def chroma(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if not cache.exists():
y, sr = librosa.load(workable)
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
np.save(cache, chroma)
else:
chroma = np.load(cache)
self.buffer[str(workable)] = chroma.tolist()
def filter_duration(x, low: float, high: float) -> bool:
hsh = create_hash(x, low, high)
tmp = Path("/tmp") / "ftis_cache"
tmp.mkdir(exist_ok=True)
cache = tmp / f"{hsh}.npy"
if not cache.exists():
dur = get_duration(x)
np.save(cache, dur)
else:
dur = np.load(cache)
return dur < high and dur > low
def flux(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if not cache.exists():
sr = get_sr(workable)
y, _ = librosa.load(workable, sr=sr)
fft = librosa.stft(y, win_length=self.windowsize, hop_length=self.hopsize)
flux = np.sum(np.abs(np.diff(np.abs(fft))), axis=0)
np.save(cache, flux)
else:
flux = np.load(cache)
self.buffer[workable] = flux.tolist()
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if cache.exists():
mfcc = np.load(cache, allow_pickle=True)
else:
mfcc = get_buffer(
fluid.mfcc(
str(workable),
fftsettings=self.fftsettings,
numbands=self.numbands,
numcoeffs=self.numcoeffs,
minfreq=self.minfreq,
maxfreq=self.maxfreq,
),
"numpy",
)
np.save(cache, mfcc)
self.buffer[str(workable)] = mfcc.tolist()
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if cache.exists():
feature = np.load(cache, allow_pickle=True)
print("loaded cache")
else:
y, sr = librosa.load(workable, sr=None, mono=True)
feature = librosa.feature.mfcc(
y=y,
sr=sr,
n_mfcc=self.numcoeffs,
dct_type=self.dct,
n_mels=self.numbands,
fmax=self.maxfreq,
fmin=self.minfreq,
hop_length=self.hop,
n_fft=self.window,
)
np.save(cache, feature)
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if cache.exists():
loudness = np.load(cache, allow_pickle=True)
else:
loudness = get_buffer(
fluid.loudness(workable["file"],
windowsize=self.windowsize,
hopsize=self.hopsize,
kweighting=self.kweighting,
truepeak=self.truepeak,
numframes=workable["numframes"],
startframe=workable["startframe"]),
"numpy",
)
np.save(cache, loudness)
workable["feature"] = loudness.tolist()
self.buffer[workable["id"]] = workable
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.wav"
if not cache.exists():
slice_output = get_buffer(
fluid.onsetslice(
workable,
indices=cache,
fftsettings=self.fftsettings,
filtersize=self.filtersize,
framedelta=self.framedelta,
metric=self.metric,
minslicelength=self.minslicelength,
threshold=self.threshold,
),
"numpy",
)
else:
slice_output = get_buffer(cache)
self.buffer[str(workable)] = slice_output.tolist()
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if cache.exists():
pitch = np.load(cache, allow_pickle=True)
else:
pitch = get_buffer(
fluid.pitch(workable["file"],
algorithm=self.algorithm,
minfreq=self.minfreq,
maxfreq=self.maxfreq,
unit=self.unit,
fftsettings=self.fftsettings,
numframes=workable["numframes"],
startframe=workable["startframe"]),
"numpy",
)
np.save(cache, pitch)
workable["features"] = pitch.tolist()
self.buffer[workable["id"]] = workable
def analyse(self, workable):
hsh = create_hash(workable, self.identity)
cache = self.process.cache / f"{hsh}.npy"
if cache.exists():
cqt = np.load(cache, allow_pickle=True)
else:
y, sr = librosa.load(workable, sr=None, mono=True)
cqt = librosa.cqt(y, sr,
fmin=self.minfreq,
n_bins=self.n_bins,
bins_per_octave=self.bins_per_octave,
tuning=self.tuning,
filter_scale=self.filter_scale,
norm=self.norm,
sparsity=self.sparsity,
window=self.window,
scale=self.scale,
pad_mode=self.pad_mode,
)
np.save(cache, cqt)
self.buffer[str(workable)] = np.abs(cqt).tolist()
def analyse_items(self):
# TODO import this function from a global place that can be used in CORPUS too
median_loudness = {}
for x in self.input:
hsh = create_hash(x, self.min_loudness, self.max_loudness)
cache = self.process.cache / f"{hsh}.npy"
if not cache.exists():
med_loudness = get_buffer(
stats(loudness(x, hopsize=4410, windowsize=17640)),
"numpy")
np.save(cache, med_loudness)
else:
med_loudness = np.load(cache, allow_pickle=True)
median_loudness[str(x)] = med_loudness[0][5]
vals = np.array([x for x in median_loudness.values()])
min_perc = np.percentile(vals, self.min_loudness)
max_perc = np.percentile(vals, self.max_loudness)
self.output = [
k for k, v in median_loudness.items()
if v <= max_perc and v >= min_perc
]
def loudness(self, min_loudness: int = 0, max_loudness: int = 100):
hopsize = 4410
windowsize = 17640
with Progress() as progress:
task = progress.add_task("[cyan]Corpus Filtering: Loudness",
total=len(self.items))
median_loudness = {}
for x in self.items:
hsh = create_hash(x, hopsize, windowsize)
# Make sure a sane temporary path exists
tmp = Path("/tmp") / "ftis_cache"
tmp.mkdir(exist_ok=True)
cache = tmp / f"{hsh}.npy"
if not cache.exists():
med_loudness = get_buffer(
stats(
loudness(x, hopsize=hopsize,
windowsize=windowsize)), "numpy")
np.save(cache, med_loudness)
else:
med_loudness = np.load(cache, allow_pickle=True)
median_loudness[str(x)] = med_loudness[0][5]
progress.update(task, advance=1)
# Get percentiles and filter
vals = np.array([x for x in median_loudness.values()])
min_perc = np.percentile(vals, min_loudness)
max_perc = np.percentile(vals, max_loudness)
self.items = [
k for k, v in median_loudness.items()
if v <= max_perc and v >= min_perc
]
return self
def create_identity(self):
self.identity["hash"] = create_hash(self.items, self.is_filtering,
self.path, self.file_type)
def create_identity(self) -> None:
p = {k: str(v) for k, v in vars(self).items() if k not in ignored_keys}
self.parent_parameters = {}
self.traverse_parent_parameters()
self.identity["hash"] = create_hash(self.parent_parameters, p)
