Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
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Enhancement of Model Robustness Against Noisy Input Data #403
I hope this message finds you well. I have been thoroughly impressed with the capabilities and performance of the AudioCraft library, particularly with its state-of-the-art models for audio generation. As an avid user and advocate for the library, I would like to propose an enhancement that could potentially elevate the robustness of the models within the AudioCraft suite.
Through my experimentation, I have observed that the models, while exceptional in handling clean and well-curated datasets, exhibit a degree of sensitivity to noisy input data. This is particularly noticeable in scenarios where the audio data may be sourced from less-than-ideal recording environments, which is often the case in real-world applications.
To this end, I believe that integrating a preprocessing module or noise reduction algorithm within the AudioCraft pipeline could significantly improve the usability and versatility of the models. Such an enhancement would not only bolster the models' performance in adverse acoustic conditions but also expand the library's applicability to a wider range of audio processing tasks.
I am aware that this is no small feat and that it may involve extensive research and development efforts. However, I am confident that this addition would be greatly appreciated by the community and could set a new benchmark for audio generation models.
I am keen to hear your thoughts on this suggestion and would be delighted to contribute to the discussion or assist in any way possible.
Dear AudioCraft Contributors,
I hope this message finds you well. I have been thoroughly impressed with the capabilities and performance of the AudioCraft library, particularly with its state-of-the-art models for audio generation. As an avid user and advocate for the library, I would like to propose an enhancement that could potentially elevate the robustness of the models within the AudioCraft suite.
Through my experimentation, I have observed that the models, while exceptional in handling clean and well-curated datasets, exhibit a degree of sensitivity to noisy input data. This is particularly noticeable in scenarios where the audio data may be sourced from less-than-ideal recording environments, which is often the case in real-world applications.
To this end, I believe that integrating a preprocessing module or noise reduction algorithm within the AudioCraft pipeline could significantly improve the usability and versatility of the models. Such an enhancement would not only bolster the models' performance in adverse acoustic conditions but also expand the library's applicability to a wider range of audio processing tasks.
I am aware that this is no small feat and that it may involve extensive research and development efforts. However, I am confident that this addition would be greatly appreciated by the community and could set a new benchmark for audio generation models.
I am keen to hear your thoughts on this suggestion and would be delighted to contribute to the discussion or assist in any way possible.
Thank you for your time and consideration.
Best regards, yihong1120