Have you ever wished to bring your words to life by converting text into natural-sounding speech? In this blog, we’ll explore how to achieve that using a pretrained Multi-band MelGAN, particularly trained on the Thorsten dataset. This guide will walk you through the steps required to implement this technology with ease, enabling you to turn written text into audio seamlessly.

What You’ll Need

• Python installed on your machine
• TensorFlowTTS library
• The Thorsten dataset for German language processing

Step 1: Install TensorFlowTTS

First things first! You need to install the TensorFlowTTS library in your Python environment. Open your terminal and run the following command:

pip install TensorFlowTTS

Step 2: Converting Text to WAV

Now that you have the library installed, it’s time to convert the text into a .wav file! Let’s run through the required code step-by-step. Think of this code as a recipe where each line is an ingredient or instruction needed to create your delightful audio dish.

1. We start by importing the necessary libraries: soundfile for audio file handling, numpy for numerical operations, and TensorFlow for handling machine learning models.

2. Then, we load our pretrained models: one for text-to-mel conversion and the other for mel-to-wav conversion.

3. Next, we define our input text, and the AutoProcessor cleans it up for the model.

4. The first model generates mel spectrograms from the text, which are like blueprints for sound.

5. Finally, the second model turns those blueprints into audio, producing our .wav file.

Here is the complete code:

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

# Load processors and models
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-thorsten-ger")

# Define your text
text = "Möchtest du das meiner Frau erklären? Nein? Ich auch nicht."

# Convert text to input IDs
input_ids = processor.text_to_sequence(text)

# Tacotron2 inference (text-to-mel)
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),
)

# MelGAN inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]

# Save the audio to files
sf.write("audio.wav", audio, 22050, "PCM_16")

Troubleshooting

If you encounter any issues during installation or execution, here are some troubleshooting ideas:

• Issue: Library not found. Make sure TensorFlowTTS is installed properly.
• Issue: Unexpected errors during inference. Ensure you’re using the correct input text and that the models have loaded successfully.
• Issue: Audio file not generating. Check the file path and permissions where you are trying to save the audio.

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Conclusion

With this step-by-step guide, you are now equipped to convert any text into spoken audio! Whether for educational purposes, software applications, or enhancing your own projects, the Multi-band MelGAN offers powerful capabilities for text-to-speech conversion.

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.