8 ms·

DeepSpeech 0.6

278 points by pionerkotik 7y ago

tobylane 7y ago
I'm starting to look into the MycroftAI. It sounds like with this release I could use Mycroft+DeepSpeech on a new RaspberryPi for a completely offline smart speaker, do I understand that right?
- zitterbewegung 7y ago
  Yes, you are correct tensorflow lite can be done on the raspberry pi. https://www.tensorflow.org/lite/guide/build_rpi https://www.tensorflow.org/lite/guide/build_rpi
- reubenmorais 7y ago
  Correct. The latest release with the TensorFlow Lite model runs in real time on a Raspberry Pi 4. I'm not sure if the Mycroft integration is updated to the latest version though, as it was just released.
est31 7y ago
Congrats to the team on the new release! Following their progress since a while. It's an important project, and I'm very happy about the size reductions while they still delivered a WER improvement over the last release. Amazing!
- reubenmorais 7y ago
  Thank you for your work on the Rust bindings :)
detaro 7y ago
> It achieves a 7.5% word error rate on the LibriSpeech test clean benchmark Anyone have a comparison for how good/bad that is compared to other solutions, and what it means for practical usage, if that can be guessed at from a single number?
- Traktor 7y ago
  You can find a leaderboard here: https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean https://paperswithcode.com/sota/speech-recognition-on-libris... It seems like sota is 2.20% word error rate
  - lunixbochs 7y ago
    I like reading through https://github.com/syhw/wer_are_we https://github.com/syhw/wer_are_we sometimes
  - wstuartcl 7y ago
    Note that the leaders there are not "near real time" streaming type implementations like this is. They have the benefit of having full context (they act on the full input not a stream of current state). 7.5% is really good for this type of low latency stream based system.
- est31 7y ago
  LibriSpeech test clean is without background noise, with a good microphone, and most likely biased to native speakers (it is 50/50 male/female at least). In summary, it's not what an ASR engine will encounter. However, the dataset is used as a common method to benchmark ASR engines and thus that number is valuable. The SpecAugment paper by Google for example claims a 2.5% word error rate [1] on that corpus (using only LibriSpeech for training data). [1]: https://arxiv.org/pdf/1904.08779.pdf https://arxiv.org/pdf/1904.08779.pdf
  - dabinat 7y ago
    It’s worth mentioning that while DeepSpeech 0.6 supports SpecAugment, the released model was not trained with SpecAugment switched on.
- reubenmorais 7y ago
  Author here. I would add to what the sibling comments have mentioned by saying that SotA results should be taken with a grain of salt. Our engine is capable of streaming (processing the audio as it's being recorded), which is not doable with architectures that have bidirectional decoders or attention mechanisms that require the whole encoder input ahead of time. For real world applications, this is absolutely crucial, users want latency numbers on the order of milliseconds, not seconds. This is why, if you run a standard test set like LibriSpeech on, say, a commercial offering from Google, it will perform considerably worse than state of the art according to Google papers. This repository [0] has a benchmark of some commercial offerings. Our model beats all of those on Librispeech clean and other (except for Speechmatics on Librispeech clean), as well as on Common Voice. But note that the Common Voice corpus used in that benchmark is very old. In sum, I would compare this against solutions that go for the same space: fast, client-side ASR, rather than state of the art. [0] https://github.com/Franck-Dernoncourt/ASR_benchmark#benchmark-results https://github.com/Franck-Dernoncourt/ASR_benchmark#benchmar...
bayesian_horse 7y ago
I wonder if it is any good at out-of-vocabulary words? Is it hard to teach it new things like medical terms and such?
- reubenmorais 7y ago
  In the post I mention a Brazilian company who's using DeepSpeech for vocabulary constrained, medical applications. They've trained their own Brazilian Portuguese models. The English acoustic model that we released does not have a fixed vocabulary. What determines the vocabulary is the language model, which can be created from text. So if you have in-domain text, and would like to try it out, I would say the first step is to create a language model using your text and then experiment with it. We have some documentation on how to create the LM here: https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm It's not super detailed, but I'd be happy to answer questions on our Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech
- faissaloo 7y ago
  I did write a small program to make custom vocabularies and datasets for DeepSpeech a while back when I was messing with it: https://github.com/faissaloo/deepspeech_dataset_builder https://github.com/faissaloo/deepspeech_dataset_builder It could use some TLC but it does the job.
