7 ms·

Mozilla Common Voice Dataset: More data, more languages

379 points by dabinat 6y ago

dang 6y ago
If curious see also 2019 https://news.ycombinator.com/item?id=19270646 https://news.ycombinator.com/item?id=19270646
intopieces 6y ago
I would love to work for Mozilla on this effort full time. I have experience in voice data collection / annotation / processing at 2 FAANG companies. Anyone have an in? Thinking of reaching out to the person on who wrote this post directly.
- codezero 6y ago
  Sounds like a good move, you may check other Mozilla threads for employees open to chatting - I did that when I was curious about a position there.
- jcims 6y ago
  How long do you think it will be before we have personalized language/reading coaches talking to us during our morning commute to the downstairs office?
- nmstoker 6y ago
  The people on those related projects seem like a great bunch of people to work with. I don't have "an in" but it's probably worth having a look over the Common Voice and Deep Speech forums on Discourse to see who the main people are. They also hang out in their Matrix Chat groups, so might be able to get in touch that way. Links are below. https://discourse.mozilla.org/c/deep-speech/247 https://discourse.mozilla.org/c/deep-speech/247 https://discourse.mozilla.org/c/voice/239 https://discourse.mozilla.org/c/voice/239 https://chat.mozilla.org/ https://chat.mozilla.org/
- ta17711771 6y ago
  Share some cool shit in their Matrix (rooms), or start a convo/give a recommendation that leads to mentioning your experience/work?
lunixbochs 6y ago
This is great! I’m always excited to see new common voice releases. As someone actively using the data I wish I could more easily see (and download lists for?) the older releases as there have been 3-4 dataset updates for English now. If we don’t have access to versioned datasets, there’s no way to reproduce old whitepapers or models that use common voice. And at this point I don’t remember the statistics (hours, accent/gender breakdown) for each release. It would be neat to see that over time on the website. I’m glad they’re working on single word recognition! This is something I’ve put significant effort into. It’s the biggest gap I’ve found in the existing public datasets - listening to someone read an audiobook or recite a sentence doesn’t seem to prepare the model very well for recognizing single words in isolation. My model and training process have adapted for that, though I’m still not sure of the best way to balance training of that sort of thing. I have maybe 5 examples of each English word in isolation but 5000 examples of each number (Speech Commands), and it seems like the model will prefer e.g. “eight” over “ace”, I guess due to training balance. Maybe I should be randomly sampling 50/5000 of the imbalanced words each epoch so the model still has a chance to learn from them without overtraining?
- scribu 6y ago
  What if you first trained a classifier that told you if the uttereance is a single word vs. multiple words? Then, based on that prediction, you would use one of two separate models. The technique you're thinking of is called oversampling and there are many other general techniques for dealing with imbalanced datasets, as it's a very common situation.
  - lunixbochs 6y ago
    Thanks, the oversampling mention gives me a good reference to start. The model itself has generalized pretty well to handle both single and multi word utterances I think, without a separate classifier, but I'm definitely not going to rule out multi-model recognition in the long run. My main issues with single words right now are: - The model sometimes plays favorites with numbers (ace vs eight) - Collecting enough word-granularity training data for words-that-are-not-numbers (I've done a decent job of this so far, but it's a slow and painful process. I've considered building a frontend to turn sentence datasets into word datasets with careful alignment)
j45 6y ago
This is really encouraging to see. So nice to see languages that have more speakers than the most commonly translated languages.
Polylactic_acid 6y ago
Why on earth are they using mp3 for the dataset? Its absolutely ancient and probably the worst choice possible. Opus is widely used for voice because it gets flawless results at minuscule bitrates. And don't tell me its because users find mp3 simpler because if you are doing machine learning I expect you know how to use an audio file.
- lunixbochs 6y ago
  Probably because they're uploading (and playing back) from a webpage and Web Audio is weird and inconsistent, so sticking to a builtin codec is probably more reliable. As someone who trains on their data, it seems usable anyway. Training on 1000 hours of Common Voice makes my model better in very clear ways. https://caniuse.com/#search=mp3 https://caniuse.com/#search=mp3 https://caniuse.com/#search=opus https://caniuse.com/#search=opus I got flac working for speech.talonvoice.com with an asm codec so they could do whatever in theory, but I do get some audio artifacts sometimes.
  - est31 6y ago
    Yeah especially compatibility with Apple browsers was very important for them. I'd added functionality to normalize audio for verification but they removed it multiple times because it didn't work on Safari for various reasons. I ended up building an extension for Firefox that normalizes the audio on the website if installed: https://github.com/est31/vmo-audio-normalizer https://github.com/est31/vmo-audio-normalizer https://addons.mozilla.org/de/firefox/addon/vmo-audio-normalizer/ https://addons.mozilla.org/de/firefox/addon/vmo-audio-normal... In general I don't think normalization should happen at the backend. It's useful for training data to have multiple loudness levels, so that the network can understand them all.
jointpdf 6y ago
