Welcome to the world of text-to-speech synthesis! In this guide, we will explore how to use the Tacotron 2 model, enhanced with Guided Attention, trained on the LJSpeech dataset to convert text into mel spectrograms. This technology enables machines to produce human-like speech from text. Let’s get started!

What is Tacotron 2?

Tacotron 2 is a cutting-edge model developed to synthesize natural text-to-speech outputs. By leveraging mel spectrograms, it can produce high-fidelity speech. Think of Tacotron 2 as a skilled narrator who reads your text with the correct intonations and emotions, creating a pleasant listening experience.

Installation of TensorFlowTTS

Before we dive into text conversion, we need to install the required library. Open your terminal and run the following command:

pip install TensorFlowTTS

Converting Text to Mel Spectrogram

Now that we have TensorFlowTTS installed, let’s explore the code to convert text to mel spectrograms. Below is a breakdown of the process.

import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel

# Load the pre-trained models
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en")

# Input text for TTS
text = "This is a demo to show how to use our model to generate mel spectrogram from raw text."
input_ids = processor.text_to_sequence(text)

# Inferencing process
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
    input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
    input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
    speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)

In this code:

• Importing Libraries: We start by importing necessary libraries similar to gathering tools before construction.
• Loading Pre-trained Models: The AutoProcessor and TFAutoModel are like pre-trained chefs ready to whip up delicious meals, only this time, they serve us mel spectrograms.
• Input Text: Here, you provide the text you want to convert. It’s akin to providing a script to our narrator.
• Inference Process: Finally, the model processes the input to generate mel spectrograms, effectively transforming written words into something audible.

Troubleshooting Tips

If you encounter issues while implementing the process, here are some troubleshooting ideas:

• Ensure TensorFlowTTS is Installed: Double-check your installation by running pip list and searching for TensorFlowTTS.
• Correct Model Path: Make sure you are using the correct model paths when loading AutoProcessor and TFAutoModel.
• Input Text Format: Ensure that the text provided to the model is properly formatted and does not contain special characters that may cause issues during processing.

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Conclusion

With just a few steps, you can unlock the power of Tacotron 2 with Guided Attention for your text-to-speech applications. This model not only enhances the listening experience but also opens up avenues for further development in AI-driven speech synthesis.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.