- candiodari 7y ago
  It's using letters, not words, so in theory anything that can be spelled in English can be output: https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/files/2018/09/bidirectional.gif https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/fil... Or, if you prefer the code: https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253e46c7041c5e48d8da923cf2/DeepSpeech.py#L196 https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253... (well, I suppose technically it's in https://github.com/mozilla/DeepSpeech/blob/master/data/alphabet.txt https://github.com/mozilla/DeepSpeech/blob/master/data/alpha... through https://github.com/mozilla/DeepSpeech/blob/master/util/text.py https://github.com/mozilla/DeepSpeech/blob/master/util/text...., but I mean come on) In practice ... meh. It gets some things predictably wrong. They're readable, just wrong. So the good news is that WER is words it has 100% correct. The remaining errors aren't usually totally unreadable, just irritating, going from "totext" instead of "to text", but of course names go pretty wrong and don't get better. It butchers my name, of course. If you test it yourself, please do realize it really doesn't deal very well with accents.
kbumsik 7y ago
Looks great. Is it related to any of Mozilla's product?
- reubenmorais 7y ago
  We'll have more to share about that soon :)
bayesian_horse 7y ago
I just found https://voice.mozilla.org/ https://voice.mozilla.org/ Besides being a great resource for speech analysis, this could be a real game changer for acquiring listening comprehension in a foreign language. I feel that even after a few years of learning a new language I still have trouble with listening. Part of that is that it's often all or nothing, even one or two unknown words in a sentence means I can't understand the sentence. But worse is that most language teaching materials use a very small set of native speakers, which deprives the learner's brain of being able to generalize.
- pete762 7y ago
  Some of the English samples by non-natives were completely incomprehensible. I hope the system learns to be accent free.
  - bayesian_horse 7y ago
    They have a validation process, and the data is mainly meant to train speech-to-text. For that, it's important to include accents and even non-native speakers, especially in English. I think that those wavenet models for TTS were able to be conditioned by gender and maybe accent, if they had the data.
  - Barrin92 7y ago
    given that there are more non-native than native english speakers on the planet I think it makes a fair bit of sense to include non-native data in the training set
franciscop 7y ago
Tensorflow lite, interesting. Has anyone tried this with Google Coral USB accelerator? I got one laying around but very little experience with ML. I could get the USB Accelerator working with the pretrained posenet after much messing around, but my chances with DeepSpeech are small to none. This seems like the perfect fit though.
- dabinat 7y ago
  To the best of my knowledge, DeepSpeech currently isn’t compatible with any inference or training accelerators (other than NVIDIA GPUs). But the whole point of the TFLite model is that it has low resource requirements so an accelerator for inference shouldn’t be necessary.
  - reubenmorais 7y ago
    TFLite on Android with the NNAPI delegate is theoretically supposed to leverage hardware accelerators. We haven't tested it extensively though.
therealmarv 7y ago
So who will be developing an open source assistant like Goggle Home / Alexa? :)
- Erlich_Bachman 7y ago
  If you do a 1 minute google search you will find that there are multple open source voice assistant offerings already, for example mycroft.
  - detaro 7y ago
    Most of which are not fully open, e.g. do not have open speech recognition.
bmn__ 7y ago
I do not understand how to use Deepspeech even in the most simple use case. 1. I want to teach it ten words. How do I do this? 2. I want to speak into my microphone (available as a Pulseaudio device) and recognise the words and output the words as a text stream on stdout. How do I do this? This is the documentation: https://deepspeech.readthedocs.io/en/v0.6.0/Python-Examples.html https://deepspeech.readthedocs.io/en/v0.6.0/Python-Examples.... https://deepspeech.readthedocs.io/en/v0.6.0/Python-API.html https://deepspeech.readthedocs.io/en/v0.6.0/Python-API.html It does not answer the questions I have. https://deepspeech.readthedocs.io/en/v0.6.0/DeepSpeech.html https://deepspeech.readthedocs.io/en/v0.6.0/DeepSpeech.html The introduction page is full of incomprehensible jargon.
- reubenmorais 7y ago