Does this dataset include people with voice or speech disorders (or other disabilities)? I don’t see any mention of it in this announcement or the forums, though I haven’t looked thoroughly (yet). Examples: dysphonias of various kinds, dysarthria (e.g. from ALS / cerebral palsy), vocal fold atrophy, stuttering, people with laryngectomies / voice prosthesis, and many more. Altogether, this represents millions of people for whom current speech recognition systems do not work well. This is an especially tragic situation, since people with disabilities depend more heavily on assistive technologies like ASR. Data/ML bias is rightfully a hot topic lately, so I feel that the voices of people w/ disabilities need to be amplified as well (npi).
- dabinat 6y ago
  It’s not in the current dataset, but offering such a disordered speech dataset has been discussed. I imagine it’s something that will probably be offered at some point in future.
- sagz 6y ago
  There's g.co/euphonia for those projects
  - daanzu 6y ago
    Google is certainly doing some great work with this, both Project Euphonia and other research [0]. However, as far as I know, the Euphonia dataset is closed and only usable by Google. A Common Voice disordered speech dataset would (presumably) be open to all, allowing independent projects and research. (I would love to have access to such a dataset.) [0] https://ai.googleblog.com/2019/08/project-euphonias-personalized-speech.html https://ai.googleblog.com/2019/08/project-euphonias-personal...
  - jointpdf 6y ago
    Can you clarify what you mean by “those” projects? Because this project is supposed to be about “everyone”: >”Common Voice is part of Mozilla’s initiative to make voice recognition technologies better and more accessible for everyone.” If people with speech/voice disorders are not represented in the dataset, then this is not inclusive of everyone.
villgax 6y ago
Nice, now we need the CTC based models to run offline on low-powered devices & then pretty much all speech-to-text APIs are done for.
- lunixbochs 6y ago
  I've been working on this. I think I can reliably hit the quality ballpark of STT APIs at the acoustic model level, but not at the language model level (word probabilities) in a low-powered-way yet. Also, non-English models are _way_ behind still.
  - villgax 6y ago
    The way google & now Apple manage to run these on device is pretty neat. Google has a blog post for the same too. The recently updated Mozilla Voice dataset still lacks non-EN languages sadly.
echelon 6y ago
Data in ML is critical, and this release from Mozilla is absolute gold for voice research. This dataset and will help the many independent deep learning practitioners such as myself that aren't working at FAANG and have only had access to datasets such as LJS [1] or self-constructed datasets that have been cobbled together and manually transcribed. Despite the limited materials available, there's already some truly amazing stuff being created. We've seen a lot of visually creative work being produced in the past few years, but the artistic community is only getting started with voice and sound. https://www.youtube.com/watch?v=3qR8I5zlMHs https://www.youtube.com/watch?v=3qR8I5zlMHs https://www.youtube.com/watch?v=L69gMxdvpUM https://www.youtube.com/watch?v=L69gMxdvpUM Another really cool thing popping up are TTS systems trained from non-English speakers reading English corpuses. I've heard Angela Merkel reciting copypastas, and it's quite amazing. I've personally been dabbling in TTS as one of my "pandemic side projects" and found it to be quite fun and rewarding: https://trumped.com https://trumped.com https://vo.codes https://vo.codes Besides TTS, one of the areas I think this data set will really help with is the domain of Voice Conversion (VC). It'll be awesome to join Discord or TeamSpeak and talk in the voice of Gollum or Rick Sanchez. The VC field needs more data to perfect non-aligned training (where source and target speakers aren't reciting the same training text that is temporally aligned), and this will be extremely helpful. I think the future possibilities for ML techniques in art and media are nearly limitless. It's truly an exciting frontier to watch rapidly evolve and to participate in. [1] https://keithito.com/LJ-Speech-Dataset/ https://keithito.com/LJ-Speech-Dataset/
- indogooner 6y ago
  Curious to know why don't researchers use Audiobooks/Videos and transcript when data is not available? Is it because these do not capture different dialects/accents?
  - petargyurov 6y ago
    I suppose there might some copyright issues with such content (?)
    - 6y ago
user764743 6y ago
This is interesting. As someone who has always tons of interview data to transcribe for academic research, what TTS systems should I be looking into to help me save some time? Is Deep Speech adapted for this use?
tumetab1 6y ago
To contribute https://voice.mozilla.org/en/speak https://voice.mozilla.org/en/speak
- pjfin123 6y ago
  They make it really easy to contribute! You don't need to make an account (you can though) and you read/review short sentences. I just added 75 recordings and it only took ~30 minutes. Also if you speak other languages you can contribute in them. It would really be great if there was a comprehensive public voice dataset for people to do all sorts of interesting things with.
stergro 6y ago
The complete project is very exciting, I hope that this is really a game changer, that enables private persons and startups to create new neural networks without a big investment for the data collection. I worked for the Esperanto dataset of common voice in the last year, and we now have collected over 80 hours in Esperanto. I hope that in a year or two we'll have collected enough data to create the first usable neural network for a constructed language and maybe the first voice assistant in Esperanto too. I will train a first experimental model with this release soon.

