I am inside a conda virtual environment. I get the same behavior for an environment running python 2. I know pypi has deepspeech as I can go to its page in pypi. The other sees deepspeech install without a problem.
We all have macbook pros. Any suggestions? I ran into a similar issue. I was on ubuntu Upgrading pip to 9. After pip install deepspeech succeeded. Good hint, it is something likely possible, and that would be consistent with the manual install that fails this way.
Pip is up to date. I did this both inside and outside my virtual environment. Your conda info shows OSX Our packages are targetting As a quick-hack, it might work that you rename the file deepspeech Thanks lissyx Lissyx. That seems to have worked! You are having those issues with the Python package? Either something regressed, or our configuration differs in ways that made it work for us and not for you. Loading language model from files lm. No such file or directory while opening lm.
Questions tagged [mozilla-deepspeech]
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. On Windowsyou must use Python 3. Unfortunately, the bit version is not supported by tensorflow and will give you that nasty error:. Could not find a version that satisfies the requirement tensorflow from versions: none ERROR: No matching distribution found for tensorflow.
Could you try to look if you have the right python version installed? Sometimes something went wrong and a bit version of python is installed. But tensorflow only works with the 64 bit verison of python.
You can check your python version with the following comand into the python interpreter. It generally seems that there is a problem with python 3. For 3. It's dumb but I don't knew that I had to change pycharm python versions manually. I tough that would set automatically through the terminal. Learn more. Asked 5 months ago. Active 19 days ago. Viewed 5k times. What platform are you using?
Active Oldest Votes. If you have previously installed Python 3. Finally, install Python 3. Now, simply install tensorflow: python -m pip install --user tensorflow. You save jobs!! Hi writing my suggestions here because i can't commment yet. You can check your python version with the following comand into the python interpreter import struct print struct.
Also a solution might be to downgrade to phyton 3. Fabian Fabian 1 1 silver badge 7 7 bronze badges. This solution always worked for me. My System Specification Windows 10 python 3. Shriram Navaratnalingam Shriram Navaratnalingam 3 3 silver badges 13 13 bronze badges. Gabriel Saviank Gabriel Saviank 13 4 4 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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If nothing happens, download the GitHub extension for Visual Studio and try again. Supports live recording and testing of speech and quickly creates customised datasets using own-voice dataset creation scripts!
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Don't like it? Open an issue and let's talk about it! Pull requests are appreciated! Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python Jupyter Notebook Shell. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Download the language model large file run.
Learn more. Questions tagged [mozilla-deepspeech]. Ask Question. Learn more… Top users Synonyms. Filter by. Sorted by. Tagged with. Apply filter. ERROR installing deepspeech in ubuntu server I was actually trying to install deepspeech to a raspberry 4 whith pip3 and python 3.
Converting Mozilla DeepSpeech model to use in tensorflow.
How to Check-Point Deep Learning Models in Keras
I have been Allasso 4 4 silver badges 12 12 bronze badges. Shak97 1. DeepSpeech giving bad results I am new to DeepSpeech i followed this link to create Speech to text code, but my results are no where near to the original speech. I am using Deepspeech 0. Ironman 1, 2 2 gold badges 9 9 silver badges 29 29 bronze badges. Segmentation fault during transcription - DeepSpeech 0.
Amnon 1, 1 1 gold badge 13 13 silver badges 27 27 bronze badges. Get alternative suggestions during speech recognition I would like to use offline speech to text recognition, mostly for German language.
Is there a way to continuously send snippets of audio being recorded in realtime to backend server in Flutter.How to Make a Simple Tensorflow Speech Recognizer
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In the image of the neural net below hidden layer1 has 4 units. Does this directly translate to the units attribute of the Layer object? Or does units in Keras equal the shape of every weight in the hidden layer times the number of units? It's a property of each layer, and yes, it's related to the output shape as we will see later.
In your picture, except for the input layer, which is conceptually different from other layers, you have:. Shapes are consequences of the model's configuration. Shapes are tuples representing how many elements an array or tensor has in each dimension.
In Keras, the input layer itself is not a layer, but a tensor. It's the starting tensor you send to the first hidden layer. This tensor must have the same shape as your training data. Example: if you have 30 images of 50x50 pixels in RGB 3 channelsthe shape of your input data is 30,50,50,3.
Then your input layer tensor, must have this shape see details in the "shapes in keras" section. Now, the input shape is the only one you must define, because your model cannot know it.
