If you’ve ever wanted to turn text into evocative speech, the ESPnet JETS Text-to-Speech (TTS) model is your trusty companion. Exported to ONNX via the espnet_onnx library, this model takes the complexity out of voice synthesis. In this article, we’ll explore how to leverage the txtai library for simplistic use of this powerful tool.

How to Use the ESPnet JETS TTS Model

We’ll break down the usage into two sections: one for using the built-in pipeline of txtai, and another using ONNX directly. Think of this process like assembling a delicious sandwich. With the right ingredients (tools) and the right method, you’ll have a mouth-watering result (speech) in no time!

1. Using txtai

txtai provides a neat interface to make the process simpler. Just follow these steps:

• Install the necessary packages.
• Prepare your pipeline.
• Generate and save the speech.

Here’s how you can do it:

import soundfile as sf
from txtai.pipeline import TextToSpeech

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

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

# Write to file
sf.write("out.wav", speech, 22050)

2. Using ONNX Directly

If you prefer to work directly with ONNX (like handcrafting your sandwich from scratch), follow these steps:

• Ensure your files are ready and downloaded locally.
• Load the configuration.
• Tokenize your input text.
• Run the model.

Follow this code to accomplish it:

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

# This example assumes the files have been downloaded locally
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)

Troubleshooting Ideas

If you encounter any hiccups along the way, don’t worry! Here are a few troubleshooting tips:

• Model Not Loading: Ensure that the model path is correctly set and the model files have been downloaded correctly.
• Inconsistent Audio Quality: Double-check your configuration files and ensure you’re using the right audio sampling rate.
• Tokenization Issues: Ensure the tokenizer is correctly configured with the right definitions from the config YAML file.
• General Dependency Issues: Check if all necessary libraries are installed and up to date.

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Conclusion

By following these instructions, you have the tools to transform text into speech seamlessly. Just like a well-crafted sandwich, the combination of the right ingredients (the model and libraries) with the right process will yield delightful results.

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.