├── .gitignore ├── .gitattributes ├── .vscode └── settings.json ├── Output ├── Charles Cornell's Smooth Heart and Soul.mp3 └── That One Song That Everybody Plays On Piano.mid ├── Input └── That One Song That Everybody Plays On Piano.mp3 ├── piano_transcription_inference_data ├── note_F1=0.9677_pedal_F1=0.9186.pth └── TMI.md ├── requirements.txt ├── README.md ├── RUN.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | **/.DS_Store -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | *.pth filter=lfs diff=lfs merge=lfs -text 2 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "git.ignoreLimitWarning": true 3 | } -------------------------------------------------------------------------------- /Output/Charles Cornell's Smooth Heart and Soul.mp3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BambooOnFire/Piano-AI-Transcription/HEAD/Output/Charles Cornell's Smooth Heart and Soul.mp3 -------------------------------------------------------------------------------- /Input/That One Song That Everybody Plays On Piano.mp3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BambooOnFire/Piano-AI-Transcription/HEAD/Input/That One Song That Everybody Plays On Piano.mp3 -------------------------------------------------------------------------------- /Output/That One Song That Everybody Plays On Piano.mid: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BambooOnFire/Piano-AI-Transcription/HEAD/Output/That One Song That Everybody Plays On Piano.mid -------------------------------------------------------------------------------- /piano_transcription_inference_data/note_F1=0.9677_pedal_F1=0.9186.pth: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c3fa9730725bf4a762f1c14bc80cd5986eacda01b026f5a4a2525cd607876141 3 | size 171966578 4 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | h5py==2.10.0 2 | pandas==1.1.2 3 | librosa==0.6.0 4 | numba==0.48 5 | mido==1.2.9 6 | mir_eval==0.5 7 | matplotlib==3.0.3 8 | torchlibrosa==0.0.4 9 | sox==1.4.0 10 | torch==1.4.0 11 | torchvision==0.5.0 12 | pydub 13 | piano_transcription_inference 14 | ffmpeg-python 15 | wget 16 | youtube_dl 17 | certifi -------------------------------------------------------------------------------- /piano_transcription_inference_data/TMI.md: -------------------------------------------------------------------------------- 1 | ByteDance's audio dataset 2 | 3 | Usually, when you first import the transcription module this file will be automatically downloaded to a specific folder via terminal's download mechanism. This is incredibly slow. So, my python wrapper will bypass that step by simply moving this inference file to it designated location. It saves more than a minute based on my experience. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Piano-AI-Transcription 2 | 3 | This is a simple Python "wrapper" that utilizes ByteDance's amazing polyphonic transcription tool. The model is not widely-known but it definitely is a powerful tool. In my opinion, no other models beat this. It supports 1) Correct timing, 2) Correct MIDI velocity, 3) Partially correct sustain points. 4 | 5 | It also supports direct YouTube URL input so you don't have to use shady sites to download audio. 6 | 7 | # Source Model 8 | 9 | ByteDance: https://github.com/bytedance/piano_transcription 10 | 11 | # Usage 12 | 13 | 1. Download this repository. 14 | 2. Install python3.7 from https://www.python.org/downloads/ 15 | 3. Drag any audio file to the **Input** folder. 16 | 4. Or you can follow the instructions in command line and directly render Youtube URLs. (Pretty handy right?) 17 | 5. Open terminal and type **python3.7** then press **SPACE** 18 | 6. Drag **RUN.py** and press **ENTER** 19 | 7. MIDI will be exported to the **Output** folder. 20 | 8. Currently, the python script will automatically remove the audio files in Input to save storage. 21 | 22 | # Examples 23 | 24 | The Input and Output folder in this repo already contains an example render of Charles Cornell's sexy "Heart and Soul" jazz piano performance. 25 | 26 | The output file is in MIDI format and you can use any DAW and VST to render the audio yourself. 27 | -------------------------------------------------------------------------------- /RUN.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3.7 2 | from __future__ import unicode_literals 3 | import re 4 | import subprocess 5 | import sys 6 | import sndhdr 7 | import os 8 | 9 | def install(MODULE): 10 | subprocess.check_call([sys.executable, "-m", "pip", "install", MODULE]) 11 | 12 | def req_install(PATH): 13 | subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", PATH]) 14 | 15 | def pip_install(): 16 | subprocess.check_call([sys.executable, "-m", "ensurepip", "--upgrade"]) 17 | 18 | pip = subprocess.check_output([sys.executable, '-m', 'pip', '--version']).decode('UTF-8') 19 | 20 | matches = ['pip', 'from', 'python'] 21 | 22 | if 'inux' in sys.platform: 23 | os.system('echo export PYTHONPATH="$PYTHONPATH:~/lib/python2.7/site-packages/" >> ~/.bash_profile') 24 | os.system('source ~/.bash_profile') 25 | os.system('echo export PYTHONPATH="$PYTHONPATH:~/lib/python3.7/site-packages/" >> ~/.bash_profile') 