fresh start

.ipynb_checkpoints/app-checkpoint.py

@@ -1,56 +1,62 @@
 import gradio as gr
 
-# from musicautobot.numpy_encode import file2stream
 from musicautobot.utils.setup_musescore import play_wav
 from music21.midi.translate import midiFileToStream
 from pathlib import Path
 from midi2audio import FluidSynth
 
+from musicautobot.numpy_encode import *
+from musicautobot.config import *
+from musicautobot.music_transformer import *
+from musicautobot.utils.midifile import *
+from musicautobot.utils.file_processing import process_all
+
+import pickle
+
 import subprocess
 import os
 
 print(os.getcwd())
-# print(os.listdir())
-# print(os.listdir('..'))  # ['.bash_logout', '.bashrc', '.profile', 'app', '.cache', '.local']
-# print(os.listdir('../.local'))  # ['bin', 'lib', 'etc', 'share']
-# print(os.listdir('../..'))  # ['user']
-# print(os.listdir('../../..'))  # ['bin', 'boot', 'dev', 'etc', 'home', 'lib', 'lib64', 'media', 'mnt', 'opt', 'proc', 'root', 'run', 'sbin', 'srv', 'sys', 'tmp', 'usr', 'var', '.dockerenv']
-# print(os.listdir('../../../bin'))
-
-# result = subprocess.run(['echo $PATH'], capture_output=True, shell=True)
-# stdout = result.stdout
-# paths = stdout.decode('utf-8').strip().split(':')
 
-# from pathlib import Path
+# Load the stored data. This is needed to generate the vocab.
+data_dir = Path('data/')
+data = load_data(data_dir, 'data.pkl')
 
-# paths = [Path(path) for path in paths]
-# for path in paths:
-#     print(path)
-#     print(os.listdir(path))
-#     print('-'*10)
+# Default config options
+config = default_config()
+config['encode_position'] = True
 
-# subprocess.run(['/home/user/.local/bin/install_fluidsynth_with_soundfonts_osx.sh'], shell=True)
-# subprocess.run(['cat /home/user/.local/bin/install_fluidsynth_with_soundfonts_osx.sh'], shell=True)
-
-# subprocess.run(['cat /root/packages.txt'], shell=True)
+# Load our fine-tuned model
+learner = music_model_learner(
+    data, 
+    config=config.copy(),
+    pretrained_path='model.pth'
+)
 
 
-# subprocess.run(['apt-get install fluidsynth'], shell=True)  # do not have the right perms
-# results = subprocess.run(['find / | grep "fluidsynth"'], capture_output=True, shell=True)
-results = subprocess.run(['find / | grep "FluidR"'], capture_output=True, shell=True)  # look for sound fonts
-print(results.stdout)
 
-# subprocess.run(['PATH=$PATH:~/opt/bin'])
-# subprocess.run(['PATH=$PATH:~/opt/bin/fluidsynth'])
-# subprocess.run(['fluidsynth'], shell=True)
- 
 def process_midi(midi_file):
     name = Path(midi_file.name)
-#     stream = midiFileToStream(midi_file)
-#     output_wav = play_wav(stream)
-#     FluidSynth("../usr/share/sounds/sf2/FluidR3_GM.sf2").midi_to_audio(name, 'test.wav')
-    FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2").midi_to_audio(name, 'test.wav')
-    return 'test.wav'
+    
+    # create the model input object
+    item = MusicItem.from_file(name, data.vocab);
+
+    # full is the prediction appended to the input
+    pred, full = learn.predict(item, n_words=100)
+   
+    # convert to stream and then MIDI file
+    stream = full.to_stream()
+    out = music21.midi.translate.streamToMidiFile(stream)
+    
+    # save MIDI file
+    out.open('result.midi', 'wb')
+    out.write()
+    out.close()
+    
+    # use fluidsynth to convert MIDI to WAV so the user can hear the output
+    sound_font = "/usr/share/sounds/sf2/FluidR3_GM.sf2"
+    FluidSynth(sound_font).midi_to_audio('result.midi', 'result.wav')
+    return 'result.wav'
 
 iface = gr.Interface(
     fn=process_midi, 

app.py

@@ -1,56 +1,62 @@
 import gradio as gr
 
-# from musicautobot.numpy_encode import file2stream
 from musicautobot.utils.setup_musescore import play_wav
 from music21.midi.translate import midiFileToStream
 from pathlib import Path
 from midi2audio import FluidSynth
 
