Welcome to the world of Text-to-Speech (TTS) where your words come to life through sound! In this article, we’re going to guide you through the process of converting text into speech using the TensorFlow TTS library. Get ready to transform your text into mellifluous audio!

Step 1: Install TensorFlow TTS

First things first, you need to have TensorFlow TTS installed. This can easily be done via pip. Open your terminal or command prompt and run the following command:

pip install TensorFlowTTS

Step 2: Convert Text to Mel Spectrogram

Now that you have TensorFlow TTS installed, let’s dive into the code that will convert your text into a Mel spectrogram. Think of a Mel spectrogram as a graphical representation of sound, similar to how a sound wave can be visualized. Here’s a breakdown using an analogy for clarity:

Imagine you are at a bakery, and you want to bake a cake. You have the ingredients (text) and need a recipe (Mel spectrogram) to represent how to combine those ingredients (convert text to sound). Just as the cake needs precise measurements combined in a specific order, your text needs to be processed to turn it into audio.

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

# Initialize processor and model
processor = AutoProcessor.from_pretrained("MarcNg/fastspeech2-vi-infore")
fastspeech2 = TFAutoModel.from_pretrained("MarcNg/fastspeech2-vi-infore")

# Define the text to convert
text = "xin chào đây là một ví dụ về chuyển đổi văn bản thành giọng nói"
input_ids = processor.text_to_sequence(text)

# Get mel spectrograms
mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
    input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
    speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
    speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
    f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
    energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)

Step 3: Bonus – Convert Mel Spectrogram to Speech

Once you have your Mel spectrogram, the next step is to convert it into actual speech. Continuing with our bakery analogy, this is like taking your cake batter and putting it in the oven to bake it into a delicious cake. Here’s how you can achieve this:

mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en")

# Convert to audio
audio_before = mb_melgan.inference(mel_before)[0, :, 0]
audio_after = mb_melgan.inference(mel_after)[0, :, 0]

# Save audio as .wav files
sf.write("audio_before.wav", audio_before, 22050, "PCM_16")
sf.write("audio_after.wav", audio_after, 22050, "PCM_16")

# Play resulting audio
ipd.Audio("audio_after.wav")

Troubleshooting

If you encounter any issues during this process, here are a few troubleshooting ideas:

• Make sure you have TensorFlow and all necessary dependencies installed correctly.
• If the models don’t load, double-check the model names to ensure they are correctly spelled and available.
• If there are audio playback problems, make sure you have appropriate libraries like SoundFile installed.

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Conclusion

In this guide, we walked through how to install TensorFlow TTS, convert text into Mel spectrograms, and subsequently transform them into audio. As you create your own text-to-speech applications, remember that with a little experimentation and creativity, the sky’s the limit!

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.