Fine-Tuned TTS Models

Introduction

Fine-Tuned TTS Models are a set of models fine-tuned for text-to-speech (TTS) tasks. These models utilize the ONNX library, offering efficient inference capabilities.

Architecture

The models are built using ONNX, a popular format for deep learning models that ensures high performance and interoperability across various platforms and frameworks.

Training

The training process for these fine-tuned TTS models involves adapting pre-existing models to specific datasets for enhanced performance in text-to-speech applications. Details on the training methodology and datasets used are specific to the model version and can be found in the model card documentation.

Guide: Running Locally

To run the Fine-Tuned TTS Models locally, follow these steps:

1. Clone the Repository: Use Git to clone the model repository.

   git clone https://huggingface.co/drewThomasson/fineTunedTTSModels
   

2. Install Dependencies: Ensure that ONNX and any other required libraries are installed.

   pip install onnxruntime
   

3. Load the Model: Use ONNX Runtime to load the model and perform inference.

   import onnxruntime as ort
   session = ort.InferenceSession("path_to_model.onnx")
   

4. Perform Inference: Prepare your input data and use the session to perform inference.

   inputs = {"input": your_data}
   outputs = session.run(None, inputs)
   

Cloud GPUs

For improved performance, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure to handle more intensive computations efficiently.

License

The Fine-Tuned TTS Models are available under the Apache-2.0 license, allowing for broad use in both commercial and non-commercial applications.