def __test(y, top_db, ref, trim_duration):
yt, idx = librosa.effects.trim(y, top_db=top_db,
ref=ref)
# Test for index position
fidx = [slice(None)] * y.ndim
fidx[-1] = slice(*idx.tolist())
assert np.allclose(yt, y[tuple(fidx)])
# Verify logamp
rms = librosa.feature.rmse(y=librosa.to_mono(yt), center=False)
logamp = librosa.power_to_db(rms**2, ref=ref, top_db=None)
assert np.all(logamp > - top_db)
# Verify logamp
rms_all = librosa.feature.rmse(y=librosa.to_mono(y)).squeeze()
logamp_all = librosa.power_to_db(rms_all**2, ref=ref,
top_db=None)
start = int(librosa.samples_to_frames(idx[0]))
stop = int(librosa.samples_to_frames(idx[1]))
assert np.all(logamp_all[:start] <= - top_db)
assert np.all(logamp_all[stop:] <= - top_db)
# Verify duration
duration = librosa.get_duration(yt)
assert np.allclose(duration, trim_duration, atol=1e-1), duration
def __test(y, top_db, ref, trim_duration):
yt, idx = librosa.effects.trim(y, top_db=top_db,
ref=ref)
# Test for index position
fidx = [slice(None)] * y.ndim
fidx[-1] = slice(*idx.tolist())
assert np.allclose(yt, y[tuple(fidx)])
# Verify logamp
rms = librosa.feature.rms(y=librosa.to_mono(yt), center=False)
logamp = librosa.power_to_db(rms**2, ref=ref, top_db=None)
assert np.all(logamp > - top_db)
# Verify logamp
rms_all = librosa.feature.rms(y=librosa.to_mono(y)).squeeze()
logamp_all = librosa.power_to_db(rms_all**2, ref=ref,
top_db=None)
start = int(librosa.samples_to_frames(idx[0]))
stop = int(librosa.samples_to_frames(idx[1]))
assert np.all(logamp_all[:start] <= - top_db)
assert np.all(logamp_all[stop:] <= - top_db)
# Verify duration
duration = librosa.get_duration(yt)
assert np.allclose(duration, trim_duration, atol=1e-1), duration
def _slice_audio_by_interval(y: np.ndarray,
sr: float,
hop_length: int = 512,
segmentation_interval_s: float = 1.0,
**_kwargs) -> Tuple[np.ndarray, np.ndarray]:
interval_samples: int = librosa.time_to_samples(
segmentation_interval_s, sr=sr)
total_samples: int = y.size # y is monophonic
num_segments: int = np.ceil(total_samples / interval_samples)
onset_samples: np.ndarray = interval_samples * np.arange(num_segments)
onset_frames: np.ndarray = librosa.samples_to_frames(
onset_samples, hop_length=hop_length)
duration_samples: np.ndarray = interval_samples * np.ones_like(
onset_frames)
# adjust duration of last fragment to end of file
remainder = total_samples % interval_samples
if remainder == 0:
# `total_samples` is divisible by `interval_samples`: ceil operation above was not needed
pass
else:
# `total_samples` is not divisible by `interval_samples`: last slice is shorter
duration_samples[-1] = remainder
duration_frames: np.ndarray = librosa.samples_to_frames(
duration_samples, hop_length=hop_length)
return onset_frames, duration_frames
def gen_hihat(all_data, fs, fps, cand):
fps = librosa.samples_to_frames(fs, hop_length=hop_len, n_fft=win_len)
fps = 100
print(cand)
proc = BeatTrackingProcessor(look_aside=0.2, fps=fps)
act = RNNBeatProcessor()(all_data)
beat_times = proc(act)
song_len = librosa.samples_to_time(data.shape, sr=fs)[0]
hihat = np.zeros(all_data.shape)
idx = np.where(beat_times <= song_len)[0]
new_beat_times = np.zeros(idx.shape)
new_beat_times[idx] = beat_times[idx]
beat_samples = librosa.time_to_samples(new_beat_times, sr=fs)
start = librosa.frames_to_samples(cand[0], hop_len, n_fft=win_len)
end = librosa.frames_to_samples(cand[-1], hop_len, n_fft=win_len)
cand_len = end - start
i = 3
is_hihat = np.zeros(beat_samples.shape)
while i < len(beat_samples):
is_hihat[i] = 1
i = i + 4
for i, s in enumerate(beat_samples):
if is_hihat[i] == 1:
if s + cand_len > hihat.shape:
break
hihat[s:s + cand_len] = data[start:end]
return hihat, new_beat_times, beat_samples
def get_frame(self) -> int:
if Beat.INDEX_VALUE == 'samples':
return librosa.samples_to_frames(self.index, hop_length=util.HOP_LENGTH)
elif Beat.INDEX_VALUE == 'time':
return librosa.time_to_frames(self.index, sr=util.SAMPLE_RATE, hop_length=util.HOP_LENGTH)
else:
raise NotImplementedError("Only samples and time are supported")
def trackBeatsPer16thNote(x,
bpm,
sr=22050,
hop_length=512,
offset_16th_notes=0):
"""
clickで書き出すと16分音符毎にクリック音が鳴らせるようにビートトラッキングする
16分音符毎にインデックスが割り当てられている
offset_16th_notes（最初の16分音符の数）でアウフタクトの除去が可能
"""
tempo, beat_samples = librosa.beat.beat_track(x,
sr=sr,
hop_length=hop_length,
start_bpm=bpm,
units='samples')
beat_frames_per_16th_note = []
for i in range(len(beat_samples) - 1):
interval_per_16th_units = librosa.samples_to_frames(
np.linspace(beat_samples[i], beat_samples[i + 1], 5),
hop_length=hop_length)
print(interval_per_16th_units[0:4])
beat_frames_per_16th_note = np.hstack(
(beat_frames_per_16th_note, interval_per_16th_units[0:4]))
if offset_16th_notes > 0:
beat_frames_per_16th_note = beat_frames_per_16th_note[
offset_16th_notes:]
return beat_frames_per_16th_note.astype(np.int)
def apply_trim_offset(self, frame):
return (
librosa.samples_to_frames(
librosa.frames_to_samples(frame) + self.trim_offset
)
if self.trim_offset