  > 1. I want to teach it ten words. How do I do this? I'm assuming you mean "my target vocabulary is only ten words". In that case, you have a couple of options: 1. Just use it as is and see how it does. 2. Create a language model containing just those 10 words so that the engine is constrained to that vocabulary, which should improve accuracy. 3. Train a (smaller) model from scratch, which requires (audio, transcript) pairs. This is basically research work, there's no recipe you can just follow. The advantage is that if you can make it work, it can be a much smaller model than a model trained for general English dictation. > 2. I want to speak into my microphone (available as a Pulseaudio device) and recognise the words and output the words as a text stream on stdout. How do I do this? We have a few examples with microphone input, but they're all based on voice activity detection: https://github.com/mozilla/DeepSpeech/tree/master/examples https://github.com/mozilla/DeepSpeech/tree/master/examples I should write an example that is just continuous streaming and output without any voice activity detection. I'd be happy to discuss further on Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech
  - na85 7y ago
    >Just use it as is and see how it does. How? The docs seem to be suffering from the same problem as the git docs: they assume the reader is already a domain expert, which would seem to run counter to the stated goal of being simple (and ubiquitous). I tried to figure out how to "just use" deepspeech and failed.
travisporter 7y ago
Any recommendations on a good raspberry pi hat or microphone to test this?
- deleted 7y ago
  [deleted]
jcims 7y ago
This reminds me of something I would love to see happen but I don't have the skills to put it all together. I really think there's some potential merit to a reading coach app(lication) that listens to someone read and looks for weaknesses/disorders/etc compared to a trained model. It could provide those diagnostics to an educator, guide the content to focus on those, coach the reader directly, etc. It all seems very doable based on what I see in the technology today, I just don't have the skills to do it.
- Enginerrrd 7y ago
  I'll be honest... that seems like a pretty tough sell to me. You could be looking at anything from a speech disorder to a learning disability to a perfectly healthy normal child with idiosyncrasies and those things are usually left to professionals with graduate degrees. The liability seems high, you've got both the potential stigma associated with incorrectly flagging a child, or worse, missing an otherwise obvious issue that means treatment is delayed during critical years.
  - jcims 7y ago
    I totally understand where you're coming from, but the reality is that there is a lot of imbalance and delay in this type of assessment and educational support already. My wife has been an English language arts teacher and instructional coach for almost 20 years. While they definitely have standard diagnostics and regular assessments to try to keep kids on track, it's essentially best effort. And when you move out of 4th/5th grade in many districts you start getting to 90:1 teacher-student ratios with 1-2 interns available to do 1:1 reading support across maybe 500 kids. And this is in a middle class school district. There's definitely some ways it can go bad, but even basic fluency monitoring without any active remediation attempts would be a good addition.
    - yorwba 7y ago
      > even basic fluency monitoring without any active remediation attempts would be a good addition. How do you define fluency? I guess it involves some combination of speed and accuracy. Speed shouldn't be too hard to measure, but for accuracy you'll probably need a model that's more accurate than 7.5% WER and can handle the difference in vocal range between children and adults. Otherwise the speech disfluencies you want to detect will be drowned out by the model not correctly recognizing actually pretty clear speech.
mncharity 7y ago
Perhaps there's an opportunity to create a demo webpage using tensorflow.js? Especially with the new TSLite support. To raise visibility and awareness. The posenet webcam demo[1] for example, seems to have ~2k links. [1] https://storage.googleapis.com/tfjs-models/demos/posenet/camera.html https://storage.googleapis.com/tfjs-models/demos/posenet/cam...
testbed 7y ago
I can't seem to find pre-trained models for other languages (French). How long did you training take for those in English? Do you think it makes sense to start from the English one? Thank you!
maxbrunsfeld 7y ago
> DeepSpeech v0.6 with TensorFlow Lite runs faster than real time on a single core of a Raspberry Pi 4 This is great news. I'm not very familiar with the deep learning framework ecosystem. Does anyone know what the simplest way would be to incorporate a DeepSpeech model into a WebAssembly project? For instance, is it straightforward to compile Tensorflow Lite with emscripten? If not, can this TensorFlow model be run with tensorflow.js? Or can it be converted to some other format that is easier to use with WASM?
z3t4 7y ago
I was very exited about this, then I tried it with the pre-trained model and I recorded "Hello this is a test message" 3 times and it was inferred as "a sassanian", "he is a paris" "he states that" ...

