[AI from Scratch] Episode 314: Basics of Speech Synthesis (Text-to-Speech) — Generating Audio from Text

Recap and Today’s Theme

Hello! In the previous episode, we discussed Wav2Vec, a self-supervised learning model used to extract features from audio data, which significantly improves the accuracy of speech recognition systems.

Today, we will explore the opposite process: Speech Synthesis or Text-to-Speech (TTS). TTS technology converts text into natural-sounding speech and is widely used in applications such as voice assistants, navigation systems, and e-book readers. In this episode, we’ll cover the basics of speech synthesis, its evolution, and demonstrate how to implement a simple TTS system using Python【689†source】.

What is Speech Synthesis (Text-to-Speech)?

Text-to-Speech (TTS) is a technology that converts written text into spoken words. TTS systems enable computers to simulate human speech, and they combine natural language processing (NLP) and audio signal processing to achieve this.

Common Use Cases of TTS

• Voice Assistants (e.g., Amazon Alexa, Google Assistant)
• Navigation Systems (providing driving directions or traffic updates)
• E-Book Readers (reading aloud digital books)
• Accessibility for the Visually Impaired (reading web content or books aloud)

Basic Structure of Speech Synthesis

A TTS system generates speech in several stages:

1. Text Analysis and Preprocessing

The first step is to analyze the input text and prepare it for speech generation. This step includes:

• Text Normalization: Expanding abbreviations, numbers, and dates into full words (e.g., converting “12/25” to “December 25”).
• Phoneme Conversion: Converting words into phonemes, the smallest units of sound in a language. This process relies on dictionaries and phonological rules【689†source】.

2. Acoustic Model

After converting text into phonemes, the acoustic model generates the audio waveform. This step involves:

• Waveform Generation: Creating a waveform for each phoneme and combining them into continuous speech.
• Prosody Control: Adding intonation, rhythm, and emphasis to make the speech sound natural based on the sentence structure and meaning【689†source】.

3. Vocoder for Final Output

Finally, a vocoder synthesizes the final speech signal. The vocoder adjusts basic elements of the speech signal, such as pitch and formants, to make the output sound more natural【689†source】.

Evolution of Speech Synthesis Technologies

1. Rule-Based Synthesis

Early TTS systems used rule-based synthesis, which combined phonemes based on predefined linguistic rules. However, this approach often resulted in robotic and unnatural speech【689†source】.

2. Concatenative Synthesis

Concatenative synthesis improved speech quality by using pre-recorded sound samples. These samples were stitched together to form speech. While this method produced more natural results, it lacked flexibility and required large amounts of data【689†source】.

3. Neural TTS

Modern TTS systems use deep learning, specifically neural networks, to generate speech directly from text. Models like Tacotron and WaveNet generate highly natural speech, often indistinguishable from human speech【689†source】.

• Tacotron: Converts text into a spectrogram and generates natural-sounding speech with appropriate intonation.
• WaveNet: A deep learning model that directly generates high-quality speech waveforms, though it requires significant computational resources【689†source】.

Implementing TTS in Python

Let’s walk through a simple example of using Python to implement TTS using the gTTS (Google Text-to-Speech) library.

1. Install Required Libraries

First, install the gTTS library, which allows you to easily convert text to speech.

pip install gtts

2. Example Code for Generating Speech from Text

Here’s how you can generate speech from text and save it as an audio file:

from gtts import gTTS

# Text to be converted into speech
text = "Hello, this is an example of text-to-speech synthesis using gTTS."

# Set language (e.g., 'en' for English)
language = 'en'

# Create the TTS object and generate speech
tts = gTTS(text=text, lang=language, slow=False)

# Save the generated speech to an audio file
tts.save("output.mp3")

print("Audio file saved as output.mp3")

• gTTS(): This function converts the input text into speech. The lang parameter specifies the language, and slow controls the speed of the speech.
• save(): Saves the generated audio as an MP3 file. In this example, the output file is named output.mp3【689†source】.

Advanced TTS Models: Tacotron 2 and WaveGlow

For higher-quality TTS, Tacotron 2 and WaveGlow are cutting-edge models used to generate realistic speech.

Tacotron 2

Tacotron 2, developed by Google, works in two steps:

1. Text Encoding: Converts text into a spectrogram by learning how each word or phoneme corresponds to acoustic patterns.
2. Waveform Generation: Converts the spectrogram into audio using models like WaveNet or WaveGlow【689†source】.

WaveGlow

WaveGlow is a vocoder that synthesizes high-quality speech in real time. It works efficiently to create natural-sounding speech by optimizing complex audio signals【689†source】.

Challenges and Future of TTS

Current Challenges

• Computational Cost: Neural TTS models, especially those generating high-quality waveforms, require significant computational resources.
• Multilingual and Accent Support: Supporting multiple languages and accents demands large, diverse datasets【689†source】.

Future Prospects

• Self-Supervised Learning: Techniques like those used in Wav2Vec could enhance TTS by learning from smaller datasets, reducing the need for extensive training data.
• Real-Time Applications: As models become more efficient, high-quality TTS systems will become increasingly accessible for real-time applications【689†source】.

Summary

In this episode, we explored the basics of Text-to-Speech (TTS) technology, from early rule-based systems to modern neural network-based models like Tacotron and WaveNet. TTS has come a long way, and with deep learning, it now produces extremely natural-sounding speech. Next, we will dive into the implementation of Tacotron 2, one of the leading models in the TTS domain.

Next Episode Preview

In the next episode, we will explore Tacotron 2 in detail, learning about its architecture and how it achieves high-quality speech synthesis. We will also provide an implementation guide to help you experiment with this cutting-edge technology.

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Notes

• Vocoder: A technology used in TTS to generate the final speech waveform by adjusting pitch, tone, and other elements of the sound.
• Tacotron: A neural network-based model developed by Google to convert text into speech using a spectrogram【689†source】.