else frame
)
def __test(x, y, hop_length, n_fft):
y_test = librosa.samples_to_frames(x,
hop_length=hop_length,
n_fft=n_fft)
assert np.allclose(y, y_test)
y = np.asanyarray(y)
assert y.shape == y_test.shape
assert y.ndim == y_test.ndim
def __test(x, y, hop_length, n_fft):
y_test = librosa.samples_to_frames(x,
hop_length=hop_length,
n_fft=n_fft)
assert np.allclose(y, y_test)
y = np.asanyarray(y)
assert y.shape == y_test.shape
assert y.ndim == y_test.ndim
def convert_sample_to_nframes(y_sample_start, y_sample_end, **stft_args):
n_fft = 2048
if 'n_fft' in stft_args:
n_fft = stft_args['n_fft']
hop_length = n_fft//4
if 'hop_length' in stft_args:
hop_length = stft_args['hop_length']
return lr.samples_to_frames(np.array([y_sample_start, y_sample_end]),
hop_length=hop_length, n_fft=n_fft)
def get_downbeats(y, tempo, beat_frames, sr):
measures = len(beat_frames) // BEATS
beat_frames = librosa.samples_to_frames(beat_frames)
onset_env = librosa.onset.onset_strength(y, sr=sr, aggregate=np.median)
beat_strengths = onset_env[beat_frames]
measure_beat_strengths = beat_strengths[:measures * BEATS].reshape(
-1, BEATS)
beat_pos_strength = np.sum(measure_beat_strengths, axis=0)
downbeat_pos = np.argmax(beat_pos_strength)
full_measure_beats = beat_frames[:measures * BEATS].reshape(-1, BEATS)
downbeat_frames = full_measure_beats[:, downbeat_pos]
return librosa.frames_to_samples(downbeat_frames)
def test_samples_to_frames(samples, hop_length, n_fft):
frames = librosa.samples_to_frames(samples,
hop_length=hop_length,
n_fft=n_fft)
samples = np.asanyarray(samples)
assert frames.shape == samples.shape
assert frames.ndim == samples.ndim
if n_fft is None:
assert np.allclose(samples, frames * hop_length)
else:
assert np.allclose((samples - n_fft // 2) // hop_length, frames)
def read_data():
os.chdir(DATA_PATH)
os.chdir('Gitarre monophon/Samples/Distortion')
train_data = []
train_labels = []
for file_name in os.listdir(os.getcwd()):
if file_name.endswith(".wav"):
print(file_name)
# Labeling the sample with one hot encoding
label_no = int(file_name[13]) # Effect setting is the label
label = np.zeros([3])
label[label_no - 1] = 1
train_labels.append(label)
# Loading the audio
y, sr = librosa.load(file_name, sr=44100)
# Onset Detection
y = np.insert(y, 0, np.zeros(1023))
y = librosa.util.normalize(y)
onset_frame = librosa.onset.onset_detect(y=y,
sr=sr,
units='frames',
pre_max=20000,
post_max=20000,
pre_avg=20000,
post_avg=20000,
delta=0,
wait=1000)
offset_frame = librosa.samples_to_frames(samples=y.shape[0])
onset_sample = librosa.core.frames_to_samples(onset_frame[0])
offset_sample = librosa.core.frames_to_samples(offset_frame)
y_cut = y[onset_sample:offset_sample]
mfcc = librosa.feature.mfcc(y=y_cut, sr=sr, n_mfcc=2)
mfcc_delta = librosa.feature.delta(mfcc)
m_features = np.concatenate((mfcc, mfcc_delta))
v_features = []
for feat in m_features:
lin_coeff, lin_residual, _, _, _ = np.polyfit(np.arange(
len(feat)),
feat,
1,
full=True)
v_features.extend(lin_coeff)
# v_features.append(lin_residual)
train_data.append(np.hstack(v_features))
train_data = np.array(train_data)
train_labels = np.array(train_labels)
return train_data, train_labels
def forward(self, signals, lengths):
mel_features = self.mfcc(signals)
if self.remove_zeroth_coef:
mel_features = mel_features[:, 1:, :]
device = lengths.device
lengths_frames = librosa.samples_to_frames(lengths.cpu().numpy(),
hop_length=self.hop_length,
n_fft=self.n_fft)
lengths_frames = torch.Tensor(lengths_frames).to(device).int()
if self.use_deltas:
delta = self.deltas(mel_features)
delta2 = self.deltas(delta)
mel_features = torch.cat((mel_features, delta, delta2), dim=-2)
if self.normalize_features:
mel_features = self.norm(mel_features)
return mel_features, lengths_frames
def _compute_slice_durations(y: np.ndarray,
sr: float,
hop_length: float,
onsets: np.ndarray,
min_size_s: Optional[float] = None,
max_size_s: Optional[float] = None,
off_threshold_db: Optional[float] = None,
discard_by_mean: bool = True,
**_kwargs) -> Tuple[np.ndarray, np.ndarray]:
""" y: mono signal [shape: (n,)]
onsets: onset frames """
rms_frames_db = 20 * np.log10(
np.abs(librosa.feature.rms(y=y, hop_length=hop_length)) +
librosa.util.tiny(y)).reshape(-1)
eof = librosa.samples_to_frames(y.size, hop_length=hop_length)
durations = np.diff(np.block([onsets, eof]))
if max_size_s is not None:
max_size_frames = librosa.time_to_frames(max_size_s,
sr=sr,
hop_length=hop_length)
durations[durations > max_size_frames] = max_size_frames
if off_threshold_db is not None:
for i in range(onsets.size):
segment_rms = rms_frames_db[onsets[i]:onsets[i] + durations[i]]
first_silent_frame = np.argmax(segment_rms < off_threshold_db)
# Only discard part of segment if mean of entire part to be discarded is below threshold
if discard_by_mean:
if np.mean(segment_rms[first_silent_frame:]
) < off_threshold_db:
durations[i] = first_silent_frame
# Discard part of segment starting from frame below threshold
else:
