[AI from Scratch] Episode 200: Tacotron — A Model for Text-to-Speech Conversion

Recap: WaveNet

In the previous episode, we explained WaveNet, a neural network-based model that directly generates speech waveforms, offering high-quality speech synthesis. By generating audio at the sample level, WaveNet produces more natural and realistic speech compared to traditional methods. In this episode, we will discuss Tacotron, a model often used in conjunction with WaveNet.

What Is Tacotron?

Tacotron is a model that takes text as input and extracts speech features to synthesize speech. Specifically, it converts text into a representation called a mel spectrogram, which contains the features of the speech. This mel spectrogram is then used by WaveNet or other models to generate the final speech waveform.

Tacotron has two primary versions:

1. Tacotron (Original Tacotron)
2. Tacotron 2

Tacotron 2 is an improved version of the original Tacotron, achieving higher quality speech synthesis. Below, we will explain the basic mechanism of Tacotron and highlight the differences between Tacotron and Tacotron 2.

How Does Tacotron Work?

1. Text Processing and Encoder

The first step in Tacotron is text processing. The input text is treated as a sequence of characters, which is then converted into numerical values (encoding). This encoding process involves converting each character into a one-hot vector or embedding vector.

Next, an encoder extracts the features of the text. This encoder learns the meaning and structure of the text, extracting the necessary information for speech synthesis.

2. Attention Mechanism

A distinctive element of Tacotron is the attention mechanism. This mechanism connects the output from the encoder to the generation of speech features in the decoder. Specifically, it focuses on the important parts of the encoder’s output (through attention) to determine the next set of speech features to generate.

This attention mechanism efficiently learns which parts of the text correspond to specific speech elements.

3. Decoder and Mel Spectrogram Generation

Next, the decoder receives the output from the attention mechanism and generates a mel spectrogram representing the speech features. The mel spectrogram shows how speech varies over time and across frequencies, containing the information needed to create the final speech waveform.

4. Speech Waveform Generation

Finally, the generated mel spectrogram is used by models like WaveNet or the Griffin-Lim Algorithm to produce the speech waveform. Tacotron 2 primarily uses WaveNet, resulting in high-quality speech synthesis.

Differences Between Tacotron and Tacotron 2

The main difference between Tacotron and Tacotron 2 lies in how they generate the speech waveform:

1. Tacotron (Original Tacotron): After generating the mel spectrogram, it uses the Griffin-Lim Algorithm to reconstruct the waveform. However, this algorithm doesn’t achieve the same audio quality as WaveNet, limiting the naturalness of the output.
2. Tacotron 2: This version uses WaveNet to convert the mel spectrogram into a speech waveform, greatly enhancing audio quality. By combining WaveNet’s waveform generation capability with Tacotron’s mel spectrogram generation, Tacotron 2 achieves highly natural speech.

Features and Advantages of Tacotron

1. Natural and Smooth Speech Synthesis

Tacotron can directly generate speech from text through end-to-end learning, resulting in smoother and more natural speech compared to traditional methods.

2. Flexible Text Processing

Tacotron is capable of handling complex text and can naturally reflect intonation and prosody (rhythm and emphasis). This allows it to produce more human-like speech rather than monotonous readings.

3. Learning Complex Pronunciations and Expressions

Tacotron, trained with a large dataset of speech, can learn complex pronunciations and expressions. It can even adapt to special accents or different languages.

Applications of Tacotron

Tacotron is widely used in various contexts, such as:

1. Voice Assistants: Technologies like Google Assistant and Amazon Alexa use Tacotron for high-quality speech synthesis.
2. Speech Narration: Tacotron is used in automated reading services, helping provide audio for news articles and books.
3. Language Learning Apps: Tacotron is also used in learning tools to provide correct pronunciation for language learners.

Summary

In this episode, we discussed Tacotron, an important model for generating speech from text. Tacotron’s second version, Tacotron 2, allows for even higher-quality speech synthesis when combined with WaveNet. In the next episode, we will explore evaluation metrics for speech generation, explaining how to assess speech quality using metrics like PESQ and STOI.

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Preview of the Next Episode

Next time, we will dive into evaluation metrics for speech generation. We’ll learn about methods such as PESQ and STOI to understand how the quality of synthesized speech is evaluated. Stay tuned!

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Annotations

1. Mel Spectrogram: A spectrum representing the time-frequency characteristics of speech, used in analyzing and generating speech waveforms.
2. One-Hot Vector: A vector format that represents each element as either 0 or 1.
3. Attention Mechanism: A mechanism that focuses on important parts of the input data to extract essential information.
4. Griffin-Lim Algorithm: A method for reconstructing speech waveforms from mel spectrograms.