#TFLite

Explore TensorFlow 2's capabilities for state-of-the-art speech synthesis with models like Tacotron-2, FastSpeech, and MelGAN. The project enhances training efficiency and inference speed, making it suitable for real-time use on mobile and embedded systems. It supports multiple languages and offers comprehensive documentation for easy integration. Learn more about innovations such as the HiFi-GAN vocoder and guided attention loss for high-quality speech synthesis.

TensorFlowASR implements notable automatic speech recognition frameworks like DeepSpeech2, Jasper, RNN Transducer, ContextNet, and Conformer, with conversion capabilities to TFLite for optimised resource usage. It supports both transducer and CTC architectures, featuring ready-to-use models and academic references. The software can be installed efficiently from source or PyPI, with Apple Silicon compatibility. Well-crafted tutorials facilitate its use for developers at any skill level, offering strategies for feature processing and model augmentations. The community's contribution is valued through structured development guidelines.

The onnx2tflite project simplifies the conversion of ONNX models to TensorFlow Lite, maintaining high precision and efficiency. It minimizes errors per element to less than 1e-5 compared to ONNX outputs and accelerates model output, achieving speeds 30% faster than other methods. This tool automatically aligns channel formats and supports the deployment of quantized models, including fp16 and uint8. Users can modify input and output layers, add custom operators, and benefit from a straightforward code structure, making it a versatile solution for AI model deployments.