AudioLCM

Preview：

Introduce：

AudioLCM is a text-to-audio generation model based on PyTorch implementation, which uses the underlying consistency model to generate high-quality and efficient audio. The model, developed by Huadai Liu et al., provides an open source implementation and pre-training model. It can transform text description into near-real audio, which has important application value, especially in the fields of speech synthesis and audio production.

Stakeholders:
The AudioLCM model is primarily aimed at audio engineers, speech synthesis researchers and developers, as well as academics and enthusiasts interested in audio generation technology. It is suitable for applications where text descriptions need to be automatically converted to audio, such as virtual assistants, audiobooks, language learning tools, etc.
Usage Scenario Examples:

• Use AudioLCM to generate reading audio of specific texts for audio books or podcasts.
• Transform the speeches of historical figures into realistic voices for educational or exhibition use.
• Generate customized voice for video game or animated characters to enhance character personality and expressiveness.

The features of the tool:

• Supports high fidelity generation from text to audio.
• A pre-trained model is provided for users to get started quickly.
• Allows users to download weights to support custom datasets.
• The detailed training and inference codes are provided to facilitate user learning and secondary development.
• The ability to process the generation of mel spectrograms provides the necessary intermediate representation for audio synthesis.
• Supports the training of variational autoencoders and diffusion models to generate high-quality audio.
• Evaluation tools are provided to calculate audio quality indicators such as FD, FAD, IS, KL, etc.

Steps for Use:

• Clone AudioLCM’s GitHub repository to a local machine.
• Prepare the NVIDIA GPU and CUDA cuDNN environment according to the instructions in README.
• Download the required data set and follow the instructions to prepare the data set information.
• Run the mel spectrogram generation script to prepare an intermediate representation for audio synthesis.
• Train variational autoencoders (VAE) to learn potential mappings between text and audio.
• Using a trained VAE model, the diffusion model is trained to produce high-quality audio.
• Use evaluation tools to evaluate the quality of the generated audio, such as calculating FD, FAD and other indicators.
• Models are fine-tuned and optimized to suit specific application scenarios according to individual needs.

Tool’s Tabs: Text to audio, speech synthesis