# `np.argmax(a < v)` will by default return 0 if it doesn't find any matches:
# therefore the check that the condition indeed is fulfilled.
durations[i] = first_silent_frame if segment_rms[first_silent_frame] < off_threshold_db else \
durations[i]
if min_size_s is not None:
valid_frames_mask = durations > min_size_s
onsets = onsets[valid_frames_mask]
durations = durations[valid_frames_mask]
return onsets, durations
def retrieve_components(self, selection_order=None):
if selection_order is None:
return self.spectrogram
if len(selection_order) > 0:
max_val = max(selection_order)
if max_val >= self.get_number_components():
raise ValueError("{} out of bounds for {} components", max_val,
self.get_number_components())
mask = torch.zeros_like(self.spectrogram)
unmask = torch.ones_like(self.spectrogram)
# following the order of segments in [Mishra 2017] Figure 4
temp_length = mask.shape[1] // len(self.temporal_segments)
freq_length = mask.shape[0] // self.n_frequency_segments
left_over = mask.shape[1] - temp_length * len(self.temporal_segments)
if left_over > 0:
warnings.warn(
"Adding last {} frames to last segment".format(left_over))
def compute_f_start(f):
return f * freq_length
def compute_f_end(f):
return compute_f_start(f) + freq_length
for so in selection_order:
t = so // self.n_frequency_segments # index of temporal_segment
# print("t", t)
f = so % self.n_frequency_segments
[t_start,
t_end] = librosa.samples_to_frames(self.temporal_segments[t],
hop_length=self.hop_length)
if t == len(self.temporal_segments) - 1:
t_end = mask.shape[1]
# print("t_start {}, t_end{}".format(t_start, t_end))
f_start = compute_f_start(f)
f_end = compute_f_end(f)
mask[f_start:f_end, t_start:t_end] = 1.
unmask[f_start:f_end, t_start:t_end] = 0.
return self.spectrogram * mask + self.baseline * unmask
def length_convert(length: float,
sr: int,
units_def: LengthUnit,
units_target: LengthUnit,
hop_length: int = 512) -> float:
"""Convert length from one unit to another.
Parameters
----------
length : float
in sec
sr : int
units_def : LengthUnit
Units that are passed
units_target : LengthUnit
Units that are expected
hop_length : int, optional
512 by default, mandatory for frames conversion
Returns
-------
float
"""
if units_def == LengthUnit.samples:
if units_target == LengthUnit.frames:
return lr.samples_to_frames(length, hop_length) # type:ignore
if units_target == LengthUnit.ms:
return lr.samples_to_time(length, sr) # type:ignore
return length
if units_def == LengthUnit.ms:
if units_target == LengthUnit.samples:
return lr.time_to_samples(length, sr) # type:ignore
if units_target == LengthUnit.frames:
return lr.time_to_frames(length, sr, hop_length) # type:ignore
return length
if units_def == LengthUnit.frames:
if units_target == LengthUnit.samples:
return lr.frames_to_samples(length, hop_length) # type:ignore
if units_target == LengthUnit.ms:
return lr.frames_to_time(length, sr, hop_length) # type:ignore
return length
raise TypeError(f'not a LengthUnit: {units_def, units_target}')
def process_damp_data(artist_tracks_file):
sys.path.append('../')
import damp_config
damp_data_dir = damp_config.vocal_audio_dir
musdb_data_dir = damp_config.bg_audio_dir
# musdb_data_dir = 'background_tracks'
if not os.path.exists('damp_mashup_output'):
os.makedirs('damp_mashup_output')
train_dict = pickle.load(open(artist_tracks_file, 'rb'))
vocal_paths = []
for artist_id, track_list in train_dict.items():
for track_id in track_list:
vocal_track_path = os.path.join(damp_data_dir, track_id + '.m4a')
mashability_result = find_mashup_pairs(vocal_track_path,
musdb_data_dir)
for start_sample, (bg_track,
(bg_start_sample, bg_key,
bg_ismajor)) in mashability_result.items():
print(start_sample, bg_track, bg_start_sample)
mixed_output = mash(vocal_track_path, start_sample, bg_track,
bg_start_sample, 3.0)
start_frame = librosa.samples_to_frames(
start_sample,
hop_length=damp_config.hop_length,
n_fft=damp_config.n_fft)
# librosa.output.write_wav(os.path.join(config.mix_audio_dir, Path(vocal_path).stem + '_' + str(start_frame) +'.wav'), mixed_output, sr=AUDIO_PARAMS['sr'])
print(track_id, start_sample / 44100, start_frame)
soundfile.write(os.path.join(
config.mix_audio_dir,
track_id + '_' + str(start_frame) + '.wav'),
mixed_output,
AUDIO_PARAMS['sr'],
format='WAV')
def main_autoedit(args, **kwargs):
"""main_autoedit
Complete autoedit flow
..todo::
- loop over chunks of input, batch is large single chunk
- handle returned chunk data, integrate over time
- chunk parallel processing (entire graph) vs. chunk serial processing (entire graph)
- nodes with memory and nodes without
- graph class
- populate 'func' in graph w/ cached/non-cached funcs
- step file / stream input: deal with chunking and collecting, refuse to work on files > maxlen (configurable)
- openl3
"""
