Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,603 Bytes
dcca7d2 afa00bd dcca7d2 afa00bd dcca7d2 afa00bd dcca7d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import os
import numpy as np
import time
import librosa
from pathlib import Path
import torch
from utilities import (create_folder, get_filename, RegressionPostProcessor,
write_events_to_midi)
from models import Regress_onset_offset_frame_velocity_CRNN, Note_pedal
from pytorch_utils import move_data_to_device, forward
import config
class PianoTranscription(object):
def __init__(self, model_type='Note_pedal', checkpoint_path=None,
segment_samples=16000*10, device=torch.device('cuda')):
"""Class for transcribing piano solo recording.
Args:
model_type: str
checkpoint_path: str
segment_samples: int
device: 'cuda' | 'cpu'
"""
checkpoint_path="CRNN_note_F1%3D0.9677_pedal_F1%3D0.9186.pth"
print('Checkpoint path: {}'.format(checkpoint_path))
print('Using {} for inference.'.format(device))
self.segment_samples = segment_samples
self.frames_per_second = config.frames_per_second
self.classes_num = config.classes_num
self.onset_threshold = 0.3
self.offset_threshod = 0.3
self.frame_threshold = 0.1
self.pedal_offset_threshold = 0.2
# Build model
Model = eval(model_type)
self.model = Model(frames_per_second=self.frames_per_second,
classes_num=self.classes_num)
# Load model
checkpoint = torch.load(checkpoint_path, map_location=device)
self.model.load_state_dict(checkpoint['model'], strict=False)
# Parallel
if 'cuda' in str(device):
self.model.to(device)
print('GPU number: {}'.format(torch.cuda.device_count()))
self.model = torch.nn.DataParallel(self.model)
else:
print('Using CPU.')
def transcribe(self, audio, midi_path):
"""Transcribe an audio recording.
Args:
audio: (audio_samples,)
midi_path: str, path to write out the transcribed MIDI.
Returns:
transcribed_dict, dict: {'output_dict':, ..., 'est_note_events': ...}
"""
audio = audio[None, :] # (1, audio_samples)
# Pad audio to be evenly divided by segment_samples
audio_len = audio.shape[1]
pad_len = int(np.ceil(audio_len / self.segment_samples))\
* self.segment_samples - audio_len
audio = np.concatenate((audio, np.zeros((1, pad_len))), axis=1)
# Enframe to segments
segments = self.enframe(audio, self.segment_samples)
"""(N, segment_samples)"""
# Forward
output_dict = forward(self.model, segments, batch_size=1)
"""{'reg_onset_output': (N, segment_frames, classes_num), ...}"""
# Deframe to original length
for key in output_dict.keys():
output_dict[key] = self.deframe(output_dict[key])[0 : audio_len]
"""output_dict: {
'reg_onset_output': (N, segment_frames, classes_num),
'reg_offset_output': (N, segment_frames, classes_num),
'frame_output': (N, segment_frames, classes_num),
'velocity_output': (N, segment_frames, classes_num)}"""
# Post processor
post_processor = RegressionPostProcessor(self.frames_per_second,
classes_num=self.classes_num, onset_threshold=self.onset_threshold,
offset_threshold=self.offset_threshod,
frame_threshold=self.frame_threshold,
pedal_offset_threshold=self.pedal_offset_threshold)
# Post process output_dict to MIDI events
(est_note_events, est_pedal_events) = \
post_processor.output_dict_to_midi_events(output_dict)
# Write MIDI events to file
if midi_path:
write_events_to_midi(start_time=0, note_events=est_note_events,
pedal_events=est_pedal_events, midi_path=midi_path)
print('Write out to {}'.format(midi_path))
transcribed_dict = {
'output_dict': output_dict,
'est_note_events': est_note_events,
'est_pedal_events': est_pedal_events}
return transcribed_dict
def enframe(self, x, segment_samples):
"""Enframe long sequence to short segments.
Args:
x: (1, audio_samples)
segment_samples: int
Returns:
batch: (N, segment_samples)
"""
assert x.shape[1] % segment_samples == 0
batch = []
pointer = 0
while pointer + segment_samples <= x.shape[1]:
batch.append(x[:, pointer : pointer + segment_samples])
pointer += segment_samples // 2
batch = np.concatenate(batch, axis=0)
return batch
def deframe(self, x):
"""Deframe predicted segments to original sequence.
Args:
x: (N, segment_frames, classes_num)
Returns:
y: (audio_frames, classes_num)
"""
if x.shape[0] == 1:
return x[0]
else:
x = x[:, 0 : -1, :]
"""Remove an extra frame in the end of each segment caused by the
'center=True' argument when calculating spectrogram."""
(N, segment_samples, classes_num) = x.shape
assert segment_samples % 4 == 0
y = []
y.append(x[0, 0 : int(segment_samples * 0.75)])
for i in range(1, N - 1):
y.append(x[i, int(segment_samples * 0.25) : int(segment_samples * 0.75)])
y.append(x[-1, int(segment_samples * 0.25) :])
y = np.concatenate(y, axis=0)
return y |