+from musicautobot.numpy_encode import *
+from musicautobot.config import *
+from musicautobot.music_transformer import *
+from musicautobot.utils.midifile import *
+from musicautobot.utils.file_processing import process_all
+
+import pickle
+
 import subprocess
 import os
 
 print(os.getcwd())
-# print(os.listdir())
-# print(os.listdir('..'))  # ['.bash_logout', '.bashrc', '.profile', 'app', '.cache', '.local']
-# print(os.listdir('../.local'))  # ['bin', 'lib', 'etc', 'share']
-# print(os.listdir('../..'))  # ['user']
-# print(os.listdir('../../..'))  # ['bin', 'boot', 'dev', 'etc', 'home', 'lib', 'lib64', 'media', 'mnt', 'opt', 'proc', 'root', 'run', 'sbin', 'srv', 'sys', 'tmp', 'usr', 'var', '.dockerenv']
-# print(os.listdir('../../../bin'))
-
-# result = subprocess.run(['echo $PATH'], capture_output=True, shell=True)
-# stdout = result.stdout
-# paths = stdout.decode('utf-8').strip().split(':')
 
-# from pathlib import Path
+# Load the stored data. This is needed to generate the vocab.
+data_dir = Path('data/')
+data = load_data(data_dir, 'data.pkl')
 
-# paths = [Path(path) for path in paths]
-# for path in paths:
-#     print(path)
-#     print(os.listdir(path))
-#     print('-'*10)
+# Default config options
+config = default_config()
+config['encode_position'] = True
 
-# subprocess.run(['/home/user/.local/bin/install_fluidsynth_with_soundfonts_osx.sh'], shell=True)
-# subprocess.run(['cat /home/user/.local/bin/install_fluidsynth_with_soundfonts_osx.sh'], shell=True)
-
-# subprocess.run(['cat /root/packages.txt'], shell=True)
+# Load our fine-tuned model
+learner = music_model_learner(
+    data, 
+    config=config.copy(),
+    pretrained_path='model.pth'
+)
 
 
-# subprocess.run(['apt-get install fluidsynth'], shell=True)  # do not have the right perms
-# results = subprocess.run(['find / | grep "fluidsynth"'], capture_output=True, shell=True)
-results = subprocess.run(['find / | grep "FluidR"'], capture_output=True, shell=True)  # look for sound fonts
-print(results.stdout)
 
-# subprocess.run(['PATH=$PATH:~/opt/bin'])
-# subprocess.run(['PATH=$PATH:~/opt/bin/fluidsynth'])
-# subprocess.run(['fluidsynth'], shell=True)
- 
 def process_midi(midi_file):
     name = Path(midi_file.name)
-#     stream = midiFileToStream(midi_file)
-#     output_wav = play_wav(stream)
-#     FluidSynth("../usr/share/sounds/sf2/FluidR3_GM.sf2").midi_to_audio(name, 'test.wav')
-    FluidSynth("/usr/share/sounds/sf2/FluidR3_GM.sf2").midi_to_audio(name, 'test.wav')
-    return 'test.wav'
+    
+    # create the model input object
+    item = MusicItem.from_file(name, data.vocab);
+
+    # full is the prediction appended to the input
+    pred, full = learn.predict(item, n_words=100)
+   
+    # convert to stream and then MIDI file
+    stream = full.to_stream()
+    out = music21.midi.translate.streamToMidiFile(stream)
+    
+    # save MIDI file
+    out.open('result.midi', 'wb')
+    out.write()
+    out.close()
+    
+    # use fluidsynth to convert MIDI to WAV so the user can hear the output
+    sound_font = "/usr/share/sounds/sf2/FluidR3_GM.sf2"
+    FluidSynth(sound_font).midi_to_audio('result.midi', 'result.wav')
+    return 'result.wav'
 
 iface = gr.Interface(
     fn=process_midi,