# convert args to dict
# kwargs = args_to_dict(args)
# convert arguments to locals, TODO: config file for autoedit param dict
# sr_comp = kwargs['sr_comp'] # 22050
# numsegs = kwargs['numsegs'] # 10
# duration = kwargs['duration'] # 10
# verbose = kwargs['verbose']
# seglen_min = time_to_frames(kwargs['seglen_min'])
# seglen_max = time_to_frames(kwargs['seglen_max'])
args = autoedit_args_check(args)
seglen_min = time_to_frames(args.seglen_min)
seglen_max = time_to_frames(args.seglen_max)
timebase = "frames"
spacer = '\n '
# caching
# compute_music_extractor_essentia_cached = memory.cache(compute_music_extractor_essentia)
# computation graph g
g = OrderedDict()
# populate graph with functions
g['func'] = {}
for func in [
compute_beats_librosa,
compute_chroma_librosa,
compute_onsets_librosa,
compute_segments_essentia,
compute_segments_librosa,
data_load_essentia,
data_load_librosa,
]:
g['func'][func] = memory.cache(func)
for func in [
compute_event_merge_combined,
track_assemble_from_segments,
track_assemble_from_segments_sequential_scale,
]:
g['func'][func] = func
# layer 1: file data
g['l1_files'] = OrderedDict()
for filename in args.filenames:
# replace with basename
filename_short = filename.split('/')[-1]
if args.verbose:
print(('main_autoedit{1}filename_short: {0}'.format(
filename_short, spacer)))
# files[filename_short] = compute_tempo_beats(filename)
# load data
# y, sr = data_load_essentia_cached(filename)
# compute beatiness on data
g['l1_files'][filename_short] = {}
tmp_ = g['func'][data_load_essentia](filename, sr=args.sr_comp)
# tmp_ = g['func'][data_load_librosa](filename, sr=args.sr_comp)
g['l1_files'][filename_short]['data'] = tmp_[0]
g['l1_files'][filename_short]['numsamples'] = len(tmp_[0])
g['l1_files'][filename_short]['numframes'] = samples_to_frames(
len(tmp_[0]))
g['l1_files'][filename_short]['sr'] = tmp_[1]
if args.verbose:
print(
'main_autoedit{5}loaded {0} with shape {1}, numsamples {2}, numframes {3}, sr {4}'
.format(filename_short,
g['l1_files'][filename_short]['data'].shape,
g['l1_files'][filename_short]['numsamples'],
g['l1_files'][filename_short]['numframes'],
g['l1_files'][filename_short]['sr'], spacer))
# layer 2: compute chromagram
g['l2_chromagram'] = {}
for file_ in g['l1_files']:
# file_key = '{0}-{1}'.format(file_, 'chromagram')
g['l2_chromagram'][file_] = {}
g['l2_chromagram'][file_]['data'] = g['func'][compute_chroma_librosa](
g['l1_files'][file_]['data'], args.sr_comp)['chromagram']
# layer 3: compute segments based on chromagram
g['l3_segments'] = OrderedDict()
for file_ in g['l2_chromagram']:
# file_key = '{0}-{1}'.format(file_, 'segments')
bounds_frames = g['func'][compute_segments_essentia](
g['l2_chromagram'][file_]['data'], args.sr_comp,
args.numsegs)['bounds_frames']
# print((' file_: {0}, bounds_frames {1}, {2}'.format(file_, len(bounds_frames), pformat(bounds_frames))))
g['l3_segments'][file_] = {}
g['l3_segments'][file_]['seg_sbic'] = np.clip(bounds_frames, 0, [
g['l1_files'][filename_short]['numframes']
for filename_short in g['l1_files']
][0] - 1)
bounds_frames = g['func'][compute_segments_librosa](
g['l2_chromagram'][file_]['data'], args.sr_comp,
args.numsegs)['bounds_frames']
# print((' file_: {0}, bounds_frames {1}, {2}'.format(file_, len(bounds_frames), pformat(bounds_frames))))
g['l3_segments'][file_]['seg_clust_1'] = bounds_frames
bounds_frames = g['func'][compute_segments_librosa](
g['l2_chromagram'][file_]['data'], args.sr_comp,
args.numsegs + 5)['bounds_frames']
# print((' file_: {0}, bounds_frames {1}, {2}'.format(file_, len(bounds_frames), pformat(bounds_frames))))
g['l3_segments'][file_]['seg_clust_2'] = bounds_frames
# layer 4: compute onsets
g['l4_onsets'] = OrderedDict()
for file_ in g['l1_files']:
onsets = g['func'][compute_onsets_librosa](
g['l1_files'][file_]['data'], args.sr_comp)
g['l4_onsets'][file_] = onsets
# layer 5: compute beats based on onsets
g['l5_beats'] = OrderedDict()
for file_ in g['l4_onsets']:
g['l5_beats'][file_] = {}
for start_bpm in [60, 90, 120]:
beats = g['func'][compute_beats_librosa](
g['l4_onsets'][file_]['onsets_env'],
g['l4_onsets'][file_]['onsets_frames'], start_bpm,
args.sr_comp)
# print('beats type = {0}'.format(type(beats['beats'])))
# beats['beats'] = beats['beats'][np.logical_not(np.isnan(beats['beats']))]
# beats = beats[~np.isnan(beats)]
# print(' file_: {0}, bounds_frames {1}, {2}'.format(file_, len(bounds_frames), pformat(bounds_frames)))
g['l5_beats'][file_]['beats_{0}'.format(
start_bpm)] = beats['beats']
g['l5_beats'][file_]['beats_{0}_16'.format(
start_bpm)] = beats['beats'][::16]
# layer 6: compute final segments from merging segments with beats
g['l6_merge'] = OrderedDict()
g['l6_merge']['files'] = []
for file_ in g['l1_files']:
# get basedir from filename
dirname = os.path.dirname(filename)
# return realpath absolute path
# dirname = os.path.dirname(os.path.realpath(filename))
if dirname == '':
dirname = '.'
