Let's define a method that will take in the filename of the WAV file and output a tuple containing the audio and labels for supervised training. # Note: You'll use indexing here instead of tuple unpacking to enable this Parts = tf.strings.split(file_path, os.p) The label for each WAV file is its parent directory. def decode_audio(audio_binary):Īudio, _ = tf.code_wav(audio_binary) Note that tf.code_wav will normalize the values to the range. The sample rate for this dataset is 16kHz. In a 16-bit system, like the files in mini_speech_commands, the values range from -32768 to 32767. To load an audio file, you will use tf.code_wav, which returns the WAV-encoded audio as a Tensor and the sample rate.Ī WAV file contains time series data with a set number of samples per second.Įach sample represents the amplitude of the audio signal at that specific time. The audio file will initially be read as a binary file, which you'll want to convert into a numerical tensor.

Print('Validation set size', len(val_files)) Print('Training set size', len(train_files)) Split the files into training, validation and test sets using a 80:10:10 ratio, respectively. Print('Example file tensor:', filenames)Įxample file tensor: tf.Tensor(b'data/mini_speech_commands/right/a8e25ebb_nohash_0.wav', shape=(), dtype=string) Len(tf.io.gfile.listdir(str(data_dir/commands)))) Print('Number of total examples:', num_samples) filenames = tf.io.gfile.glob(str(data_dir) + '/*/*') commands = np.array(tf.io.gfile.listdir(str(data_dir)))Ĭommands = commandsĬommands: Įxtract the audio files into a list and shuffle it. Extract the mini_speech_commands.zip and load it in using the tf.data API. You'll be using a portion of the dataset to save time with data loading. This data was collected by Google and released under a CC BY license. The original dataset consists of over 105,000 WAV audio files of people saying thirty different words. You'll write a script to download a portion of the Speech Commands dataset. # Set seed for experiment reproducibility Import necessary modules and dependencies. Once you've completed this tutorial, you'll have a model that tries to classify a one second audio clip as 'down', 'go', 'left', 'no', 'right', 'stop', 'up' and 'yes'.

It's important to know that real speech and audio recognition systems are much more complex, but like MNIST for images, it should give you a basic understanding of the techniques involved. This tutorial will show you how to build a basic speech recognition network that recognizes ten different words.

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