278 points by pionerkotik 7y ago

I'm starting to look into the MycroftAI. It sounds like with this release I could use Mycroft+DeepSpeech on a new RaspberryPi for a completely offline smart speaker, do I understand that right?

zitterbewegung 7y ago
Yes, you are correct tensorflow lite can be done on the raspberry pi. https://www.tensorflow.org/lite/guide/build_rpi https://www.tensorflow.org/lite/guide/build_rpi
reubenmorais 7y ago
Correct. The latest release with the TensorFlow Lite model runs in real time on a Raspberry Pi 4. I'm not sure if the Mycroft integration is updated to the latest version though, as it was just released.

est31 7y ago

Congrats to the team on the new release! Following their progress since a while. It's an important project, and I'm very happy about the size reductions while they still delivered a WER improvement over the last release. Amazing!

reubenmorais 7y ago
Thank you for your work on the Rust bindings :)

detaro 7y ago

> It achieves a 7.5% word error rate on the LibriSpeech test clean benchmark Anyone have a comparison for how good/bad that is compared to other solutions, and what it means for practical usage, if that can be guessed at from a single number?

Traktor 7y ago
You can find a leaderboard here: https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean https://paperswithcode.com/sota/speech-recognition-on-libris... It seems like sota is 2.20% word error rate
- lunixbochs 7y ago
  I like reading through https://github.com/syhw/wer_are_we https://github.com/syhw/wer_are_we sometimes
- wstuartcl 7y ago
  Note that the leaders there are not "near real time" streaming type implementations like this is. They have the benefit of having full context (they act on the full input not a stream of current state). 7.5% is really good for this type of low latency stream based system.
est31 7y ago
LibriSpeech test clean is without background noise, with a good microphone, and most likely biased to native speakers (it is 50/50 male/female at least). In summary, it's not what an ASR engine will encounter. However, the dataset is used as a common method to benchmark ASR engines and thus that number is valuable. The SpecAugment paper by Google for example claims a 2.5% word error rate [1] on that corpus (using only LibriSpeech for training data). [1]: https://arxiv.org/pdf/1904.08779.pdf https://arxiv.org/pdf/1904.08779.pdf
- dabinat 7y ago
  It’s worth mentioning that while DeepSpeech 0.6 supports SpecAugment, the released model was not trained with SpecAugment switched on.
reubenmorais 7y ago
Author here. I would add to what the sibling comments have mentioned by saying that SotA results should be taken with a grain of salt. Our engine is capable of streaming (processing the audio as it's being recorded), which is not doable with architectures that have bidirectional decoders or attention mechanisms that require the whole encoder input ahead of time. For real world applications, this is absolutely crucial, users want latency numbers on the order of milliseconds, not seconds. This is why, if you run a standard test set like LibriSpeech on, say, a commercial offering from Google, it will perform considerably worse than state of the art according to Google papers. This repository [0] has a benchmark of some commercial offerings. Our model beats all of those on Librispeech clean and other (except for Speechmatics on Librispeech clean), as well as on Common Voice. But note that the Common Voice corpus used in that benchmark is very old. In sum, I would compare this against solutions that go for the same space: fast, client-side ASR, rather than state of the art. [0] https://github.com/Franck-Dernoncourt/ASR_benchmark#benchmark-results https://github.com/Franck-Dernoncourt/ASR_benchmark#benchmar...

bayesian_horse 7y ago

I wonder if it is any good at out-of-vocabulary words? Is it hard to teach it new things like medical terms and such?