if args.verbose:
print(f'main_autoedit dirname {dirname}')
print(
f'main_autoedit{spacer}l6_merge file_ {file_}, dirname {dirname}, filename {filename}'
)
beats_keys = ['beats_60', 'beats_90', 'beats_120'
] + ['beats_60_16', 'beats_90_16', 'beats_120_16']
# beats = [g['l5_beats'][file_][beat_type] for beat_type in beats_keys for file_ in g['l1_files']]
beats = [g['l5_beats'][file_][beat_type] for beat_type in beats_keys]
# segs = [g['l3_segments'][file_][seg_type_] for seg_type_ in ['seg_sbic', 'seg_clust_1', 'seg_clust_2'] for file_ in g['l1_files']]
segs = [
g['l3_segments'][file_][seg_type_]
for seg_type_ in ['seg_sbic', 'seg_clust_1', 'seg_clust_2']
]
numframes = g['l1_files'][file_]['numframes']
# compute
if args.verbose:
print(
f'main_autoedit{spacer}l6_merge dirname {dirname}, filename {filename}'
)
files = g['func'][compute_event_merge_combined](
filename_48=dirname + '/' + file_,
beats=beats,
segs=segs,
numframes=numframes,
numsegs=args.numsegs,
verbose=args.verbose,
sr_comp=args.sr_comp,
rootdir=args.rootdir,
)
g['l6_merge']['files'].extend(files['files'])
if args.verbose:
print('main_autoedit{2}l6_merge {0}, {1}'.format(
file_, g['l6_merge']['files'], spacer))
# layer 7: compute assembled song from segments and duration
g['l7_assemble'] = OrderedDict()
# compute duration
g['l6_merge']['duration'] = args.duration
# output filename
g['l6_merge']['filename_export'] = args.filename_export
# crossfade argument
g['l6_merge']['assemble_crossfade'] = args.assemble_crossfade
# rootdir argument
g['l6_merge']['rootdir'] = args.rootdir
g['l6_merge']['verbose'] = args.verbose
if args.assemble_mode == 'random':
g['l7_assemble']['outfile'] = g['func'][track_assemble_from_segments](
**(g['l6_merge']))
elif args.assemble_mode == 'sequential':
g['l7_assemble']['outfile'] = g['func'][
track_assemble_from_segments_sequential_scale](**(g['l6_merge']))
filename_export_wav = g['l7_assemble']['outfile']['filename_export_wav']
filename_export_txt = g['l7_assemble']['outfile']['filename_export_txt']
export_duration = g['l7_assemble']['outfile']['final_duration']
export_segs = g['l7_assemble']['outfile']['seg_s']
export_numsegs = len(g['l7_assemble']['outfile']['seg_s'])
if 'pkl' in args.outputs:
filename_export_graph = os.path.join(
args.rootdir, f'{args.filename_export}_graph.pkl')
if args.verbose:
print(
f'main_autoedit{spacer}exporting graph to {filename_export_graph}'
)
joblib.dump(g, filename_export_graph)
# # plot dictionary g as graph
# autoedit_graph_from_dict(g=g, plot=False)
ret = {
'data': {
'output_files': [
{
'format': 'wav',
'filename': os.path.basename(filename_export_wav)
},
{
'format': 'txt',
'filename': os.path.basename(filename_export_txt)
},
],
'output_length':
export_duration,
'output_numsegs':
export_numsegs,
}
}
if 'pkl' in args.outputs:
ret['data']['output_files'].append({
'format': 'pkl',
'filename': filename_export_graph
})
# # yeah nice, should be obsolete
# ret.update(g['l7_assemble']['outfile'])
filename_result = os.path.join(
args.rootdir,
os.path.basename(args.filename_export) + ".json")
# this saves the array in .json format
json.dump(
ret,
codecs.open(filename_result, 'w', encoding='utf-8'),
# separators=(',', ':'),
# sort_keys=True,
# indent=4,
# cls=NumpyEncoder,
)
if 'task' in kwargs:
kwargs['task'].set_done(
result_location=os.path.basename(args.filename_export) + ".json")
return ret
def find_loop_pairs(self):
runtime_start = time.time()
S = librosa.core.stft(y=self.audio)
S_power = np.abs(S) ** 2
S_weighed = librosa.core.perceptual_weighting(
S=S_power, frequencies=librosa.fft_frequencies(sr=self.rate)
)
mel_spectrogram = librosa.feature.melspectrogram(S=S_weighed, sr=self.rate, n_mels=128, fmax=8000)
chroma = librosa.feature.chroma_stft(S=S_power)
power_db = librosa.power_to_db(S_weighed, ref=np.median)
onset_env = librosa.onset.onset_strength(S=mel_spectrogram)
pulse = librosa.beat.plp(onset_envelope=onset_env)
beats_plp = np.flatnonzero(librosa.util.localmax(pulse))
bpm, beats = librosa.beat.beat_track(onset_envelope=onset_env)
beats = np.union1d(beats, beats_plp)
beats = np.sort(beats)
logging.info("Detected {} beats at {:.0f} bpm".format(beats.size, bpm))
min_duration = int(chroma.shape[-1] * self.min_duration_multiplier)
runtime_end = time.time()
prep_time = runtime_end - runtime_start
logging.info("Finished initial audio processing in {:.3}s".format(prep_time))
candidate_pairs = []
deviation = np.linalg.norm(chroma[..., beats] * 0.085, axis=0)
for idx, loop_end in enumerate(beats):
for loop_start in beats:
if loop_end - loop_start < min_duration:
break
dist = np.linalg.norm(chroma[..., loop_end] - chroma[..., loop_start])
if dist <= deviation[idx]:
db_diff = self.db_diff(
power_db[..., loop_end], power_db[..., loop_start]
)
if db_diff <= 1.5:
candidate_pairs.append(
{
"loop_start": loop_start,
"loop_end": loop_end,
"dB_diff": db_diff,
"dist": (dist / deviation[idx])
}
)
logging.info(f"Found {len(candidate_pairs)} possible loop points")
if not candidate_pairs:
return candidate_pairs
beats_per_second = bpm / 60
num_test_beats = 12
seconds_to_test = num_test_beats / beats_per_second
test_offset = librosa.samples_to_frames(int(seconds_to_test * self.rate))
# adjust offset for very short tracks to 25% of its length
if test_offset > chroma.shape[-1]:
test_offset = chroma.shape[-1] // 4
candidate_pairs = self._dB_prune(candidate_pairs)
weights = _geometric_weights(test_offset, start=test_offset // num_test_beats)
pair_score_list = [
self._pair_score(
pair["loop_start"],
pair["loop_end"],
chroma,
test_duration=test_offset,
weights=weights,
)
for pair in candidate_pairs
]
# Add cosine similarity as score
for pair, score in zip(candidate_pairs, pair_score_list):
pair["score"] = score
candidate_pairs = self._score_prune(candidate_pairs)
# re-sort based on new score
candidate_pairs = sorted(candidate_pairs, reverse=True, key=lambda x: x["score"])
# prefer longer loops for highly similar sequences
if len(candidate_pairs) > 1:
self._prioritize_duration(candidate_pairs)
if self.trim_offset:
for pair in candidate_pairs:
pair["loop_start"] = self.apply_trim_offset(
pair["loop_start"]
)
pair["loop_end"] = self.apply_trim_offset(
pair["loop_end"]
)
for pair in candidate_pairs:
logging.info(
"Found from {} to {}, dB_diff:{}, similarity:{}".format(
pair["loop_start"],
pair["loop_end"],
pair["dB_diff"],
pair["score"],
)
)
if not candidate_pairs:
raise LoopNotFoundError(f'No loop points found for {self.filename} with current parameters.')
