The Aina Projects Catalan multi-speaker text-to-speech (TTS) model is a groundbreaking tool that allows users to generate synthetic speech in the beautiful Catalan language. In this guide, we will walk you through the process of setting up and using the model, as well as offer troubleshooting tips along the way.

Model Description

This model has been meticulously trained from scratch using the Coqui TTS toolkit, combining three diverse datasets:

• Festcat
• OpenSLR69
• Common Voice v12

We utilized 487 hours of recordings from 255 speakers, trimming and denoising the data in two separate datasets (festcat_trimmed_denoised and openslr69_trimmed_denoised).

How to Use the Aina TTS Model

Follow these steps to successfully synthesize speech:

1. Required Libraries

First, ensure you have the necessary libraries installed. You can do this by running the following command:

pip install git+https://github.com/coqui-ai/TTS@dev#egg=TTS

2. Synthesize Speech Using Python

Next, you can use the following Python script to synthesize speech:

import tempfile
import gradio as gr
import numpy as np
import os
import json
from typing import Optional
from TTS.config import load_config
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer

model_path = "Absolute path to the model checkpoint.pth"
config_path = "Absolute path to the model config.json"
speakers_file_path = "Absolute path to speakers.pth file"
text = "Text to synthesize"
speaker_idx = "Speaker ID"

synthesizer = Synthesizer(model_path, config_path, speakers_file_path, None, None, None)
wavs = synthesizer.tts(text, speaker_idx)

In this analogy, think of the TTS model as a professional chef. The ingredients for the dish (or the text to be synthesized) include the recipe (the model path), cooking techniques (the configuration path), and various flavorings (the speaker IDs). When combined, the chef (synthesizer) creates a delicious meal (the audio output).

Training Overview

The model is based on VITS, as proposed by Kim et al., and was trained using the following hyperparameters:

• Model: vits
• Batch Size: 16
• Learning Rate: 0.0001
• Optimizer: adam
• Training Steps: 730,962

Troubleshooting

If you encounter issues while using the Aina Projects model, here are some common troubleshooting tips:

• Make sure all paths provided in the code are absolute and correct.
• Verify that all required libraries are installed without errors.
• Check if the model dependencies are up to date and compatible with your current setup.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

The Aina Projects Catalan TTS model is a powerful tool for generating synthetic speech in Catalan, expanding the linguistic capabilities of TTS technology. With its easy setup and versatility, you can explore the world of synthetic speech with ease.

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.