Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Product Overview
An end-to-end source separation in the time-domain. Vocal separation of an input source audio. It uses a convolutional neural network to separate the background music/noise and vocals in an input wav/mp3 audio file. The model package is an adaptation of the U-Net architecture to the one-dimensional time domain. The prediction is in the form of two audio files comprising of the vocals and accompaniment (background noise/ music). The output is a zipped folder containing the above two audio files.
Key Data
Highlights
Model Package to separate an audio file into vocals and accompaniment (background music/ noise).
Usable in cases of noise removal from input or generating Karaoke for songs.
Can also be used for noise cancellation in the audio files.
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Usage Information
Model input and output details
Input
Summary
Model expects audio input in WAV or MP3 file format.
Supported audio format: WAV/MP3 Supported Content type: application/x-recordio-protobuf
Limitations for input type
Maximum file size is 5 MB
Input MIME type
application/x-recordio-protobufSample input data
Output
Summary
Output audio format: WAV Suported Content type: application/x-recordio-protobuf
Output MIME type
application/x-recordio-protobufSample output data
Sample notebook
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Support Information
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