Methods and apparatus, including computer program products, are provided for receiving, at a bidirectional recurrent neural network, a music file preprocessed to include at least one token data inserted within at least one location in the music file in order to enable varying the music file; generating, by the bidirectional recurrent neural network, an output music file, wherein the bidirectional recurrent neural network generates music data to replace the at least one token data; and providing, by the bidirectional recurrent neural network, the output music file representing a varied version of the music file. Related apparatus, systems, methods, and articles are also described.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a bidirectional recurrent neural network, a music file preprocessed to include at least one token data inserted within at least one location in the music file in order to enable varying the music file; generating, by the bidirectional recurrent neural network, an output music file, wherein the bidirectional recurrent neural network generates music data to replace the at least one token data; and providing, by the bidirectional recurrent neural network, the output music file representing a varied version of the music file. 2. The method of claim 1 further comprising: varying the at least one location of the at least one data token. 3. The method of claim 2 further comprising: generating, based on the varied at least one location of the at least one data token, another output music file, wherein the other output music file represents another varied version of the music file. 4. The method of claim 2 further comprising: receiving, from a user interface at a user equipment, the at least one location. 5. The method of claim 1, wherein the output music file represents the varied at least one of timing, rhythm, melody, amplitude, pitch, or timbre. 6. The method of claim 1, wherein the bidirectional recurrent neural network includes a first hidden layer configured to process backward states of an input music file, and a second hidden layer configured to process forward states of the input music file. 7. The method of claim 1 further comprising: receiving at least one parameter to configure the bidirectional recurrent neural network, wherein the at least one parameter is obtained as part of a training phase during which a neural network trains to replace gaps in music with varied music. 8. The method of claim 1, wherein the at least one token data comprises at least one of a value or a character representative of noise in the music file. 9. The method of claim 1 further comprising: controlling variation in the output music file by at least mixing the output music file with the music file, wherein the music file represent the original music file without preprocessing to include the at least one token data. 10. The method of claim 9, wherein the mixing comprises replacing one or more values in the output music file with corresponding values from the original music file. 11. An apparatus, comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: receive, at the apparatus, a music file preprocessed to include at least one token data inserted within at least one location in the music file in order to enable varying the music file; generate, by the apparatus, an output music file, wherein the bidirectional recurrent neural network generates music data to replace the at least one token data; and provide, by the apparatus, the output music file representing a varied version of the music file. 12. The apparatus of claim 11, wherein the apparatus is further configured to at least vary the at least one location of the at least one data token. 13. The apparatus of claim 12, wherein the apparatus is further configured to at least generate, based on the varied at least one location of the at least one data token, another output music file, wherein the other output music file represents another varied version of the music file. 14. The apparatus of claim 12, wherein the apparatus is further configured to at least receive, from a user interface at the apparatus, the at least one location. 15. The apparatus of claim 11, wherein the output music file represents the varied at least one of timing, rhythm, melody, amplitude, pitch, or timbre. 16. The apparatus of claim 11, wherein the apparatus comprises a bidirectional recurrent neural network including a first hidden layer configured to process backward states of an input music file, and a second hidden layer configured to process forward states of the input music file. 17. The apparatus of claim 11, wherein the apparatus is further configured to at least receive at least one parameter to configure the apparatus as a bidirectional recurrent neural network, wherein the at least one parameter is obtained as part of a training phase during which a neural network trains to replace gaps in music with varied music. 18. The apparatus of claim 11, wherein the at least one token data comprises at least one of a value or a character representative of noise in the music file. 19. The apparatus of claim 11 wherein the apparatus is further configured to at least control variation in the output music file by at least mixing the output music file with the music file, wherein the music file represent the original music file without preprocessing to include the at least one token data. 20. The apparatus of claim 11, wherein the apparatus is further configured to at least mix by replacing one or more values in the output music file with corresponding values from the original music file. 21. The apparatus of claim 11, wherein the apparatus comprises at least one of a bidirectional recurrent neural network or a user equipment. 22. A non-transitory computer-readable storage medium including code which when executed causes operations comprising: receiving, at a bidirectional recurrent neural network, a music file preprocessed to include at least one token data inserted within at least one location in the music file in order to enable varying the music file; generating, by the bidirectional recurrent neural network, an output music file, wherein the bidirectional recurrent neural network generates music data to replace the at least one token data; and providing, by the bidirectional recurrent neural network, the output music file representing a varied version of the music file. 23. An apparatus comprising: means for receiving, at a bidirectional recurrent neural network, a music file preprocessed to include at least one token data inserted within at least one location in the music file in order to enable varying the music file; means for generating, by the bidirectional recurrent neural network, an output music file, wherein the bidirectional recurrent neural network generates music data to replace the at least one token data; and means for providing, by the bidirectional recurrent neural network, the output music file representing a varied version of the music file.