Metadata-Version: 2.1
Name: piano-transcription-inference
Version: 0.0.1
Summary: Piano transcription inference toolbox
Home-page: UNKNOWN
Author: Qiuqiang Kong
Author-email: qiuqiangkong@gmail.com
License: UNKNOWN
Description: # Piano transcription inference
        
        This toolbox is a piano transcription inference package that can be easily installed. Users can transcribe their favorite piano recordings to MIDI files after installation. To see how the piano transcription system is trained, please visit: https://github.com/bytedance/piano_transcription.
        
        ## Demos
        Here is a demo of our piano transcription system: https://www.youtube.com/watch?v=5U-WL0QvKCg
        
        ## Installation
        Install PyTorch (>=1.4) following https://pytorch.org/
        
        ```
        $ python3 setup.py install
        ```
        
        ## Usage
        ```
        python3 example.py --audio_path='resources/cut_liszt.mp3' --output_midi_path='cut_liszt.mid' --cuda
        ```
        
        For example:
        ```
        import librosa
        from piano_transcription_inference import PianoTranscription, sample_rate
        
        # Load audio
        (audio, _) = librosa.core.load('resources/cut_liszt.mp3', sr=sample_rate, mono=True)
        
        # Transcriptor
        transcriptor = PianoTranscription(device=device)
        
        # Transcribe and write out to MIDI file
        transcribed_dict = transcriptor.transcribe(audio, 'cut_liszt.mid')
        ```
        
        ## Visualization of piano transcription
        
        **Demo.** Lang Lang: Franz Liszt - Love Dream (Liebestraum) [[audio]](resources/cut_liszt.mp3) [[transcribed_midi]](resources/cut_liszt.mid)
        
        <img src="resources/cut_liszt.png">
        
        ## Applications
        
        We have built a large-scale classical piano MIDI dataset https://github.com/bytedance/GiantMIDI-Piano using our piano transcription system.
        
        ## Cite
        [1] High-resolution Piano Transcription with Pedals by Regressing Onsets and Offsets Times, [To appear], 2020
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