else:
return candidate_pairs
def __test(x, y, hop_length, n_fft):
y_test = librosa.samples_to_frames(x,
hop_length=hop_length,
n_fft=n_fft)
assert np.allclose(y, y_test)
def main_automix(args):
"""main_automix
Perform complete automix flow with the following schema:
1. input list of audio files / text file containing list of audio files
2. loop over files
2.1. compute bag of measures for each file: beatiness, extractor essentia, features paa
2.2. sort files by selected feature args.sort_feature
2.3. assemble output wav from concatenating input files pydub
2.4. TODO: optional: local measures
2.4. TODO: optional: complexity / information measures smp/sequence
"""
# convert args to dict
kwargs = args_to_dict(args)
print('main_automix: kwargs {0}'.format(pformat(kwargs)))
# flow graph g
g = OrderedDict()
# cached functions
g['func'] = {}
for func in [
compute_beats_librosa,
compute_chroma_librosa,
compute_features_paa,
compute_music_extractor_essentia,
compute_onsets_librosa,
compute_segments_essentia,
compute_segments_librosa,
compute_tempo_beats_essentia,
data_load_essentia,
]:
g['func'][func] = memory.cache(func)
# uncached functions
for func in [
compute_event_merge_combined,
track_assemble_from_segments,
]:
g['func'][func] = func
# input type: text file, list of files
if len(kwargs['filenames']) == 1 and kwargs['filenames'][0].endswith('.txt'):
filenames = [_.rstrip() for _ in open(kwargs['filenames'][0], 'r').readlines()]
# print('filenames {0}'.format(pformat(filenames)))
print('filenames {0}'.format(filenames))
else:
filenames = kwargs['filenames']
# layer 1: file/chunk data
g['l1_files'] = OrderedDict()
for i, filename in enumerate(filenames):
# print('filename {0}: {1}'.format(i, filename))
filename_short = filename.split('/')[-1]
print(('file: {0}'.format(filename_short)))
# load data
# y, sr = g['func'][data_load_essentia](filename)
g['l1_files'][filename_short] = {}
tmp_ = g['func'][data_load_essentia](filename)
g['l1_files'][filename_short]['path'] = filename
g['l1_files'][filename_short]['data'] = tmp_[0]
g['l1_files'][filename_short]['numframes'] = samples_to_frames(len(tmp_[0]))
g['l1_files'][filename_short]['sr'] = tmp_[1]
# layer 2: beatiness, compute beatiness on data
# g['l2_beatiness'] = {}
for file_ in g['l1_files']:
# file_key = '{0}-{1}'.format(file_, 'beatiness')
# g['l2_beatiness'][file_] = {}
tmp_ = g['func'][compute_tempo_beats_essentia](g['l1_files'][file_]['data'])
# g['l2_beatiness'][file_] = tmp_
# g['l1_files'][file_]['beatiness'] = tmp_
g['l1_files'][file_].update(dict([('beatiness' + _, tmp_[_]) for _ in tmp_]))
# layer 3: extractor
# g['l3_extractor'] = {}
for file_ in g['l1_files']:
print('l3_extractor on {0}'.format(file_))
# file_key = '{0}-{1}'.format(file_, 'extractor')
# g['l2_extractor'][file_] = {}
tmp_ = g['func'][compute_music_extractor_essentia](g['l1_files'][file_]['path'])
# g['l3_extractor'][file_] = tmp_
# g['l1_files'][file_]['extractor'] = tmp_
g['l1_files'][file_].update(dict([('extractor_' + _, tmp_[_]) for _ in tmp_]))
# layer 4: paa features
# g['l4_paa_features'] = {}
for file_ in g['l1_files']:
# file_key = '{0}-{1}'.format(file_, 'extractor')
# g['l4_paa_features'][file_] = {}
tmp_ = g['func'][compute_features_paa](g['l1_files'][file_]['path'])
# g['l4_paa_features'][file_]['features_st'] = dict(zip(tmp_[1], tmp_[0]))
# g['l4_paa_features'][file_]['features_mt'] = dict(zip(tmp_[1], tmp_[2]))
g['l1_files'][file_].update(dict(zip(['features_st_' + _ for _ in tmp_[1]], [_.mean() for _ in tmp_[0]])))
g['l1_files'][file_].update(dict(zip(['features_mt_' + _ for _ in tmp_[1]], [_.mean() for _ in tmp_[2]])))
# g['l1_files'][file_]['features_mt'] = dict(zip(tmp_[1], tmp_[2]))
# layer 5:
pickle.dump(g, open('g.pkl', 'wb'))
# print('files {0}'.format(pformat(files)))
# plot dictionary g as graph
autoedit_graph_from_dict(g=g, plot=False)
l1_files_df = pd.DataFrame.from_dict(g['l1_files']).T
# sort_key = 'features_mt_energy_entropy_mean'
# sort_key = 'features_mt_energy_mean'
# sort_key = 'features_mt_spectral_centroid_mean'
# sort_key = 'features_mt_spectral_entropy_mean'
# sort_key = 'features_mt_spectral_flux_mean'
# sort_key = 'features_mt_spectral_rolloff_mean'
# sort_key = 'features_mt_spectral_spread_mean'
# sort_key = 'features_mt_zcr_mean'
sort_key = kwargs['sorter']
