Exploring Voice Recognition With Tensorflow

Artificial intelligence continues to revolutionize modern technology, with its applications ranging from automated customer service to sophisticated health diagnostics. One incredible application of AI technology is in the field of voice recognition. Voice recognition technology is advancing rapidly, and today we're going to look at how to build and train a basic voice recognition model using Tensorflow, a powerful machine learning library.

Introduction to Voice Recognition

Voice recognition or speech recognition is the technology that allows computers to interpret and understand human speech. The technology is used in a multitude of applications such as virtual assistants like Amazon's Alexa or Google Home, transcription services, and even in health tech for hands-free data entry.

Building a Voice Recognition Model with Tensorflow

Let's start by installing Tensorflow into our workspace.

pip install tensorflow

Once installed, import the necessary libraries.

import tensorflow as tf
import numpy as np

For our task, we will use the SpeechCommands dataset. The SpeechCommands dataset is essentially a set of 65,000 one-second long audio clips of 30 short words. However, handling audio data can be tricky, so to simplify the task, we will convert these sound clips into spectrograms using tf.audio.decode_wav function.

autotune = tf.data.experimental.AUTOTUNE
filepath = 'path_to_your_audio_files/*.wav'  # replace with your dataset path
files_ds = tf.data.Dataset.list_files(filepath, shuffle=False)
waveform_ds = files_ds.map(get_waveform_and_label, num_parallel_calls=autotune)

Above, we first initialize the autotune then specify the file path for our files. After that, we map the audio files in our dataset to their waveforms and labels, which allows us to convert the audio file to waveforms in parallel.

Tensorflow has an in-built function tf.signal.stft that helps in generating the spectrograms from waveforms.

def get_spectrogram(waveform):
  # Concatenate audio with padding so that all audio clips will be of the 
  # same length
  zero_padding = tf.zeros([16000] - tf.shape(waveform), dtype=tf.float32)
  waveform = tf.cast(waveform, tf.float32)
  equal_length = tf.concat([waveform, zero_padding], 0)
  spectrogram = tf.signal.stft(
      equal_length, frame_length=255, frame_step=128)

  spectrogram = tf.abs(spectrogram)
  return spectrogram

After generating the spectrogram, we should normalize our data for better performance.

def normalize_spectrogram(spectrogram):
  spectrogram = tf.image.per_image_standardization(spectrogram)
  spectrogram = tf.expand_dims(spectrogram, -1)
  spectrogram = tf.image.resize(spectrogram, [32, 32])
  return spectrogram

With all these in place, we'll build a model using tf.keras.Sequential and then compile it.

model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(32, 32, 1)),  
    tf.keras.layers.Conv2D(32, 3, activation='relu'),
    tf.keras.layers.Conv2D(64, 3, activation='relu'),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

After building our model, we can train it using model.fit and save it for future use with model.save.

model.fit(dataset, epochs=10)
model.save('voice_recognition_model')

And voila! You have trained a basic voice recognition model using Tensorflow. The above model is quite basic, and for a production-grade voice recognition system, you'd require more advanced techniques and large amounts of data. However, this model provides a good starting point to delve into the exciting world of voice recognition. The source code can be found on Github.

Summary

Voice recognizing systems are becoming an integral part of human-digital interaction, allowing for seamless, hands-free operations. By learning to build a voice recognition system using Tensorflow, you're diving into an essential sector of AI, one with myriad applications and continual demand. Remember, the key to mastering this technology lies in practice and continuous learning, so don't stop exploring!