379 points by dabinat 6y ago

dang 6y ago

If curious see also 2019 https://news.ycombinator.com/item?id=19270646 https://news.ycombinator.com/item?id=19270646

I would love to work for Mozilla on this effort full time. I have experience in voice data collection / annotation / processing at 2 FAANG companies. Anyone have an in? Thinking of reaching out to the person on who wrote this post directly.

codezero 6y ago
Sounds like a good move, you may check other Mozilla threads for employees open to chatting - I did that when I was curious about a position there.
jcims 6y ago
How long do you think it will be before we have personalized language/reading coaches talking to us during our morning commute to the downstairs office?
nmstoker 6y ago
The people on those related projects seem like a great bunch of people to work with. I don't have "an in" but it's probably worth having a look over the Common Voice and Deep Speech forums on Discourse to see who the main people are. They also hang out in their Matrix Chat groups, so might be able to get in touch that way. Links are below. https://discourse.mozilla.org/c/deep-speech/247 https://discourse.mozilla.org/c/deep-speech/247 https://discourse.mozilla.org/c/voice/239 https://discourse.mozilla.org/c/voice/239 https://chat.mozilla.org/ https://chat.mozilla.org/
ta17711771 6y ago
Share some cool shit in their Matrix (rooms), or start a convo/give a recommendation that leads to mentioning your experience/work?

lunixbochs 6y ago

This is great! I’m always excited to see new common voice releases. As someone actively using the data I wish I could more easily see (and download lists for?) the older releases as there have been 3-4 dataset updates for English now. If we don’t have access to versioned datasets, there’s no way to reproduce old whitepapers or models that use common voice. And at this point I don’t remember the statistics (hours, accent/gender breakdown) for each release. It would be neat to see that over time on the website. I’m glad they’re working on single word recognition! This is something I’ve put significant effort into. It’s the biggest gap I’ve found in the existing public datasets - listening to someone read an audiobook or recite a sentence doesn’t seem to prepare the model very well for recognizing single words in isolation. My model and training process have adapted for that, though I’m still not sure of the best way to balance training of that sort of thing. I have maybe 5 examples of each English word in isolation but 5000 examples of each number (Speech Commands), and it seems like the model will prefer e.g. “eight” over “ace”, I guess due to training balance. Maybe I should be randomly sampling 50/5000 of the imbalanced words each epoch so the model still has a chance to learn from them without overtraining?