print('Sorting l1_files by {0}'.format(l1_files_df.sort_values(sort_key, ascending=False).path.to_string()))
l1_files_df.sort_values(sort_key, ascending=False).path.to_csv('automix-assembled-{0}-{1}.{2}'.format(3, sort_key, 'csv'))
if args.write:
track_assemble_from_segments_sequential(files=list(l1_files_df.sort_values(sort_key, ascending=False).path),
output_filename='automix-assembled-{0}-{1}.{2}'.format(3, sort_key, 'wav'),
duration=None)
def __test(x, y, hop_length, n_fft):
y_test = librosa.samples_to_frames(x,
hop_length=hop_length,
n_fft=n_fft)
assert np.allclose(y, y_test)
test.extend(drum[int(0.5 * drum.shape[0]):]) test.extend(drum) test.extend(beattt) test.extend(sapce) test.extend(sapce) test.extend(drum) test.extend(sapce) test.extend(beattt) test.extend(sapce) test.extend(drum) test.extend(drum[int(0.5 * drum.shape[0]):]) sss = np.zeros(data.shape) sss[:np.array(test).shape[0]] = np.array(test) #sd.play(sss*5+data*5, fs) '''gen drum''' fps = librosa.samples_to_frames(fs, hop_length=hop_len, n_fft=win_len) fps = 100 print(fps) proc = BeatTrackingProcessor(look_aside=0.2, fps=fps) act = RNNBeatProcessor()(all_data) beat_times = proc(act) song_len = librosa.samples_to_time(data.shape, sr=fs)[0] beat = np.zeros(all_data.shape) idx = np.where(beat_times <= song_len)[0] new_beat_times = np.zeros(idx.shape) new_beat_times[idx] = beat_times[idx] beat_samples = librosa.time_to_samples(new_beat_times, sr=fs) cand_len = len(drum)
def read_data(path_folder):
"""Reads sample data from files and extracts features"""
os.chdir(DATA_PATH)
sample_paths = [
'Gitarre monophon/Samples/NoFX', 'Gitarre polyphon/Samples/NoFX'
]
train_data = []
train_labels = []
for path in sample_paths:
sample_path = os.path.join(path_folder, path)
os.chdir(sample_path)
for file_name in os.listdir(os.getcwd()):
if file_name.endswith(".wav"):
print(file_name)
os.chdir(Path('../../Labels'))
# Label names are: Edge, Gain, Tone
label_file = file_name[:-4] + '.pickle'
# label = [0.0, 0.0, 0.0]
with open(label_file, 'rb') as handle:
label = pickle.load(handle)
print(label)
if path_folder == 'DlyRandomSamples': # Fix limited delay plugin range
label[0] = label[0] * 4.0
label[1] = label[1] * 10.0
os.chdir('../Samples/NoFX')
train_labels.append(label)
# Loading the audio
y, sr = librosa.load(file_name, sr=44100)
# Onset Detection
y = np.insert(y, 0, np.zeros(1023))
y = librosa.util.normalize(y)
onset_frame = librosa.onset.onset_detect(y=y,
sr=sr,
units='frames',
pre_max=20000,
post_max=20000,
pre_avg=20000,
post_avg=20000,
delta=0,
wait=1000)
offset_frame = librosa.samples_to_frames(samples=y.shape[0])
onset_sample = librosa.core.frames_to_samples(onset_frame[0])
offset_sample = librosa.core.frames_to_samples(offset_frame)
y_cut = y[onset_sample:offset_sample]
v_features = []
if path_folder == 'DistRandomSamples':
v_features = get_dist_feat(y_cut=y_cut, sr=sr)
elif path_folder == 'TremRandomSamples':
v_features = get_trem_feat(y_cut=y_cut, sr=sr)
elif path_folder == 'DlyRandomSamples':
v_features = get_dly_feat(y_cut=y_cut, sr=sr, y=y)
else:
print('Sample folder for feature extraction not found')
train_data.append(np.hstack(v_features))
os.chdir(DATA_PATH)
train_data = np.array(train_data)
print(train_data.shape)
scaler = preprocessing.StandardScaler()
train_data = scaler.fit_transform(train_data)
train_labels = np.array(train_labels)
os.chdir(DATA_PATH)
return train_data, train_labels
def modify_classical(level,
param_dict,
start,
dur=4,
sig_dur=4,
segment=False):
print "Classical modification begun.."
# snap to segment or start marker
if segment:
# use pre-computed segment boundaries
start_bounds = param_dict['bounds'][:, 0]
nearest_bound = start_bounds[np.where(start_bounds >= start)][0]
else:
# simply use start marker
nearest_bound = start
# level 1 - tempo change -- volume envelope needs fixing!!
if level == 1:
offset = 0.8
# in frames, conversion to samples required
tempo_curve = param_dict['tempo']
nearest_bound_in_frame = librosa.samples_to_frames([nearest_bound])[0]
tempo_factor = tempo_curve[nearest_bound_in_frame]
# change dur to account for tempo factor
dur = int(np.ceil(dur * (tempo_factor + offset)))
clip = gs.audio_buffer[nearest_bound:nearest_bound + (dur * gs.sr)]
shrink = librosa.effects.time_stretch(clip, offset + tempo_factor)