reubenmorais 7y ago
In the post I mention a Brazilian company who's using DeepSpeech for vocabulary constrained, medical applications. They've trained their own Brazilian Portuguese models. The English acoustic model that we released does not have a fixed vocabulary. What determines the vocabulary is the language model, which can be created from text. So if you have in-domain text, and would like to try it out, I would say the first step is to create a language model using your text and then experiment with it. We have some documentation on how to create the LM here: https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm https://github.com/mozilla/DeepSpeech/tree/v0.6.0/data/lm It's not super detailed, but I'd be happy to answer questions on our Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech
faissaloo 7y ago
I did write a small program to make custom vocabularies and datasets for DeepSpeech a while back when I was messing with it: https://github.com/faissaloo/deepspeech_dataset_builder https://github.com/faissaloo/deepspeech_dataset_builder It could use some TLC but it does the job.
candiodari 7y ago
It's using letters, not words, so in theory anything that can be spelled in English can be output: https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/files/2018/09/bidirectional.gif https://2r4s9p1yi1fa2jd7j43zph8r-wpengine.netdna-ssl.com/fil... Or, if you prefer the code: https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253e46c7041c5e48d8da923cf2/DeepSpeech.py#L196 https://github.com/mozilla/DeepSpeech/blob/0427c1572ac8f7253... (well, I suppose technically it's in https://github.com/mozilla/DeepSpeech/blob/master/data/alphabet.txt https://github.com/mozilla/DeepSpeech/blob/master/data/alpha... through https://github.com/mozilla/DeepSpeech/blob/master/util/text.py https://github.com/mozilla/DeepSpeech/blob/master/util/text...., but I mean come on) In practice ... meh. It gets some things predictably wrong. They're readable, just wrong. So the good news is that WER is words it has 100% correct. The remaining errors aren't usually totally unreadable, just irritating, going from "totext" instead of "to text", but of course names go pretty wrong and don't get better. It butchers my name, of course. If you test it yourself, please do realize it really doesn't deal very well with accents.

kbumsik 7y ago

Looks great. Is it related to any of Mozilla's product?

reubenmorais 7y ago
We'll have more to share about that soon :)

bayesian_horse 7y ago

I just found https://voice.mozilla.org/ https://voice.mozilla.org/ Besides being a great resource for speech analysis, this could be a real game changer for acquiring listening comprehension in a foreign language. I feel that even after a few years of learning a new language I still have trouble with listening. Part of that is that it's often all or nothing, even one or two unknown words in a sentence means I can't understand the sentence. But worse is that most language teaching materials use a very small set of native speakers, which deprives the learner's brain of being able to generalize.

pete762 7y ago
Some of the English samples by non-natives were completely incomprehensible. I hope the system learns to be accent free.
- bayesian_horse 7y ago
  They have a validation process, and the data is mainly meant to train speech-to-text. For that, it's important to include accents and even non-native speakers, especially in English. I think that those wavenet models for TTS were able to be conditioned by gender and maybe accent, if they had the data.
- Barrin92 7y ago
  given that there are more non-native than native english speakers on the planet I think it makes a fair bit of sense to include non-native data in the training set

franciscop 7y ago

Tensorflow lite, interesting. Has anyone tried this with Google Coral USB accelerator? I got one laying around but very little experience with ML. I could get the USB Accelerator working with the pretrained posenet after much messing around, but my chances with DeepSpeech are small to none. This seems like the perfect fit though.

dabinat 7y ago
To the best of my knowledge, DeepSpeech currently isn’t compatible with any inference or training accelerators (other than NVIDIA GPUs). But the whole point of the TFLite model is that it has low resource requirements so an accelerator for inference shouldn’t be necessary.
- reubenmorais 7y ago
  TFLite on Android with the NNAPI delegate is theoretically supposed to leverage hardware accelerators. We haven't tested it extensively though.

therealmarv 7y ago

So who will be developing an open source assistant like Goggle Home / Alexa? :)

Erlich_Bachman 7y ago
If you do a 1 minute google search you will find that there are multple open source voice assistant offerings already, for example mycroft.
- detaro 7y ago
  Most of which are not fully open, e.g. do not have open speech recognition.

bmn__ 7y ago

I do not understand how to use Deepspeech even in the most simple use case. 1. I want to teach it ten words. How do I do this? 2. I want to speak into my microphone (available as a Pulseaudio device) and recognise the words and output the words as a text stream on stdout. How do I do this? This is the documentation: https://deepspeech.readthedocs.io/en/v0.6.0/Python-Examples.html https://deepspeech.readthedocs.io/en/v0.6.0/Python-Examples.... https://deepspeech.readthedocs.io/en/v0.6.0/Python-API.html https://deepspeech.readthedocs.io/en/v0.6.0/Python-API.html It does not answer the questions I have. https://deepspeech.readthedocs.io/en/v0.6.0/DeepSpeech.html https://deepspeech.readthedocs.io/en/v0.6.0/DeepSpeech.html The introduction page is full of incomprehensible jargon.

