Text-to-speech (TTS) technology allows machines to convert written text into spoken words. With the release of the ESPnet VITS Text-to-Speech model, you can easily create lifelike speech from text using ONNX (Open Neural Network Exchange). In this article, we’ll dive into how to utilize this powerful tool effectively.

What You Should Know Before Getting Started

The ESPnet VITS model might sound complex, but with a little guidance, you can efficiently implement it to suit your needs. This model has been exported using the espnet_onnx library, which streamlines the process of using TTS with ONNX.

Using the Model with txtai

The txtai library provides an intuitive interface to integrate TTS capabilities. Follow these steps:

• Import necessary libraries:

import soundfile as sf
from txtai.pipeline import TextToSpeech

• Build the pipeline:

tts = TextToSpeech("NeuML/ljspeech-vits-onnx")

• Generate speech:

speech = tts("Say something here")

• Write to file:

sf.write("out.wav", speech, 22050)

An Analogy for Better Understanding

Think of using the ESPnet VITS TTS model like baking a cake. Firstly, you gather the ingredients (libraries and model). Then, you mix the ingredients in an organized manner (building the pipeline) to create your batter (text input). Following this, you place it into the oven (the processing stage), where the magic happens, transforming your batter into a delicious cake (outputting speech file). Finally, you take your cake out and present it as a masterpiece (writing to a file).

Using ONNX Directly

If you wish to run the model directly with ONNX, you’ll need to tokenize the input text. Here’s how:

• Import relevant libraries:

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

• Load your configuration:

with open("ljspeech-vits-onnx/config.yaml", "r", encoding="utf-8") as f:
    config = yaml.safe_load(f)

• Create the model session:

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

• Initialize the tokenizer:

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

• Tokenize your 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 run into issues during the setup or execution, here are some troubleshooting tips:

• Ensure that all libraries are correctly installed and updated. Use pip or conda to manage your packages.
• Double-check the paths for your model and config files. Typos can lead to file not found errors.
• If the generated audio sounds incorrect, verify that your input text has been properly tokenized.
• For any further questions or challenges, check for existing issues on the espnet_onnx GitHub repository.

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Exporting the Model

If you want to know more about exporting ESPnet models to ONNX, detailed documentation is available here.

Conclusion

Implementing the ESPnet VITS TTS model for your projects can drastically enhance the way your applications communicate. By following the straightforward steps above, you’ll be well on your way to creating engaging auditory experiences.

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.