# normalizing CRAP.
librosa.output.write_wav("clip.wav", clip, gs.sr)
as_clip = pydub.AudioSegment.from_wav("clip.wav")
as_amp = as_clip.dBFS
librosa.output.write_wav("shrink.wav", shrink, gs.sr)
shrink = match_target_amplitude(
pydub.AudioSegment.from_wav("shrink.wav"), as_amp)
shrink.export("new_shrink.wav", format="wav")
shrink, sr = librosa.load("new_shrink.wav")
compensate_factor = 1.2
remainder = np.concatenate(
(shrink, gs.audio_buffer[nearest_bound + (dur * gs.sr):]))
gs.audio_buffer[nearest_bound:nearest_bound +
len(remainder)] = remainder
gs.audio_buffer[-1 * (len(clip) - len(shrink)):] = 0
# taper_buffer_edges(nearest_bound, nearest_bound + len(shrink), 1.0)
# if stretch instead of shrink
# gs.audio_buffer = np.concatenate((gs.audio_buffer, np.zeros(len(stretch) - len(clip))))
# gs.audio_buffer[nearest_bound:] = np.concatenate( (window(stretch), window(gs.audio_buffer[nearest_bound + (dur*gs.sr):])) )
# level 0 - echo with delay
elif level == 0:
offset = int(0.75 * gs.sr)
clip = gs.audio_buffer[nearest_bound:nearest_bound + (dur * gs.sr)]
echo_amp_curve = param_dict['echo']
if echo_amp_curve != None:
echo_amp = echo_amp_curve[nearest_bound]
else:
echo_amp = 0.8
delay_curve = param_dict['delay']
nearest_bound_in_frame = librosa.samples_to_frames([nearest_bound])[0]
delay_in_secs = delay_curve[nearest_bound_in_frame]
delay_in_samps = int(delay_in_secs * gs.sr)
delay_in_samps += offset
# gs.audio_buffer[nearest_bound + delay_in_samps: nearest_bound + delay_in_samps + (dur*gs.sr)] += ((0.8*echo_amp) * window(clip))
gs.audio_buffer[nearest_bound + delay_in_samps:nearest_bound +
delay_in_samps + (dur * gs.sr)] += ((echo_amp) *
window(clip))
# level 2 - alert sample
else:
# issue sampled alert
alert = param_dict['alert']
if len(alert) > sig_dur * gs.sr:
alert = alert[:sig_dur * gs.sr]
remainder = np.concatenate(
(square_window(alert),
gs.audio_buffer[nearest_bound + len(alert):]))
gs.audio_buffer[nearest_bound:nearest_bound +
len(remainder)] = remainder
taper_buffer_edges(nearest_bound,
nearest_bound + len(alert),
1.0,
low_end=0.0)
print "Classical modification completed.."
return True
def extract_features(sample, training=True):
"""Extracts features from sample"""
X_list = []
y_list = []
print(sample.label)
print(sample.file_name)
snd = parselmouth.Sound(os.path.join(sample.path, sample.file_name))
# Onset Detection
sample.sig = np.insert(sample.sig, 0, np.zeros(1024))
sample.sig = librosa.util.normalize(sample.sig)
onset_frame = librosa.onset.onset_detect(y=sample.sig,
sr=sample.fs,
units='frames',
backtrack=False,
pre_max=20000,
post_max=20000,
pre_avg=20000,
post_avg=20000,
delta=0.0,
wait=1000)
offset_frame = int(
round(0.75 * librosa.samples_to_frames(samples=sample.sig.shape[0])))
if offset_frame - 32 <= onset_frame[0]: # or not training
offset_frame = librosa.samples_to_frames(samples=sample.sig.shape[0])
if training:
print(
'Training Sample shorter than 32 Frames {}; Sample Length: {}'.
format(sample.file_name, offset_frame - onset_frame[0]))
if offset_frame - 32 <= onset_frame[0]:
onset_frame[0] = 0
onset_sample = librosa.core.frames_to_samples(onset_frame[0])
offset_sample = librosa.core.frames_to_samples(offset_frame)
# plots.waveform(sample, onset_sample)
smp_cut = sample.sig[onset_sample:offset_sample]
# Randomly shorten sample from 1/4 to 3/4 note length at 120 BPM
# smp_cut = smp_cut[:int(np.random.uniform(0.66, 1, [1, 1])[0, 0]*len(smp_cut))]
# Add noise to sample at max -48dBFS
# smp_cut += np.random.uniform(-2**-9, 2**-9, [len(smp_cut)])
# Time series features from librosa
mfcc = librosa.feature.mfcc(y=smp_cut, sr=sample.fs)
# plots.spectrogram(smp_cut)
mfcc_pos = (mfcc - np.min(mfcc))
mfcc_norm = mfcc_pos / np.max(mfcc_pos) - np.mean(mfcc_pos)
mfcc_delta = librosa.feature.delta(mfcc_norm)
spec_contr = librosa.feature.spectral_contrast(y=smp_cut, sr=sample.fs)
# plots.spec_contrast(spec_contr)
phase_res = phase_fmax(smp_cut)
# plots.phase_reg_line_deviation(phase_res)
zero_cr = librosa.feature.zero_crossing_rate(y=smp_cut)
zero_cr_delta = librosa.feature.delta(zero_cr)
rms = librosa.feature.rms(y=smp_cut)
rms *= 1 / rms.max()
rms_delta = librosa.feature.delta(rms)
# Time series features from praat
pitch = snd.to_pitch().to_array()
pitch_curve, voice_prob = zip(*pitch[0][:])
pitch_curve = np.array(pitch_curve)
voice_prob = np.array(voice_prob)
pitch_onset = int(
(onset_sample / sample.sig.shape[0]) * pitch_curve.shape[0])
pitch_curve = pitch_curve[pitch_onset:]
voice_prob = voice_prob[pitch_onset:]
# plots.pitch_voiced_curve(pitch_curve, voice_prob)
pitch_curve = np.reshape(pitch_curve, [1, pitch_curve.shape[0]])
# plots.pitch(pitch_curve)
voice_prob = np.reshape(voice_prob, [1, voice_prob.shape[0]])
harmonicity = call(snd, "To Harmonicity (cc)", 0.01, 75, 0.1, 1.0)
hnr = call(harmonicity, "Get mean", 0, 0)
m_features = np.concatenate((mfcc_norm, mfcc_delta, spec_contr, zero_cr,
zero_cr_delta, rms, rms_delta))
v_features = functionals(m_features)
# phase_res and pitch curve have different lenghts from m_features, so functionals
# need to be analysed individually
v_features = np.append(v_features, functionals(phase_res))
v_features = np.append(v_features, functionals(pitch_curve))
v_features = np.append(v_features, functionals(voice_prob))
v_features = np.append(v_features, hnr)
X_list.append(v_features)
y_list.append(sample.label)
return X_list, y_list