26 | os.system('source ~/.bash_profile') 27 | 28 | if not all(x in pip for x in matches): 29 | pip_install() 30 | 31 | import shutil 32 | from pathlib import Path 33 | d = os.path.dirname(os.path.realpath(__file__)) 34 | Input = os.path.join(d, "Input") 35 | Output = os.path.join(d, "Output") 36 | home = os.path.expanduser('~') 37 | 38 | if not os.path.isdir(os.path.join(home, "piano_transcription_inference_data")): 39 | #os.mkdir(home + "/piano_transcription_inference_data") 40 | # '{}/piano_transcription_inference_data/note_F1=0.9677_pedal_F1=0.9186.pth'.format(str(Path.home())) 41 | src = os.path.join(d, "piano_transcription_inference_data") 42 | dest = os.path.join(home, "piano_transcription_inference_data") 43 | shutil.copytree(src, dest) 44 | 45 | requirements = os.path.join(d, "requirements.txt") 46 | # req_install(requirements) 47 | 48 | try: 49 | import youtube_dl 50 | from piano_transcription_inference import PianoTranscription, sample_rate, load_audio 51 | from numpy.core.numeric import full 52 | import ffmpeg 53 | import torch 54 | from pydub import AudioSegment 55 | print("All modules are already installed. Good!") 56 | except: 57 | req_install(requirements) 58 | import youtube_dl 59 | from piano_transcription_inference import PianoTranscription, sample_rate, load_audio 60 | from numpy.core.numeric import full 61 | import ffmpeg 62 | import torch 63 | from pydub import AudioSegment 64 | print("All modules are ready to go!") 65 | 66 | class MyLogger(object): 67 | def debug(self, msg): 68 | pass 69 | 70 | def warning(self, msg): 71 | pass 72 | 73 | def error(self, msg): 74 | print(msg) 75 | 76 | 77 | def my_hook(d): 78 | if d['status'] == 'finished': 79 | print('Done downloading, now converting ...') 80 | 81 | ydl_opts = { 82 | 'format': 'bestaudio/best', 83 | 'writethumbnail' : True, 84 | 'addmetadata' : True, 85 | 'postprocessors': [{ 86 | 'key': 'FFmpegExtractAudio', 87 | 'preferredcodec': 'mp3', 88 | 'preferredquality': '192', 89 | }, 90 | {'key' : 'EmbedThumbnail'}, 91 | {'key': 'FFmpegMetadata'} 92 | ], 93 | 'outtmpl': os.path.join(Input, '%(title)s - %(channel)s.%(ext)s'), 94 | 'logger': MyLogger(), 95 | 'progress_hooks': [my_hook] 96 | } 97 | 98 | yes = ['y', 'Y', 'yes', 'Yes', 'YES'] 99 | 100 | youtube = input("Do you want to also render with YouTube URLs?: ") 101 | if any(x in youtube for x in yes): 102 | Links = input("Enter youtube URLs, separated with a comma and a space, that you want to download and render: ") 103 | Links = Links.split(", ") 104 | with youtube_dl.YoutubeDL(ydl_opts) as ydl: 105 | for x in Links: 106 | try: 107 | ydl.download([x]) # If Certificate Error pops up on OSX, https://stackoverflow.com/questions/42098126/mac-osx-python-ssl-sslerror-ssl-certificate-verify-failed-certificate-verify 108 | except: 109 | pass 110 | 111 | for path in os.listdir(Input): 112 | full_path = os.path.join(Input,path) 113 | if not path.startswith("."): # IGNORE .DS_STORE 114 | print("\n" + 'RENDERING: ' + str(path) + "\n") 115 | 116 | # Convert to mp3 audio type 117 | if not full_path.endswith('.mp3'): 118 | audio_path = Path(full_path) 119 | raw_audio = AudioSegment.from_file(audio_path) 120 | export_path = full_path[:-4] + "_CONVERTED.mp3" 121 | try: 122 | raw_audio.export(export_path, format="mp3") 123 | os.remove(full_path) 124 | print("CONVERSION Successful") 125 | full_path = export_path 126 | except: 127 | print("CONVERSION Error\n") 128 | nonaudio_name = os.path.splitext(path) 129 | nonaudio_name = nonaudio_name[0] 130 | print("[{File}] is most likely NOT an AUDIO file!".format(File=path)) 131 | remove = input("Do you wish to remove the non-audio file?: ") 132 | if any(x in remove for x in yes): 133 | os.remove(full_path) 134 | 135 | # Load audio 136 | (audio, _) = load_audio(full_path, sr=sample_rate, mono=True) 137 | 138 | # Transcriptor 139 | if torch.cuda.is_available(): 140 | transcriptor = PianoTranscription(device='cuda') # 'cuda' | 'cpu' 141 | print("\n- - - - CUDA Transcriptor - - - -") 142 | else: 143 | transcriptor = PianoTranscription(device='cpu') # 'cuda' | 'cpu' 144 | print("\n- - - - CPU Transcriptor - - - -") 145 | 146 | # Transcribe and write out to MIDI file 147 | true_name = os.path.splitext(path) 148 | true_name = true_name[0] 149 | Output_name = os.path.join(str(Output), str(true_name)) + ".mid" 150 | try: 151 | transcribed_dict = transcriptor.transcribe(audio, Output_name) 152 | 153 | # Remove CONVERTED MP3 audio files for this instance 154 | try: 155 | os.remove(full_path) 156 | print("[{NAME}] audio file removed successfully!".format(NAME=true_name)) 157 | except: 158 | print("[{NAME}] file was already removed.".format(NAME=true_name)) 159 | except: 160 | print("[{NAME}] file FAILED!".format(NAME=true_name)) 161 | pass 162 | 163 | 164 | 165 | ## PATH = /Library/Frameworks/Python.framework/Versions/3.7/bin 166 | ## export PATH="/Library/Frameworks/Python.framework/Versions/3.7/bin" 167 | ## source ~/.bash_profile 168 | 169 | ## SOURCE FILE AND DATASET: https://github.com/bytedance/piano_transcription -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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