def upload_files():
if request.method == 'POST':
f = request.files['file']
fname = secure_filename(f.filename)
path = os.path.join(app.config['UPLOAD_DIR'], fname)
f.save(path)
timbre = timbral_models.timbral_extractor(os.path.join(path))
print(timbre)
return 'File upload complete (%s)' % path
def ac_timbral_models(audiofile, ac_descriptors):
logger.debug('{0}: computing timbral models'.format(audiofile))
converted_filename = convert_to_wav(audiofile)
try:
timbre = timbral_models.timbral_extractor(converted_filename,
clip_output=True,
verbose=False)
timbre['reverb'] = timbre['reverb'] == 1
ac_descriptors.update(timbre)
except Exception as e:
logger.debug('{0}: timbral models computation failed ("{1}")'.format(
audiofile, e))
def process_sounds(directory):
"""Processes sounds using AudioCommons Timbral Models."""
result = []
paths = Path(directory).rglob("*")
for path in paths:
try:
path_str = str(path)
sound = timbral_extractor(path_str, clip_output=True)
del sound["reverb"]
sound["id"] = uuid1().hex
sound["name"] = Path(path_str).resolve().stem
sound["pack_name"] = ""
sound["path"] = path_str
sound["tag"] = ""
sound["type"] = Path(path_str).suffix.replace(".", "")
sound["verified"] = False
result.append(sound)
except:
continue
return result
import json
import os
from pathlib import Path
import timbral_models
script_directory_path = Path(__file__).resolve().parent
input_directory_path = os.path.join(script_directory_path, 'mock_sounds')
file_names = os.listdir(input_directory_path)
for file_name in file_names:
print(f"Analyzing {file_name}")
result = timbral_models.timbral_extractor(os.path.join(
input_directory_path, file_name),
clip_output=True)
output_directory_path = os.path.join(script_directory_path,
'./timbral_characteristics')
if not os.path.exists(output_directory_path):
os.makedirs(output_directory_path)
file_path = os.path.join(output_directory_path,
f"{Path(file_name).resolve().stem}.json")
with open(file_path, "w") as file:
json.dump(result, file)
print(f"Result written to {file_path}")
def analysis_function(loop, sampleRate=16000):
lp_filter = es.LowPass(cutoffFrequency=90, sampleRate=sampleRate)
bp_filter = es.BandPass(bandwidth=20,
cutoffFrequency=280,
sampleRate=sampleRate)
hp_filter = es.HighPass(cutoffFrequency=9000, sampleRate=sampleRate)
[_, pattern] = ADT([loop],
output_act='yes',
tab='no',
save_dir="analysis/")
pattern = np.array(pattern)[0]
time_audio = np.linspace(0, float(29538) / 16000, 29538)
time_act = np.linspace(0, float(29538) / 16000, 160)
final_pattern = np.clip(
np.array([
interp1d(time_act, pattern[0, :, 0])(time_audio),
interp1d(time_act, pattern[1, :, 0])(time_audio),
interp1d(time_act, pattern[2, :, 0])(time_audio)
]).T, 0.0, 1.0)
final_pattern = final_pattern / final_pattern.max(axis=0)
final_pattern = np.expand_dims(final_pattern, 0)
audio_file = es.MonoLoader(filename=loop, sampleRate=sampleRate)
loop_basename = ntpath.basename(loop)
lpf_audio = lp_filter(audio_file())
bpf_audio = bp_filter(audio_file())
hpf_audio = hp_filter(audio_file())
sf.write("analysis/lpf_" + loop_basename, lpf_audio, sampleRate)
sf.write("analysis/bpf_" + loop_basename, bpf_audio, sampleRate)
sf.write("analysis/hpf_" + loop_basename, hpf_audio, sampleRate)
unordered_kick_features = timbral_models.timbral_extractor(
"analysis/lpf_" + loop_basename, clip_output=True)
unordered_snare_features = timbral_models.timbral_extractor(
"analysis/bpf_" + loop_basename, clip_output=True)
unordered_hh_features = timbral_models.timbral_extractor("analysis/hpf_" +
loop_basename,
clip_output=True)
features_kick = [
unordered_kick_features['warmth'] / 69.738235,
unordered_kick_features['roughness'] / 71.95989,
unordered_kick_features['brightness'] / 82.336105,
unordered_kick_features['hardness'] / 75.53646,
unordered_kick_features['boominess'] / 71.00043,
unordered_kick_features['depth'] / 100.0,
unordered_kick_features['sharpness'] / 81.7323,
]
features_snare = [
unordered_snare_features['warmth'] / 69.57681,
unordered_snare_features['roughness'] / 67.66642,
unordered_snare_features['brightness'] / 80.19115,
unordered_snare_features['hardness'] / 71.689445,
unordered_snare_features['boominess'] / 61.422714,
unordered_snare_features['depth'] / 100.0,
unordered_snare_features['sharpness'] / 71.406494
]
features_hh = [
unordered_hh_features['warmth'] / 32.789112,
unordered_hh_features['roughness'] / 1.0,
unordered_hh_features['brightness'] / 85.24432,
unordered_hh_features['hardness'] / 67.71172,
unordered_hh_features['boominess'] / 2.491137,
unordered_hh_features['depth'] / 0.5797179,
unordered_hh_features['sharpness'] / 87.83693
]
hpcp = file_to_hpcp(audio_file())
#[69.57681, 67.66642, 80.19115, 71.689445, 61.422714, 100.0, 71.406494]
#[32.789112, 1.0, 85.24432, 67.71172, 2.491137, 0.5797179, 87.83693]
#[69.738235, 71.95989, 82.336105, 75.53646, 71.00043, 100.0, 81.7323]
return final_pattern, hpcp, features_kick, features_snare, np.clip(
features_hh, 0, 1)
import timbral_models sample = '2248.wav' timbre = timbral_models.timbral_extractor(sample) print(timbre)