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Audiolcm
AudioLCM
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Audiolcm
AI text translation and voiceAI voice generation#text-to-audio#speech synthesis#audio generation#PyTorchStandard PicksOpen Source
Overview :
AudioLCM is a text-to-audio generation model implemented in PyTorch. It generates high-quality and efficient audio using a latent consistency model. Developed by Huadai Liu and others, it provides an open-source implementation and pre-trained models. It can convert text descriptions into near-realistic audio, holding significant application value, particularly in areas like speech synthesis and audio production.
Target Users :
The AudioLCM model is primarily aimed at audio engineers, speech synthesis researchers and developers, as well as scholars and enthusiasts interested in audio generation technology. It is suitable for applications that require automatic conversion of text descriptions to audio, such as virtual assistants, audiobook production, and language learning tools.
Total Visits： 474.6M
Top Region： US(19.34%)
Website Views ： 83.4K
Use Cases
Use AudioLCM to generate audiobook or podcast audio for specific text.
Convert historical figures' speeches into realistic voices for educational or exhibition purposes.
Generate customized voiceovers for video game or animation characters, enhancing their personality and expressiveness.
Features
Supports high-fidelity audio generation from text.
Provides pre-trained models for users to quickly get started.
Allows users to download weights to support custom datasets.
Provides detailed training and inference code, facilitating user learning and secondary development.
Can handle mel-spectrogram generation, providing necessary intermediate representations for audio synthesis.
Supports training of variational autoencoders and diffusion models to generate high-quality audio.
Provides evaluation tools to calculate audio quality metrics such as FD, FAD, IS, and KL.
How to Use
Clone the AudioLCM GitHub repository to your local machine.
Prepare an environment with an NVIDIA GPU and CUDA cuDNN, following the instructions in the README.
Download required dataset weights and prepare dataset information as guided.
Run the mel-spectrogram generation script to prepare intermediate representations for audio synthesis.
Train a variational autoencoder (VAE) to learn the latent mapping between text and audio.
Train a diffusion model with the pre-trained VAE model to generate high-quality audio.
Evaluate the generated audio quality using the provided tools, such as calculating FD, FAD metrics.
Fine-tune and optimize the model based on individual needs to adapt to specific application scenarios.
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