Hello, How are you?</prosody></speak> should take 3 seconds for google text to speech to speak this! But when i try it here https://cloud.google.com/text-to-speech/ , its not working and also I tried it in rest API.
Does google text to speech doesn't take duration attribute into account? If they don't then is there a way to achieve the same?
There are two ways I know of to solve this:
First Option: call Google's API twice: use the first call to measure the time of the spoken audio, and the second call to adjust the rate parameter accordingly.
Pros: Better audio quality? (this is subjective and depends on taste as well as the application's requirements)
Cons: Doubles the cost and processing time.
Second option:
Post-process the audio using a specialized library such as ffmpeg
Pros: Cost effective and can be fast if implemented correctly.
Cons: Some knowledge of the concepts and the usage of an audio post-processing library is required (no need to become an expert though).
As Mr Lister already mentioned, the documentation clearly says.
<prosody>
Used to customize the pitch, speaking rate, and volume of text
contained by the element. Currently the rate, pitch, and volume
attributes are supported.
The rate and volume attributes can be set according to the W3
specifications.
Using the UI interface you can test it.
In particular you can use things like
rate="low"
or
rate="80%"
to adjust the speed. However that is as far as you can go with Google TTS.
AWS Polly does support what you need, but only on Standard voices (not Neural).
Here is the documentation.
Setting a Maximum Duration for Synthesized Speech
Polly also has a UI to do a quick test.
Does needing just a single word voice recognition reduce the complexity of the task enough to be able to fully perform voice recognition processing offline, on an iOS or Android smartphone? (E.g., could a reasonably accurate counter for the number of times that a single, pre-programmed word was spoken while the microphone is active be developed to work offline on a standard iOS or Android smartphone?).
I've found plenty of tools and examples capturing voice and sending it to an online service (e.g., the Google cloud voice-to-text), but does the single-word focus reduce the complexity enough for the recognition to be doable offline today? If so, do you have any libraries to suggest or where would you start?
Cloud services are good for various reasons relating to your question:
It makes deployment of new versions of the algorithm (which happen much more frequently than most people realize) a lot easier
It allows the developer to collect your data and use it in future algorithm development (or whatever they please)
From a practical standpoint, most deployed models (at least the effective ones) can be quite large and take up quite a bit of space on a mobile device.
In addition to the above, I don't think that the singular word focus changes much, if anything. The model has to not just account for words, but also for the different ways those words can be said (volume, tone, accents, inflection, etc, etc).
So what you are asking can be done but there's also good reasons why it's on the cloud.
Are there any easy-to-use free or cheap speech synthesis libraries for PIC and/or ARM embedded systems where code size is more important than speech quality? Nowadays it seems that a 1 meg package is considered "compact", but a lot of microcontrollers are smaller than that. Back in the 1980's Apple hired a contractor to produce Macintalk, which offered reasonable-quality speech in a 26K package which ran on a 7.16MHz 68000, and a program called SAM could produce speech that wasn't quite as good, but still serviceable, with a 16K package that ran on a 1MHz 6502. The SpeakJet runs a speech-synthesis algorithm on some type of PIC.
I probably wouldn't particularly need to produce speech, but would want to be able to speak messages formed from a number of pre-set words. Obviously it would be possible to simply prerecord all the messages, but with a vocabulary of e.g. 100 words, I would think that storing 16K worth of code plus maybe 1K worth of phonetic strings would be more compact than storing audio for 100 words.
Alternatively, if I wanted to store audio for 100 words, what would be the best way of generating a set of words that would flow naturally together? On older-style speech synthesizers, any given word could be spoken three ways: neutral inflection, falling inflection (as if followed by a period), or rising inflection (followed by a question mark). Words with neutral inflection could be spliced together in any order and sound fine. The text-to-wave tools I've found, though, seem to like to add finer details of inflection which sound "off" if words are cut apart and resequenced. Are there any tools which are designed for producing waves that can be concatenated and spliced nicely? If I do use such a tool, what audio format would be best for storing the waves so as to allow efficient decoding on a small microcontroller?
Last time I did this I was able add hardware like:http://www.sparkfun.com/products/9578 . There may be patent liabilities in your environment, like I ran into, that force a commercial software stack or OTS chip.
Otherwise, I've used http://www.speech.cs.cmu.edu/flite/ for more lenient projects, and it worked well.
You know those movies where the tech geeks record someone's voice, and their software breaks it into phonemes? Which they can then use to type in any phrase, and make it seem as if the target is saying it?
Does that software exist in an API Version? I don't even know what to Google.
There is no such software. Breaking arbitrary speech into its constituent phonemes is only a partially solved problem: speech-to-text software is still imperfect, as is text-to-speech.
The idea is to reproduce the timbre of the target's voice. Even if you were able to segment the audio perfectly, reordering the phonemes would produce audio with unnatural cadence and intonation, not to mention splicing artifacts. At that point you're getting into smoothing, time-scaling, and pitch correction, all of which are possible and well-understood in theory, but operate poorly on real-world data, especially when the audio sample in question is as short as a single phoneme, and further when the timbre needs to be preserved.
These problems are compounded on the phonetic side by allophonic variation in sounds based on accent and surrounding phonemes; in order to faithfully produce even a low-quality approximation of the audio, you'd need a detailed understanding of the target's language, accent, and speech patterns.
Furthermore, your ultimate problem is one of social engineering, and people are not easy to fool when it comes to the voices of people they know. Even with a large corpus of input data, at best you could get a short low-quality sample, hardly enough for a conversation.
So while it's certainly possible, it's difficult; even if it existed, it wouldn't always be good enough.
SRI International (the company that created Siri for iOS) has an SDK called EduSpeak, which will take audio input and break it down into individual phonemes. I know this because I sat through a demo of the product about a week ago. During the demo, the presenter showed us an application that was created using the SDK. The application gave a few lines of text for the presenter to read. After reading the text, the application displayed a bar chart where each bar represented a phoneme from his speech. The height of each bar represented a score of how well each phoneme was pronounced (the presenter was not a native English speaker, so he received lower scores on certain phonemes compared to others). The presenter could also click on each individual bar to have only that individual phoneme played back using the original audio.
So yes, software exists that divides audio up by phoneme, and it does a very good job of it. Now, whether or not those phonemes can be re-assembled into speech is an open question. If we end up getting a trial version of the SDK, I'll try it out and let you know.
If your aim is to mimic someone else's voice, then another attitude is to convert your own voice (instead of assembling phonemes). It is (surprisingly) called voice conversion, e.g http://www.busim.ee.boun.edu.tr/~speech/projects/Voice_Conversion.htm
The technology is called "voice synthesis" and "voice recognition"
The java API for this can be found here Java voice JSAPI
Apple has an API for this Apple speech
Microsoft has several ...one is discussed here Vista speech
Lyrebird is a start-up that is working on this very problem. Given samples of a person's voice and some written text, it can synthesize a spoken version of that written text in the voice of the person in the samples.
You can get interesting voice warping effects with a formant-aware pitch shift. Adobe Audition has a pretty good implementation. Antares produces some interesting vocal effects VST plugins.
These techniques use some form of linear predictive coding (LPC) to treat the voice as a source-filter model. LPC works on speech signals by estimating the resonance of the vocal tract (formant), reversing its effect with an inverse filter, and then coding the resulting residual signal. The residual signal is ideally an impulse train that represents the glottal impulse. This allows the scaling of pitch and formants independently, which leads to a much better gender conversion result than simple pitch shifting.
I dunno about a commercially available solution, but the concept isn't entirely out of the range of possibility. For example, the University of Delaware has fairly decent software for doing just that.
http://www.modeltalker.com