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import torch
import piano_transcription_inference
import numpy as np
import os
import sys
sys.path.append('../../')
from src.music.utils import get_out_path, load_audio
from src.music.config import CHKPT_PATH_TRANSCRIPTION, FPS, MIN_LEN, CROP_LEN
# import librosa
device = 'cuda' if torch.cuda.is_available() else 'cpu'
TRANSCRIPTOR = piano_transcription_inference.PianoTranscription(device=device,
checkpoint_path=CHKPT_PATH_TRANSCRIPTION)
def audio2midi(audio_path, midi_path=None, crop=CROP_LEN, random_crop=True, verbose=False, level=0):
if verbose and crop < MIN_LEN + 2:
print('crop is inferior to the minimal length of a tune')
assert '.mp3' == audio_path[-4:]
if midi_path is None:
midi_path, _, _ = get_out_path(in_path=audio_path, in_word='audio', out_word='midi', out_extension='.mid')
if verbose: print(' ' * level + f'Transcribing {audio_path}.')
if os.path.exists(midi_path):
if verbose: print(' ' * (level + 2) + 'Midi file already exists.')
return midi_path, ''
error_msg = 'Error in transcription. '
try:
error_msg += 'Maybe in audio loading?'
(audio, _) = load_audio(audio_path,
sr=FPS,
mono=True)
error_msg += ' Nope. Cropping?'
if isinstance(crop, int) and len(audio) > FPS * crop:
rc_str = ' (random crop)' if random_crop else ' (start crop)'
if verbose: print(' ' * (level + 2) + f'Cropping the song to {crop}s before transcription{rc_str}. ')
size_crop = FPS * crop
if random_crop:
index_begining = np.random.randint(len(audio) - size_crop - 1)
else:
index_begining = 0
audio = audio[index_begining: index_begining + size_crop]
error_msg += ' Nope. Transcription?'
TRANSCRIPTOR.transcribe(audio, midi_path)
error_msg += ' Nope.'
extra = f' Saved to {midi_path}' if midi_path else ''
if verbose: print(' ' * (level + 2) + f'Success! {extra}')
return midi_path, ''
except:
if verbose: print(' ' * (level + 2) + 'Transcription failed.')
if os.path.exists(midi_path):
os.remove(midi_path)
return None, error_msg + ' Yes.'
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