

Optispeech
Overview :
OptiSpeech is an efficient, lightweight, and fast text-to-speech model specifically designed for device-side text-to-speech conversion. Leveraging advanced deep learning techniques, it converts text into naturally sounding speech, making it suitable for applications that require speech synthesis on mobile devices or embedded systems. The development of OptiSpeech was significantly accelerated by GPU resources provided by Pneuma Solutions.
Target Users :
OptiSpeech is primarily targeted at developers and researchers, especially those who need to implement text-to-speech (TTS) functionality on device endpoints. Its lightweight and efficient design makes it ideal for speech interaction in mobile applications, smart home devices, and in-vehicle systems.
Use Cases
Implement speech assistant features on smartphones.
Provide natural voice feedback for smart home devices.
Deliver voice outputs for navigation instructions in vehicular systems.
Features
Supports a command-line API for rapid speech synthesis.
Offers a Python API for easy integration into applications.
Allows adjustments of various speech synthesis parameters, including speed, pitch, and energy.
Supports ONNX format export for easy model deployment and usage across different platforms.
Provides a variety of model architecture options, including ConvNeXt, Transformer, Conformer, and LightSpeech.
Enables dependency management and runtime synchronization using Rye, simplifying the development process.
How to Use
1. Prepare the dataset and format it according to requirements, then process it with the preprocess_dataset script.
2. Select a model architecture and specify it in the configuration file according to your needs.
3. Use Rye to synchronize the Python runtime and dependencies.
4. Invoke OptiSpeech for text-to-speech conversion through the command-line API or Python API.
5. Adjust speech synthesis parameters (such as speed, pitch, energy) to meet specific requirements.
6. Export the trained model in ONNX format for deployment across different platforms.
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