scribu 6y ago
What if you first trained a classifier that told you if the uttereance is a single word vs. multiple words? Then, based on that prediction, you would use one of two separate models. The technique you're thinking of is called oversampling and there are many other general techniques for dealing with imbalanced datasets, as it's a very common situation.
- lunixbochs 6y ago
  Thanks, the oversampling mention gives me a good reference to start. The model itself has generalized pretty well to handle both single and multi word utterances I think, without a separate classifier, but I'm definitely not going to rule out multi-model recognition in the long run. My main issues with single words right now are: - The model sometimes plays favorites with numbers (ace vs eight) - Collecting enough word-granularity training data for words-that-are-not-numbers (I've done a decent job of this so far, but it's a slow and painful process. I've considered building a frontend to turn sentence datasets into word datasets with careful alignment)

j45 6y ago

This is really encouraging to see. So nice to see languages that have more speakers than the most commonly translated languages.

Polylactic_acid 6y ago

Why on earth are they using mp3 for the dataset? Its absolutely ancient and probably the worst choice possible. Opus is widely used for voice because it gets flawless results at minuscule bitrates. And don't tell me its because users find mp3 simpler because if you are doing machine learning I expect you know how to use an audio file.

lunixbochs 6y ago
Probably because they're uploading (and playing back) from a webpage and Web Audio is weird and inconsistent, so sticking to a builtin codec is probably more reliable. As someone who trains on their data, it seems usable anyway. Training on 1000 hours of Common Voice makes my model better in very clear ways. https://caniuse.com/#search=mp3 https://caniuse.com/#search=mp3 https://caniuse.com/#search=opus https://caniuse.com/#search=opus I got flac working for speech.talonvoice.com with an asm codec so they could do whatever in theory, but I do get some audio artifacts sometimes.
- est31 6y ago
  Yeah especially compatibility with Apple browsers was very important for them. I'd added functionality to normalize audio for verification but they removed it multiple times because it didn't work on Safari for various reasons. I ended up building an extension for Firefox that normalizes the audio on the website if installed: https://github.com/est31/vmo-audio-normalizer https://github.com/est31/vmo-audio-normalizer https://addons.mozilla.org/de/firefox/addon/vmo-audio-normalizer/ https://addons.mozilla.org/de/firefox/addon/vmo-audio-normal... In general I don't think normalization should happen at the backend. It's useful for training data to have multiple loudness levels, so that the network can understand them all.

jointpdf 6y ago

Does this dataset include people with voice or speech disorders (or other disabilities)? I don’t see any mention of it in this announcement or the forums, though I haven’t looked thoroughly (yet). Examples: dysphonias of various kinds, dysarthria (e.g. from ALS / cerebral palsy), vocal fold atrophy, stuttering, people with laryngectomies / voice prosthesis, and many more. Altogether, this represents millions of people for whom current speech recognition systems do not work well. This is an especially tragic situation, since people with disabilities depend more heavily on assistive technologies like ASR. Data/ML bias is rightfully a hot topic lately, so I feel that the voices of people w/ disabilities need to be amplified as well (npi).