reubenmorais 7y ago
> 1. I want to teach it ten words. How do I do this? I'm assuming you mean "my target vocabulary is only ten words". In that case, you have a couple of options: 1. Just use it as is and see how it does. 2. Create a language model containing just those 10 words so that the engine is constrained to that vocabulary, which should improve accuracy. 3. Train a (smaller) model from scratch, which requires (audio, transcript) pairs. This is basically research work, there's no recipe you can just follow. The advantage is that if you can make it work, it can be a much smaller model than a model trained for general English dictation. > 2. I want to speak into my microphone (available as a Pulseaudio device) and recognise the words and output the words as a text stream on stdout. How do I do this? We have a few examples with microphone input, but they're all based on voice activity detection: https://github.com/mozilla/DeepSpeech/tree/master/examples https://github.com/mozilla/DeepSpeech/tree/master/examples I should write an example that is just continuous streaming and output without any voice activity detection. I'd be happy to discuss further on Discourse: https://discourse.mozilla.org/c/deep-speech https://discourse.mozilla.org/c/deep-speech
- na85 7y ago
  >Just use it as is and see how it does. How? The docs seem to be suffering from the same problem as the git docs: they assume the reader is already a domain expert, which would seem to run counter to the stated goal of being simple (and ubiquitous). I tried to figure out how to "just use" deepspeech and failed.

travisporter 7y ago

Any recommendations on a good raspberry pi hat or microphone to test this?

deleted 7y ago
[deleted]

jcims 7y ago

This reminds me of something I would love to see happen but I don't have the skills to put it all together. I really think there's some potential merit to a reading coach app(lication) that listens to someone read and looks for weaknesses/disorders/etc compared to a trained model. It could provide those diagnostics to an educator, guide the content to focus on those, coach the reader directly, etc. It all seems very doable based on what I see in the technology today, I just don't have the skills to do it.

Enginerrrd 7y ago
I'll be honest... that seems like a pretty tough sell to me. You could be looking at anything from a speech disorder to a learning disability to a perfectly healthy normal child with idiosyncrasies and those things are usually left to professionals with graduate degrees. The liability seems high, you've got both the potential stigma associated with incorrectly flagging a child, or worse, missing an otherwise obvious issue that means treatment is delayed during critical years.
- jcims 7y ago
  I totally understand where you're coming from, but the reality is that there is a lot of imbalance and delay in this type of assessment and educational support already. My wife has been an English language arts teacher and instructional coach for almost 20 years. While they definitely have standard diagnostics and regular assessments to try to keep kids on track, it's essentially best effort. And when you move out of 4th/5th grade in many districts you start getting to 90:1 teacher-student ratios with 1-2 interns available to do 1:1 reading support across maybe 500 kids. And this is in a middle class school district. There's definitely some ways it can go bad, but even basic fluency monitoring without any active remediation attempts would be a good addition.
  - yorwba 7y ago
    > even basic fluency monitoring without any active remediation attempts would be a good addition. How do you define fluency? I guess it involves some combination of speed and accuracy. Speed shouldn't be too hard to measure, but for accuracy you'll probably need a model that's more accurate than 7.5% WER and can handle the difference in vocal range between children and adults. Otherwise the speech disfluencies you want to detect will be drowned out by the model not correctly recognizing actually pretty clear speech.

mncharity 7y ago

Perhaps there's an opportunity to create a demo webpage using tensorflow.js? Especially with the new TSLite support. To raise visibility and awareness. The posenet webcam demo[1] for example, seems to have ~2k links. [1] https://storage.googleapis.com/tfjs-models/demos/posenet/camera.html https://storage.googleapis.com/tfjs-models/demos/posenet/cam...

testbed 7y ago

I can't seem to find pre-trained models for other languages (French). How long did you training take for those in English? Do you think it makes sense to start from the English one? Thank you!

maxbrunsfeld 7y ago

> DeepSpeech v0.6 with TensorFlow Lite runs faster than real time on a single core of a Raspberry Pi 4 This is great news. I'm not very familiar with the deep learning framework ecosystem. Does anyone know what the simplest way would be to incorporate a DeepSpeech model into a WebAssembly project? For instance, is it straightforward to compile Tensorflow Lite with emscripten? If not, can this TensorFlow model be run with tensorflow.js? Or can it be converted to some other format that is easier to use with WASM?

z3t4 7y ago

I was very exited about this, then I tried it with the pre-trained model and I recorded "Hello this is a test message" 3 times and it was inferred as "a sassanian", "he is a paris" "he states that" ...