Setting Up Text-to-Speech on Linux

This guide explains how to set up a Text-to-Speech (TTS) system on Ubuntu, including instructions for Llama.cpp, a lightweight C++ implementation for AI models. We'll use it along with other necessary tools to implement text-to-speech functionality.

1. Install System Prerequisites

Ensure your system is updated and has the necessary build tools installed. Open a terminal and run the following commands:

sudo apt update
sudo apt upgrade
sudo apt install build-essential python3 python3-pip git cmake libopenblas-dev
    

This installs essential development tools and libraries like Python, Git, and CMake that are required for compiling and running C++ programs.

2. Install Llama.cpp

Llama.cpp is a C++ implementation of the LLaMA (Large Language Model) that can be used for various AI tasks, including text-based tasks, though it is not directly designed for TTS. Here’s how to clone and set it up:

# Clone the Llama.cpp repository
git clone https://github.com/ggerganov/llama.cpp

# Navigate to the directory
cd llama.cpp

# Compile the project using make
make
    

This will compile Llama.cpp and make it ready for use.

3. Install Python Text-to-Speech Libraries

Since Llama.cpp is not directly designed for text-to-speech, we’ll use additional TTS libraries in Python to handle the speech synthesis. We'll use pyttsx3, a cross-platform TTS library, for this purpose.

pip install pyttsx3
    

This installs pyttsx3, which supports text-to-speech on Ubuntu.

4. Integrating Llama.cpp with Text-to-Speech

Now that we have both Llama.cpp and pyttsx3, you can integrate a text model to generate text and then convert that text into speech. The following Python script demonstrates how to achieve this:

import os
import pyttsx3

# Function to generate text using Llama.cpp
def generate_text_with_llama(input_text):
    # Run Llama.cpp to generate text
    # Replace './main' with the actual binary of Llama.cpp
    stream = os.popen(f'./main -m ./models/7B/ggml-model.bin -p "{input_text}"')
    output = stream.read()
    return output

# Function to convert text to speech
def text_to_speech(text):
    engine = pyttsx3.init()
    engine.say(text)
    engine.runAndWait()

# Example input to generate text
input_text = "What is the future of artificial intelligence?"

# Generate text with Llama.cpp
generated_text = generate_text_with_llama(input_text)
print("Generated Text:", generated_text)

# Convert generated text to speech
text_to_speech(generated_text)

    

This script does the following:

• Generates text using Llama.cpp: The function generate_text_with_llama runs the Llama.cpp binary to produce text output based on the input query.
• Converts the text to speech: The text_to_speech function uses pyttsx3 to convert the generated text into speech.

5. Download a Pre-trained Model for Llama.cpp

For Llama.cpp to generate meaningful text, you need to download a pre-trained LLaMA model. Here’s how you can download a model and place it in the correct directory:

# Create a directory for models
mkdir models

# Download a 7B model (you need to find a valid source for the model, for instance, from Meta's release)
cd models
wget [link-to-7B-model] -O ggml-model.bin
    

Ensure the model file is placed in the ./models/7B/ directory for Llama.cpp to work correctly. Replace [link-to-7B-model] with the actual download link to the model.

6. Test the Text-to-Speech System

Once Llama.cpp is set up and the model is downloaded, you can run the Python script provided above to generate text and convert it into speech. Run the following command:

python3 your_script_name.py
    

This will generate text using Llama.cpp and convert it to speech using pyttsx3.

7. (Optional) Use GPU for Faster Processing

If you have a GPU, you can optimize Llama.cpp for GPU usage. Install the necessary dependencies and recompile Llama.cpp:

# Install CUDA and cuBLAS
sudo apt install nvidia-cuda-toolkit

# Recompile Llama.cpp with GPU support
make clean
make LLAMA_CUBLAS=1
    

This recompiles Llama.cpp with GPU acceleration for faster inference.

8. Conclusion

You have successfully set up a Text-to-Speech system on Ubuntu using Llama.cpp for text generation and pyttsx3 for speech synthesis. This system can generate AI-powered text responses and convert them to speech in real time. Feel free to expand on this by incorporating additional features or using different pre-trained models for enhanced text generation.