podlm-public is a project that utilizes AI technology to transform URL content into podcasts and publish them on the 小宇宙 platform. Through its automated service, it allows content creators to effortlessly convert web information into audio format, broadening the channels for information dissemination and enhancing content acceptance. The product primarily targets users who need to convert text or web content into podcast audio, such as podcast producers and content creators.

Multispecies-whale-detection is an open-source project developed by Google, aimed at detecting and classifying whale sounds across different species and geographic regions through neural networks. This tool helps researchers and conservation organizations better understand and protect marine biodiversity.

Draw an Audio is an innovative video-to-audio synthesis technology that generates high-quality synchronized audio based on video content through multi-command control. This technology not only enhances the controllability and flexibility of audio generation but also enables multi-stage mixed audio production, showcasing a broader range of practical applications.

Fish Speech V1.4 is a leading text-to-speech (TTS) model trained on 700,000 hours of audio data in multiple languages. This model supports eight languages, including English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic, making it a powerful tool for multilingual text-to-speech conversion.

Easy Voice Toolkit is an AI voice toolkit based on open-source voice projects, providing various automated audio tools including speech model training. The toolkit seamlessly integrates to create a complete workflow, allowing users to selectively use these tools or utilize them in sequence to gradually convert raw audio files into ideal speech models.

FreGrad is a lightweight and fast frequency-aware diffusion audio codec designed to generate realistic audio. Its framework includes discrete wavelet transform, frequency-aware expansion convolution, and a series of quality enhancement techniques for model generation. In experiments, FreGrad achieves a 3.7x speedup in training speed and a 2.2x speedup in inference speed compared to baseline models, while reducing model size by 0.6x (only 1.78 million parameters) without sacrificing output quality.

Ultimate Vocal Remover GUI is a vocal removal tool that utilizes deep neural network technology. The core developer trained all provided models except for the Demucs v3 and v4 4-channel models. This application uses advanced source separation models to remove vocals from audio files. It runs effectively without any additional prerequisites and is compatible with Windows 10 and above.

COMOSVC is a singing pitch transformation technology based on consistency models that achieves high-quality transformation effects and fast sampling speed. This technology first designs a teacher model based on diffusion for the singing pitch transformation task and then uses self-consistency properties for knowledge distillation to achieve one-step sampling. Compared to the most advanced singing pitch transformation systems based on diffusion, COMOSVC maintains, and even exceeds, comparable transformation performance while significantly faster inference speed.

podlm-public is a project that utilizes AI technology to transform URL content into podcasts and publish them on the 小宇宙 platform. Through its automated service, it allows content creators to effortlessly convert web information into audio format, broadening the channels for information dissemination and enhancing content acceptance. The product primarily targets users who need to convert text or web content into podcast audio, such as podcast producers and content creators.

Multispecies-whale-detection is an open-source project developed by Google, aimed at detecting and classifying whale sounds across different species and geographic regions through neural networks. This tool helps researchers and conservation organizations better understand and protect marine biodiversity.

Draw an Audio is an innovative video-to-audio synthesis technology that generates high-quality synchronized audio based on video content through multi-command control. This technology not only enhances the controllability and flexibility of audio generation but also enables multi-stage mixed audio production, showcasing a broader range of practical applications.

Fish Speech V1.4 is a leading text-to-speech (TTS) model trained on 700,000 hours of audio data in multiple languages. This model supports eight languages, including English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic, making it a powerful tool for multilingual text-to-speech conversion.

Easy Voice Toolkit is an AI voice toolkit based on open-source voice projects, providing various automated audio tools including speech model training. The toolkit seamlessly integrates to create a complete workflow, allowing users to selectively use these tools or utilize them in sequence to gradually convert raw audio files into ideal speech models.

Voice Isolator, an AI audio solution developed by ElevenLabs, can isolate clear vocals from various audio sources, removing unwanted background noises like street sounds and microphone feedback. This is beneficial for post-production in film, podcasting, and interviews, significantly improving audio quality and enhancing post-production efficiency.

ElevenLabs' Text-to-Sound API allows users to generate high-quality sound effects based on short text descriptions. These sound effects can be used in a variety of scenarios, such as game development and music production applications. The API utilizes advanced audio synthesis technology to dynamically generate sound effects based on text prompts, providing users with an innovative sound design tool.

FreGrad is a lightweight and fast frequency-aware diffusion audio codec designed to generate realistic audio. Its framework includes discrete wavelet transform, frequency-aware expansion convolution, and a series of quality enhancement techniques for model generation. In experiments, FreGrad achieves a 3.7x speedup in training speed and a 2.2x speedup in inference speed compared to baseline models, while reducing model size by 0.6x (only 1.78 million parameters) without sacrificing output quality.