dabinat 6y ago
It’s not in the current dataset, but offering such a disordered speech dataset has been discussed. I imagine it’s something that will probably be offered at some point in future.
sagz 6y ago
There's g.co/euphonia for those projects
- daanzu 6y ago
  Google is certainly doing some great work with this, both Project Euphonia and other research [0]. However, as far as I know, the Euphonia dataset is closed and only usable by Google. A Common Voice disordered speech dataset would (presumably) be open to all, allowing independent projects and research. (I would love to have access to such a dataset.) [0] https://ai.googleblog.com/2019/08/project-euphonias-personalized-speech.html https://ai.googleblog.com/2019/08/project-euphonias-personal...
- jointpdf 6y ago
  Can you clarify what you mean by “those” projects? Because this project is supposed to be about “everyone”: >”Common Voice is part of Mozilla’s initiative to make voice recognition technologies better and more accessible for everyone.” If people with speech/voice disorders are not represented in the dataset, then this is not inclusive of everyone.

villgax 6y ago

Nice, now we need the CTC based models to run offline on low-powered devices & then pretty much all speech-to-text APIs are done for.

lunixbochs 6y ago
I've been working on this. I think I can reliably hit the quality ballpark of STT APIs at the acoustic model level, but not at the language model level (word probabilities) in a low-powered-way yet. Also, non-English models are _way_ behind still.
- villgax 6y ago
  The way google & now Apple manage to run these on device is pretty neat. Google has a blog post for the same too. The recently updated Mozilla Voice dataset still lacks non-EN languages sadly.

echelon 6y ago

Data in ML is critical, and this release from Mozilla is absolute gold for voice research. This dataset and will help the many independent deep learning practitioners such as myself that aren't working at FAANG and have only had access to datasets such as LJS [1] or self-constructed datasets that have been cobbled together and manually transcribed. Despite the limited materials available, there's already some truly amazing stuff being created. We've seen a lot of visually creative work being produced in the past few years, but the artistic community is only getting started with voice and sound. https://www.youtube.com/watch?v=3qR8I5zlMHs https://www.youtube.com/watch?v=3qR8I5zlMHs https://www.youtube.com/watch?v=L69gMxdvpUM https://www.youtube.com/watch?v=L69gMxdvpUM Another really cool thing popping up are TTS systems trained from non-English speakers reading English corpuses. I've heard Angela Merkel reciting copypastas, and it's quite amazing. I've personally been dabbling in TTS as one of my "pandemic side projects" and found it to be quite fun and rewarding: https://trumped.com https://trumped.com https://vo.codes https://vo.codes Besides TTS, one of the areas I think this data set will really help with is the domain of Voice Conversion (VC). It'll be awesome to join Discord or TeamSpeak and talk in the voice of Gollum or Rick Sanchez. The VC field needs more data to perfect non-aligned training (where source and target speakers aren't reciting the same training text that is temporally aligned), and this will be extremely helpful. I think the future possibilities for ML techniques in art and media are nearly limitless. It's truly an exciting frontier to watch rapidly evolve and to participate in. [1] https://keithito.com/LJ-Speech-Dataset/ https://keithito.com/LJ-Speech-Dataset/

indogooner 6y ago
Curious to know why don't researchers use Audiobooks/Videos and transcript when data is not available? Is it because these do not capture different dialects/accents?
- petargyurov 6y ago
  I suppose there might some copyright issues with such content (?)
  - 6y ago

user764743 6y ago

This is interesting. As someone who has always tons of interview data to transcribe for academic research, what TTS systems should I be looking into to help me save some time? Is Deep Speech adapted for this use?

tumetab1 6y ago

To contribute https://voice.mozilla.org/en/speak https://voice.mozilla.org/en/speak

pjfin123 6y ago
They make it really easy to contribute! You don't need to make an account (you can though) and you read/review short sentences. I just added 75 recordings and it only took ~30 minutes. Also if you speak other languages you can contribute in them. It would really be great if there was a comprehensive public voice dataset for people to do all sorts of interesting things with.

stergro 6y ago

The complete project is very exciting, I hope that this is really a game changer, that enables private persons and startups to create new neural networks without a big investment for the data collection. I worked for the Esperanto dataset of common voice in the last year, and we now have collected over 80 hours in Esperanto. I hope that in a year or two we'll have collected enough data to create the first usable neural network for a constructed language and maybe the first voice assistant in Esperanto too. I will train a first experimental model with this release soon.