Are you fascinated by turning text into spoken words? With TensorFlowTTS, this marvel is achievable with a few simple commands. This blog post will guide you through the process of installing TensorFlowTTS, converting text into Mel spectrograms, and finally creating audible speech.

Step 1: Installing TensorFlowTTS

To kick off your journey, you need to install TensorFlowTTS. Run the following command in your terminal:

pip install TensorFlowTTS

Step 2: Converting Text to Mel Spectrogram

Next, you will convert your text into a Mel spectrogram. This is a crucial step that prepares the text for audio synthesis. Let’s imagine it like baking a cake: the Mel spectrogram is your cake batter that needs to be well mixed before putting it in the oven.

Here’s the Python code you need:

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

# Load the processor and model
processor = AutoProcessor.from_pretrained("MarcNgfastspeech2-vi-infore")
fastspeech2 = TFAutoModel.from_pretrained("MarcNgfastspeech2-vi-infore")

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

# Create Mel spectrogram
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),
)

In this code:

• We import necessary libraries to handle audio processing.
• We load our text-to-speech model.
• We then convert our text into input IDs used for creating the Mel spectrogram.

Step 3: Converting Mel Spectrogram to Speech

Finally, it’s time to transform the Mel spectrogram back into audio waveforms. Continuing with our baking analogy, this stage is like taking the baked cake out of the oven and preparing it for decoration!

Here’s how to do it:

mb_melgan = TFAutoModel.from_pretrained("tensorspeechtts-mb_melgan-ljspeech-en")
audio_before = mb_melgan.inference(mel_before)[0, :, 0]
audio_after = mb_melgan.inference(mel_after)[0, :, 0]

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

In this segment:

• We load a model that transforms the Mel spectrogram into audio.
• We then generate audio before and after applying the model.
• Lastly, we save these audio clips to WAV files for your listening pleasure!

Troubleshooting

If you encounter any issues, consider the following solutions:

• Ensure that all required libraries are installed and up to date.
• Check for any typos in your code.
• Verify that your TensorFlow version is compatible with TensorFlowTTS.
• If there are issues with audio playback, ensure that your sound files are saved correctly and your audio device is functioning.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With these steps, you’ve learned how to convert text into speech using TensorFlowTTS. This is an exciting step into the world of AI and machine learning where text and speech interact. 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.