Welcome to the world of text-to-speech! In this article, we will guide you through the process of using the ESPnet JETS TTS model exported to ONNX. This guide is perfect whether you’re a novice looking to explore TTS technology or a seasoned developer needing to integrate speech synthesis into your applications.

Prerequisites

• Python installed on your machine.
• The necessary libraries: `espnet_onnx`, `txtai`, `onnxruntime`, `soundfile`, and `ttstokenizer`.
• Access to the LJSpeech dataset.

Setting Up Your Environment

We will be using a couple of libraries to accomplish our task. Start by ensuring you have `txtai` and `onnxruntime` set up. To install these, you can run the following command in your terminal:

pip install txtai onnxruntime

Using the Model with txtai

The `txtai` library provides a straightforward way to utilize the JETS model. Here’s how you can leverage it:

import soundfile as sf
from txtai.pipeline import TextToSpeech

# Build pipeline
tts = TextToSpeech("NeuML/ljspeech-jets-onnx")

# Generate speech
speech, rate = tts("Say something here")

# Write to file
sf.write("out.wav", speech, rate)

Think of it like a Magician

Imagine you have a magician who can transform text into spoken words. The magician (our TTS model) takes your written script (the text you provide) and performs their magic (transforms it into audio) which is then recorded onto a magic tape (the WAV file). Just like magic, it’s seamless!

Using the Model with ONNX

If you wish to run the model directly using ONNX, follow these steps:

import onnxruntime
import soundfile as sf
import yaml
from ttstokenizer import TTSTokenizer

# Load the configuration
with open("ljspeech-jets-onnx/config.yaml", "r", encoding="utf-8") as f:
    config = yaml.safe_load(f)

# Create model
model = onnxruntime.InferenceSession("ljspeech-jets-onnx/model.onnx", providers=["CPUExecutionProvider"])

# Create tokenizer
tokenizer = TTSTokenizer(config["token"]["list"])

# Tokenize inputs
inputs = tokenizer("Say something here")

# Generate speech
outputs = model.run(None, {"text": inputs})

# Write to file
sf.write("out.wav", outputs[0], 22050)

A Recipe for Delicious Audio

Consider the process as following a recipe. You collect your ingredients (text input), measure them precisely (tokenization), mix them well in the pot (the ONNX model), and finally, you pour the mixture into a serving dish (the WAV file). Just like a well-cooked dish, a good amount of preparation results in a delicious outcome!

Troubleshooting

If you run into issues while implementing the JETS TTS model, here are some troubleshooting tips:

• Ensure that all required libraries are installed and up to date.
• Verify that your input text is correctly formatted and tokenized if using ONNX directly.
• If you encounter performance issues, check if your model is set to run on the correct CPU execution provider.

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

How to Export ESPnet Models to ONNX

If you’re interested in exporting your own ESPnet models to ONNX format, you can find detailed instructions here.

Final Thoughts

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.

Happy coding and enjoy your journey into the fascinating realm of text-to-speech technology!