Setting Up Text-to-Audio on Linux

This guide provides detailed steps on setting up a Text-to-Audio system on Ubuntu using PyTorch. We'll use a pre-trained model for text-to-audio synthesis, such as Tacotron 2 or WaveGlow, which can convert text into audio waveforms. You’ll learn how to install necessary dependencies and implement a simple example using PyTorch.

1. Install System Prerequisites

First, ensure your system is updated and that you have installed Python, Pip, and essential build tools. Open a terminal and run the following commands:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip build-essential git

This will ensure you have the necessary system dependencies to set up the environment.

2. Install PyTorch

To install PyTorch, follow the command below, which installs the stable version of PyTorch with CUDA (if you have a GPU). For a CPU-only version, remove the +cu118 part:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

If you do not have a GPU or want to use CPU only, you can install PyTorch like this:

pip install torch torchvision torchaudio

3. Install Additional Libraries

In addition to PyTorch, you’ll need a few more Python packages to work with text and audio:

pip install numpy matplotlib scipy

These libraries are used for audio processing and visualization.

4. Clone the Tacotron2 and WaveGlow Repositories

For text-to-audio generation, we will use Tacotron2 (for generating mel-spectrograms from text) and WaveGlow (to convert spectrograms to waveforms). Clone these repositories:

# Clone Tacotron2
git clone https://github.com/NVIDIA/tacotron2.git
cd tacotron2
pip install -r requirements.txt

# Clone WaveGlow
git clone https://github.com/NVIDIA/waveglow.git
cd waveglow
pip install -r requirements.txt

This installs the necessary dependencies for both Tacotron2 and WaveGlow, including compatible versions of PyTorch and other libraries.

5. Download Pre-trained Models

Next, download the pre-trained models for Tacotron2 and WaveGlow. You can use NVIDIA's pre-trained models:

# Download Tacotron2 pre-trained model
wget https://github.com/NVIDIA/tacotron2/releases/download/v1.0/tacotron2_statedict.pt -O tacotron2_statedict.pt

# Download WaveGlow pre-trained model
wget https://github.com/NVIDIA/waveglow/releases/download/v1.0/waveglow_256channels.pt -O waveglow_256channels.pt

Place these model files in the appropriate directories (e.g., inside the tacotron2 and waveglow folders).

6. Implement Text-to-Audio in Python

Now you can use both Tacotron2 and WaveGlow to generate audio from text. The following script demonstrates how to load the models, generate a mel-spectrogram, and convert it to audio:

import torch
import numpy as np
import matplotlib.pyplot as plt
from scipy.io.wavfile import write
from tacotron2.text import text_to_sequence
from waveglow.denoiser import Denoiser

# Load pre-trained Tacotron2 model
tacotron2 = torch.load('tacotron2_statedict.pt')
tacotron2.eval()

# Load pre-trained WaveGlow model
waveglow = torch.load('waveglow_256channels.pt')
waveglow.eval()

# Denoiser for cleaner audio
denoiser = Denoiser(waveglow)

# Function to convert text to mel-spectrogram using Tacotron2
def text_to_mel(text):
    sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
    sequence = torch.from_numpy(sequence).long()
    with torch.no_grad():
        mel_outputs, mel_outputs_postnet, _, _ = tacotron2.inference(sequence)
    return mel_outputs_postnet

# Function to convert mel-spectrogram to audio using WaveGlow
def mel_to_audio(mel):
    with torch.no_grad():
        audio = waveglow.infer(mel)
    audio = denoiser(audio, strength=0.01)[:, 0]
    return audio

# Example input text
text = "Hello, this is a text to audio conversion using PyTorch."

# Convert text to mel-spectrogram
mel = text_to_mel(text)

# Convert mel-spectrogram to audio
audio = mel_to_audio(mel)

# Save the audio to a WAV file
audio_numpy = audio.cpu().numpy()
write("output_audio.wav", 22050, audio_numpy)

# Plot the mel-spectrogram for visualization
plt.imshow(mel.cpu().numpy()[0], aspect='auto', origin='lower')
plt.colorbar()
plt.show()

This Python script performs the following steps:

  • Loads the pre-trained Tacotron2 and WaveGlow models.
  • Converts text into a mel-spectrogram using Tacotron2.
  • Converts the mel-spectrogram into an audio waveform using WaveGlow.
  • Saves the generated audio as a WAV file and visualizes the mel-spectrogram.

7. Test the System

Once you have the models and the script ready, you can test the text-to-audio system by running the script:

python3 text_to_audio.py

This will generate a file called output_audio.wav with the synthesized speech from the input text. You can play the file using any media player, such as VLC:

vlc output_audio.wav

8. Optimizing for GPU (Optional)

If you have a GPU available, PyTorch will automatically detect it and use it for faster model inference. To ensure your GPU is being used, check for CUDA availability:

import torch
print(torch.cuda.is_available())

If CUDA is available, PyTorch will utilize the GPU, which significantly speeds up the audio generation process.

9. Conclusion

You have successfully set up a Text-to-Audio system on Ubuntu using PyTorch. With Tacotron2 and WaveGlow, you can generate realistic audio from text input. This system can be further customized and expanded for different languages and voices by training or fine-tuning your own models.