Only you know that, based on your training data. All the other shapes are calculated automatically based on the units and particularities of each layer. The "units" of each layer will define the output shape the shape of the tensor that is produced by the layer and that will be the input of the next layer. Each type of layer works in a particular way. Dense layers have output shape based on "units", convolutional layers have output shape based on "filters".
But it's always based on some layer property.
See the documentation for what each layer outputs. So, yes, units, the property of the layer, also defines the output shape. Weights will be entirely automatically calculated based on the input and the output shapes. Again, each type of layer works in a certain way. But the weights will be a matrix capable of transforming the input shape into the output shape by some mathematical operation. In a dense layer, weights multiply all inputs. It's a matrix with one column per input and one row per unit, but this is often not important for basic works.For my robotic project, I needed to create a small monospeaker model, with nearly sentences orders not just single word!
But, as Kdavis told me, removing white sound before processing, limits time spent for model creation! It seems that for small corpus my casea value from 3 to 4 seems to be the best way to success.
In first case, the distance produces echoschange voice wave amplitude … In the second case, motor wheels produce noiseground texture too… In both cases, noises, echos, amplitude signal variations, cause bad inferences!! Sure it needs a bit of time, recording new waves, to fit all scenarios, but I must say that the inference difference is very impressive : I can talk to my robot when moving, and ask it to stop, for example. A tool to apply a series of commands to a collection of samples.
Usage: voice. Oh, I can modify a whole csv file with a lot of params… Calling a csv file, you process on the fly modifications on every wav in csv!!! Would it work usagenot creation with a low end computer as a raspberry pi?
But I plan to create a multi-speakers french model, helped with voxforge. This one would suit you! Now, about openjarvis, my AI is based on Rivescript-python very-very powerfull!! You should try it!! I would not expect realtime though, but it might be manageable in your case.
If you need some other voices for your model I might help you. Openjarvis is nicely packaged and easy to use at least if you use raspbian Jessie and not stretch. I use snowboy for offline hotword detection and bing otherwise. It works quite well in french some problems with my 7 years old child As for the interactions it seems that the syntax is comparable at least for basic tasks. For now, I only have nearly 5h of my own voice nearly train samples….
Should I first compile it somehow? Hi Dj-Hay. Is there any workarounds or should I compile the program from sources? You need to pass --arch osx as documented. Great, thanks!
Ah, nevermind. I was wondering why you use your vocabulary. In alphabet. Each symbol is a label. Deepspeech learns each label with a lot of sounds. Thanks for your tutorial. Was considering breaking up each audio into single words, for training purposes. However, now I see by your comment that a complete sentence is preferred. For example, a 19 second WAV that has 55 words has 33 unique words.
Is there any advantage in using the same word by the same speaker for training the model?Have you ever wondered how to build your own speech recognition model that is able to distinguish words? Real recognition systems are pretty complex, but in this post, you will learn how to implement a simple machine learning model that recognizes a few words using neural networks.
We will convert sound data to images and use lessons learned from image recognition. There are not so many publicly available datasets that can be used for simple audio recognition problems. This contains around one-second sound files with commands like GoYes or Stop.
Please download the data before we move on it weights 1. Once you have downloaded and extracted the dataset, you will find audio files corresponding to 30 various commands in separate folders. The following code snippet produces wave plots of arbitrarily selected offgo and yes commands. Wave plots of off, go and yes commands. The procedure of representing a word in a vector or matrix form is called embedding. One dimensional vectors are easy to visualize, however, in speech recognition we rarely work with a raw amplitude data.
In our case we would follow spectrogram method to be more precise log-spectrograms as these are better to visualize. Interested readers can find the information about MFCC here. The spectrogram is a representation of audio file in a frequency domain instead of a temporal domain as it was for a raw data.
The parameters of the function control the trade-off between frequency resolution and time resolution and as a consequence also an output size. I encourage you to compare the outputs with wave plots.
Log-spectrograms of off, go and yes commands. The image size is x 98 time x number of frames. Here is an interesting fact to note. Nevertheless, for our purposes, the current frequency resolution will be sufficient.
As we have learned how to convert data into log-spectrograms, we are ready to apply machine learning models used in image recognition. It is a very simple model, but you are free to scale it up to more labels. Please download dataset. In the same folder as dataset. It turns out that it is better to preprocess the data and create log-spectrograms during training instead of beforehand.