Text-to-Speech (TTS): Transforming Written Words into Natural Speech

February 13, 2025

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Summary

This post will cover Text to Speech (TTS) we will explore:

How TTS works
The evolution of TTS from rule-based to deep learning models
The key applications of TTS in modern AI
How to implement TTS in Python using open-source libraries
Voice cloning: replicating any voice using AI
Multiple voices so we can text to speech a Shakespeare play

How Text-to-Speech Works

At a high level, TTS involves converting text input into spoken audio output. This process consists of three main stages:

1️⃣ Text Processing (Linguistic Analysis)

The input text is broken down into phonemes (smallest units of sound).
Sentence structure and punctuation are analyzed to determine intonation and pauses.

2️⃣ Acoustic Modeling

This step predicts how each phoneme should sound by considering pitch, duration, and articulation.
Early TTS systems used concatenative synthesis, where pre-recorded speech fragments were stitched together.
Modern deep learning-based TTS relies on neural networks to generate highly realistic and expressive speech.

3️⃣ Waveform Generation

This step converts the synthesized phonemes into a human-like voice.
Deep learning models such as WaveNet, Tacotron, and VITS are widely used for this purpose.

Evolution of TTS Technology

Early Rule-Based Systems

Based on hand-coded rules and phonetic dictionaries.
Voices sounded robotic and unnatural.

Concatenative Synthesis (Pre-Recorded Speech)

Uses recorded speech units.
Produces higher quality but limited flexibility.

Statistical Parametric Synthesis (HMM & DNN-Based)

Uses machine learning models to generate speech dynamically.
More flexible than concatenative synthesis but less natural.

Neural TTS (WaveNet, Tacotron, FastSpeech, VITS)

Deep learning models that mimic human speech patterns.
Can generate expressive, emotionally rich speech.

Applications of Text-to-Speech

Accessibility – Screen readers (e.g., NVDA, JAWS) help visually impaired users.
Voice Assistants – Siri, Google Assistant, Alexa use TTS for human-like conversations.
Audiobooks & Podcasting – Automatic audiobook generation with expressive voices.
Customer Support – IVR (Interactive Voice Response) systems in call centers.
Language Learning & Translation – Real-time spoken translations.
Developer Tools – AI-driven voice interfaces for applications.
Voice Cloning & AI Avatars – AI-powered synthetic voices for entertainment & digital humans.

Implementing TTS in Python

There are several open-source libraries for building TTS applications in Python:

1️⃣ Using `pyttsx3` (Offline TTS)

This is a lightweight offline TTS engine.

import pyttsx3

# Initialize TTS engine
engine = pyttsx3.init()

# Set properties (voice rate, volume, etc.)
engine.setProperty("rate", 150)
engine.setProperty("volume", 1.0)

# Convert text to speech
engine.say("Hello! Welcome to Text-to-Speech in Python.")
engine.runAndWait()

Works offline
Supports different voices
️ Less natural than deep learning-based TTS

2️⃣ Using `gTTS` (Google Text-to-Speech)

This library provides cloud-based speech synthesis.

from gtts import gTTS
import os

text = "Hello! This is a test of Google Text-to-Speech."
tts = gTTS(text=text, lang="en")

# Save the audio file
tts.save("gtts_output.mp3")

from IPython.display import Audio
display(Audio('gtts_output.mp3', autoplay=True))

High-quality voices
Supports multiple languages
️ Requires internet connection

3️⃣ Using pytorch `Tacotron2`

import torch
from TTS.api import TTS

# Load a pre-trained TTS model
tts = TTS("tts_models/en/ljspeech/tacotron2-DDC", progress_bar=True).to("cuda")

# Convert text to speech
tts.tts_to_file(text="Deep learning-based text-to-speech is amazing!", file_path="output.wav")

Realistic voice
Supports GPU acceleration
️ Requires deep learning models & dependencies

Voice Cloning: Replicating Any Voice with AI

What is Voice Cloning?

Voice cloning is a specialized TTS application where AI learns a person’s voice from a short audio sample and then generates speech that sounds like them.

How Voice Cloning Works

Speaker Embedding – The model extracts unique features of the target speaker’s voice.
Text-to-Speech Synthesis – The extracted voice features are combined with new text to generate realistic speech.
Fine-Tuning (Optional) – Further training on the target speaker’s dataset improves quality.

Popular Voice Cloning Models

Resemble AI
ElevenLabs
Meta’s Voicebox
VITS (Variational Inference Text-to-Speech)
Real-Time Voice Cloning (RTVC)

Implementing Voice Cloning in Python

We use the RTVC (Real-Time Voice Cloning) model.

Installation

git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git
cd Real-Time-Voice-Cloning
pip install -r requirements.txt

Clone a Voice

from synthesizer.inference import Synthesizer
from encoder import inference as encoder
from vocoder import inference as vocoder
import numpy as np
import torch

# Load pre-trained models
encoder.load_model("saved_models/default/encoder.pt")
synthesizer = Synthesizer("saved_models/default/synthesizer.pt")
vocoder.load_model("saved_models/default/vocoder.pt")

# Load and process a voice sample
wav = synthesizer.load_wav("path/to/speaker.wav", 16000)
embedding = encoder.embed_utterance(wav)

# Generate cloned speech
text = "Hello! This is an AI-generated version of my voice."
specs = synthesizer.synthesize_spectrograms([text], [embedding])
generated_wav = vocoder.infer_waveform(specs[0])

# Save the output
import soundfile as sf
sf.write("cloned_voice.wav", generated_wav, 22050)

High-quality cloned voices
Works with a few seconds of audio
️ May require a powerful GPU

Future of TTS & Voice Cloning

Real-time voice cloning – Instant AI-generated speech mimicking any voice.
AI-powered dubbing & localization – Automatic voice translation with speaker identity retention.
Hyper-realistic voice synthesis – AI-generated voices indistinguishable from humans.
Ethical concerns – Preventing misuse in deepfake audio & misinformation.

Example

Convert a pdf to an Audio book


# get text from pdf
import pdfplumber as pp

text = ''
with pp.open(r"your_pdf.pdf") as pdf:
    for page in pdf.pages:
        text += page.extract_text()    

from gtts import gTTS

# Convert Extracted Text to Speech
def create_audiobook(text):
    tts = gTTS(text=text, lang='en')
    tts.save(r"output_audio.mp3")  # Replace with your desired output path

create_audiobook(text)