AI News, Implementing an Autoencoder in TensorFlow 2.0 artificial intelligence

mozilla/DeepSpeech

DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper.

Once everything is installed, you can then use the deepspeech binary to do speech-to-text on short (approximately 5-second long) audio files as such: Alternatively, quicker inference can be performed using a supported NVIDIA GPU on Linux.

Alternatively, you can run the following command to download and unzip the model files in your current directory: There are three ways to use DeepSpeech inference: The GPU capable builds (Python, NodeJS, C++ etc) depend on the same CUDA runtime as upstream TensorFlow.

You can then use the deepspeech binary to do speech-to-text on an audio file: For the Python bindings, it is highly recommended that you perform the installation within a Python 3.5 or later virtual environment.

To perform the installation, just use pip3 as such: If deepspeech is already installed, you can update it as such: Alternatively, if you have a supported NVIDIA GPU on Linux, you can install the GPU specific package as follows: See the release notes to find which GPUs are supported.

To download the pre-built binaries for the deepspeech command-line client, use util/taskcluster.py: or if you're on macOS: also, if you need some binaries different than current master, like v0.2.0-alpha.6, you can use --branch: The script taskcluster.py will download native_client.tar.xz (which includes the deepspeech binary and associated libraries) and extract it into the current folder.

You can download the Node.JS bindings using npm: Alternatively, if you're using Linux and have a supported NVIDIA GPU, you can install the GPU specific package as follows: See the release notes to find which GPUs are supported.

In addition to the bindings above, third party developers have started to provide bindings to other languages: Install the required dependencies using pip3: You'll also need to install the ds_ctcdecoder Python package.

You can use util/taskcluster.py with the --decoder flag to get a URL to a binary of the decoder package appropriate for your platform and Python version: This command will download and install the ds_ctcdecoder package.

We provide an importer (bin/import_cv.py) which automates downloading and preparing the Common Voice corpus as such: If you already downloaded Common Voice from here, simply run bin/import_cv.py on the directory where the corpus is located.

The following files are official user-validated sets for training, validating and testing: The following files are the non-validated unofficial sets for training, validating and testing: cv-invalid.csv contains all samples that users flagged as invalid.

If, for example, Common Voice was imported into ../data/CV, DeepSpeech.py could be called like this: If you are brave enough, you can also include the other dataset, which contains not-yet-validated content: The central (Python) script is DeepSpeech.py in the project's root directory.

As a simple first example you can open a terminal, change to the directory of the DeepSpeech checkout and run: This script will train on a small sample dataset called LDC93S1, which can be overfitted on a GPU in a few minutes for demonstration purposes.

The purpose of checkpoints is to allow interruption (also in the case of some unexpected failure) and later continuation of training without losing hours of training time.

If you already have a trained model, you can re-export it for TFLite by running DeepSpeech.py again and specifying the same checkpoint_dir that you used for training, as well as passing --notrain --notest --export_tflite --export_dir /model/export/destination.

TensorFlow has tooling to achieve this: it requires building the target //tensorflow/contrib/util:convert_graphdef_memmapped_format (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use util/taskcluster.py tool to download, specifying tensorflow as a source and convert_graphdef_memmapped_format as artifact.

If it reports converting 0 nodes, something is wrong: make sure your model is a frozen one, and that you have not applied any incompatible changes (this includes quantize_weights).

For example, if you want to fine tune the entire graph using your own data in my-train.csv, my-dev.csv and my-test.csv, for three epochs, you can something like the following, tuning the hyperparameters as needed: Note: the released models were trained with --n_hidden 2048, so you need to use that same value when initializing from the release models.

Implementing an Autoencoder in TensorFlow 2.0

Well, let’s first recall that a neural network is a computational model that is used for finding a function describing the relationship between data features x and its values (a regression task) or labels (a classification task) y, i.e.

But instead of finding the function mapping the features x to their corresponding values or labels y, it aims to find the function mapping the features x to itself x.

the important features z of the data, and (2) a decoder which reconstructs the data based on its idea z of how it is structured.

Mathematically, The encoder h-sub-e learns the data representation z from the input features x, then the said representation serves as the input to the decoder h-sub-d in order to reconstruct the original data x.

The encoding is done by passing data input x to the encoder’s hidden layer h in order to learn the data representation z = f(h(x)).

Then, we connect the hidden layer to a layer (self.output_layer) that encodes the data representation to a lower dimension, which consists of what it thinks as important features.

However, instead of reducing data to a lower dimension, it reconstructs the data from its lower dimension representation z to its original dimension x.

The decoding is done by passing the lower dimension representation z to the decoder’s hidden layer h in order to reconstruct the data to its original dimension x = f(h(z)).

from the input layer to the encoder layer which learns the data representation, and use that representation as input to the decoder layer that reconstructs the original data.

We can finally (for real now) train our model by feeding it with mini-batches of data, and compute its loss and gradients per iteration through our previously-defined train function, which accepts the defined error function, the autoencoder model, the optimization algorithm, and the mini-batch of data.

Lastly, to record the training summaries in TensorBoard, we use the tf.summary.scalar for recording the reconstruction error values, and the tf.summary.image for recording the mini-batch of the original data and reconstructed data.

adding more layers and/or neurons, or using a convolutional neural network architecture as the basis of the autoencoder model, or use a different kind of autoencoder.

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