![]() If users met running out of GPU memory error, then try to reduce batch size. Lang Lang: Franz Liszt - Love Dream (Liebestraum) ĭemo 2. workspaces/piano_transcription/checkpoints/main/Regress_onset_offset_frame_velocity_CRNN/loss_type=regress_onset_offset_frame_velocity_bce/augmentation=none/batch_size=12/300000_iterations.pthĭemo 1. workspaces/piano_transcription/statistics/main/Regress_onset_offset_frame_velocity_CRNN/loss_type=regress_onset_offset_frame_velocity_bce/augmentation=none/batch_size=12/statistics_00-22-33.pickle workspaces/piano_transcription/statistics/main/Regress_onset_offset_frame_velocity_CRNN/loss_type=regress_onset_offset_frame_velocity_bce/augmentation=none/batch_size=12/statistics.pklĭump statistics to. The training looks like: Namespace(augmentation='none', batch_size=12, cuda=True, early_stop=300000, filename='main', learning_rate=0.0005, loss_type='regress_onset_offset_frame_velocity_bce', max_note_shift=0, mini_data=False, mode='train', model_type='Regress_onset_offset_frame_velocity_CRNN', reduce_iteration=10000, resume_iteration=0, workspace='./workspaces/piano_transcription') The system is trained for 300k iterations for one week. The training uses a single Tesla-V100-PCIE-32GB card. Users may consider to reduce the batch size, or use multiple GPU cards to train this system. In total 29 GB GPU memoroy is required with a batch size of 12. It worth looking into runme.sh to see how the piano transcription system is trained. Combine piano note and piano pedal transcription systems.Īll training steps are described in runme.sh.Train piano pedal transcription system.Config dataset path and your workspace.Statistics of MAESTRO V2.0.0 : SplitĪfter downloading, the dataset looks like: dataset_rootĮxecute the commands line by line in runme.sh, including: MAESTRO consists of over 200 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms. We use MAESTRO dataset V2.0.0 to train the piano transcription system. This section provides instructions if users would like to train a piano transcription system from scratch. Train a piano transcription system from scratch Transcribed_dict = anscribe(audio, 'cut_liszt.mid') Transcriptor = PianoTranscription(device='cuda') # 'cuda' | 'cpu' ![]() (audio, _) = load_audio('resources/cut_liszt.mp3', sr=sample_rate, mono=True) From piano_transcription_inference import PianoTranscription, sample_rate, load_audio
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