Converting Text to Speech and Speech to Text using gTTS and Vosk

Introduction

In this blog post, we’ll explore how to convert text to speech and then convert that speech back to text using Python. We’ll utilize the gTTS library for text-to-speech conversion and the vosk library for speech-to-text conversion. The entire process involves saving the generated speech as an MP3 file, converting it to a WAV file, and finally using the Vosk library for speech recognition.

Installing Required Packages

Before we proceed, let’s make sure we have the necessary packages installed:

!pip install gtts pydub vosk &>/dev/null

This will install the gTTS, pydub, and vosk packages, which are essential for our text-to-speech and speech-to-text tasks.

Text to Speech with gTTS

To get started, we’ll use the gTTS library, a Python wrapper for Google Text-to-Speech (gTTS) API. This library allows us to convert text into speech easily. Here’s a simple example:

from gtts import gTTS
from pydub import AudioSegment

# Text to be converted to speech
text = "Hello, this is a test. One two three four five. Ten nine eight seven."

# Create a gTTS object
tts = gTTS(text=text, lang='en')

# Save the speech as an mp3 file
tts.save('test.mp3')

In this example, we generate an MP3 file named test.mp3 containing the synthesized speech from the given text.

Converting MP3 to WAV

Next, we need to convert the MP3 file to a WAV file with a RIFF header, as Vosk requires input audio in this format. We can use the pydub library for this task:

from pydub import AudioSegment

# Convert the mp3 file to a wave file with the RIFF header
mp3_audio = AudioSegment.from_file("test.mp3", format="mp3")
wave_audio = mp3_audio.set_frame_rate(16000).set_sample_width(2).set_channels(1)

# Save the wave file
wave_audio.export("test.wav", format="wav")

This code takes the test.mp3 file, sets the appropriate parameters for Vosk, and saves the resulting WAV file as test.wav.

Speech to Text with Vosk

Now that we have our WAV file, we can use the Vosk library for speech-to-text conversion:

from vosk import Model, KaldiRecognizer
import wave

# Load Vosk model for English
model = Model(lang="en-us")

# Open the WAV file
wf = wave.open('test.wav', 'rb')

# Initialize the recognizer with the WAV file's frame rate
rec = KaldiRecognizer(model, wf.getframerate())

# Process the audio data in chunks
while True:
    data = wf.readframes(40)  # Adjust the chunk size as needed
    if len(data) == 0:
        break
    if rec.AcceptWaveform(data):
        pass

# Get the final result
result = rec.FinalResult()
print(result)

In this code snippet, we use the Vosk library to create a speech recognizer and process the audio file in chunks. The final result is then printed, providing the text representation of the speech.

Displaying the Resulting Audio

Lastly, let’s display the resulting audio using the IPython.display module:

from IPython.display import Audio
display(Audio('test.wav', autoplay=True))

This line of code will play the audio directly in your Jupyter notebook, allowing you to hear the synthesized speech and confirm the accuracy of the text-to-speech conversion.