source code for text into ai voice

Streamlining Text-to-Speech Tasks Using Google Colab

For those facing computational constraints on their local devices, Google Colab emerges as a powerful solution. This guide details the process of utilizing Google Colab for converting text into speech, leveraging cloud computing to overcome hardware limitations.

input text:

DevOps is a collaborative approach that integrates development (Dev) and operations (Ops) teams to improve software development and delivery. It emphasizes automation, continuous integration/continuous delivery (CI/CD), and monitoring to streamline workflows and enhance efficiency. The goal is to deliver high-quality software faster, with greater reliability and agility.

output audio:

Initial Setup on Google Colab

Begin by accessing Google Colab. Here’s how you can prepare your environment for text-to-speech operations:

!pip install TTS

Purpose of the “!” Command

Within Google Colab, the exclamation point ! serves to execute shell commands directly from the notebook. This functionality allows you to manage package installations from within the notebook’s interface, providing a smooth interaction with the operating system.

Code Implementation for Text-to-Speech

After installing the TTS library, you can proceed with converting your text into spoken words. The following steps illustrate the implementation:

# Install the required libraries
# Uncomment the line below to install required libraries
# !pip install TTS

from TTS.api import TTS

# Initialize the TTS model
# "tts_models/en/ljspeech/tacotron2-DCA" is a Tacotron2 model trained on LJ Speech dataset.
# You can replace this with other pre-trained models for different languages or voice styles.
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DCA")

# Text to convert to speech
text = "DevOps is a collaborative approach that integrates development (Dev) and operations (Ops) teams to improve software development and delivery. It emphasizes automation, continuous integration/continuous delivery (CI/CD), and monitoring to streamline workflows and enhance efficiency. The goal is to deliver high-quality software faster, with greater reliability and agility."

# Specify the output file
output_file = "output_audio.wav"

# Generate speech and save it to the file
tts.tts_to_file(text=text, file_path=output_file)

# Play the generated audio (optional, requires Colab environment)
from IPython.display import Audio
Audio(output_file)