Python Engine.flush示例

示例#1

class Yaafe(EnhancedObject):
    '''Yaafe toolbox wrapper. To be used with Database object.

    Attributes:
        sample_rate     The Files' sample rate
        plan_filename   The Featue Plan filename

    Methods:
        process(audiofile)          Process audiofile and return features
        get_X(entries_list, feat)   Fetch array of processed data from Database
        get_y                       Fetch subdir i's from Database
    `
    Init:
        Yaafe(sample_rate, feature_plan)
    '''
    _features = {
        'spec_rolloff': ("SpectralRolloff blockSize=512 stepSize=128 "
                         "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spec_shape': ("SpectralShapeStatistics blockSize=512 stepSize=128 "
                       "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spec_flux': ("SpectralFlux blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'amp_mod': ("AmplitudeModulation blockSize=512 stepSize=128 >"
                    "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'auto': ("AutoCorrelation  blockSize=512 stepSize=128 >"
                 "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'lpc': ("LPC  blockSize=512 stepSize=128 > StatisticalIntegrator "
                "NbFrames=40 StepNbFrames=8"),
        'loudness': ("Loudness blockSize=512 stepSize=128 >"
                     "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'mfcc': ("MFCC blockSize=512 stepSize=128 > StatisticalIntegrator "
                 "NbFrames=40 StepNbFrames=8"),
        'mel_spectrum': ("MelSpectrum blockSize=512, stepSize=128 >"
                         "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'obsi': ("OBSI blockSize=512 stepSize=128 > StatisticalIntegrator "
                 "NbFrames=40 StepNbFrames=8"),
        'obsir': ("OBSIR blockSize=512 stepSize=128 >"
                  "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'perc_sharp': ("PerceptualSharpness blockSize=512 stepSize=128 >"
                       "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'perc_spread': ("PerceptualSpread blockSize=512 stepSize=128 >"
                        "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_crest': ("SpectralCrestFactorPerBand blockSize=512 "
                        "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                        "StepNbFrames=8"),
        'spec_decr': ("SpectralDecrease blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_flat': ("SpectralFlatness blockSize=512 stepSize=128 >"
                       "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_flat_band': ("SpectralFlatnessPerBand blockSize=512 "
                            "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                            "StepNbFrames=8"),
        'spect_slope': ("SpectralSlope blockSize=512 stepSize=128 >"
                        "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_var': ("SpectralVariation blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'temp_shape': ("TemporalShapeStatistics blockSize=512 stepSize=128 "
                       "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'zcr': ("ZCR blockSize=512 stepSize=128 > StatisticalIntegrator "
                "NbFrames=40 StepNbFrames=8"),
        'env_shape': ("EnvelopeShapeStatistics blockSize=512 stepSize=128"
                      " > StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'comp_onest': ("ComplexDomainOnsetDetection blockSize=512 "
                       "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                       "StepNbFrames=8"),
    }

    def __init__(self, sample_rate, features=None):
        if features is None:
            features = self._features
        self.sample_rate = sample_rate
        self.initialize(features)
        self.features = features

    def initialize(self, feature_dict):
        """ Run the required boilerplate for yaafe """
        self.feature_dict = feature_dict
        self.fp = FeaturePlan(sample_rate=self.sample_rate, normalize=0.98)
        for name, desc in self.feature_dict.items():
            self.fp.addFeature("{0}: {1}".format(name, desc))
        self.df = self.fp.getDataFlow()
        self.engine = Engine()
        self.engine.load(self.df)
        self.afp = AudioFileProcessor()
        return self

    def save_fplan(self, name):
        """ Save a feature plan (text file) """
        text_file = open("{}.txt".format(name), 'w')
        for name, desc in self.features.items():
            text_file.write("{}: {}".format(name, desc))
        text_file.close()

    def process(self, audiofile):
        """ Process function for running a file through yaafe's
            feature extractor
        """
        self.afp.processFile(self.engine, audiofile)
        out = self.engine.readAllOutputs()
        self.engine.flush()
        return sorted(out)

示例#2

文件： toolbox.py 项目： sotlampr/generic_audio_classifier

class Yaafe(EnhancedObject):
    '''Yaafe toolbox wrapper. To be used with Database object.

    Attributes:
        sample_rate     The Files' sample rate
        plan_filename   The Featue Plan filename

    Methods:
        process(audiofile)          Process audiofile and return features
        get_X(entries_list, feat)   Fetch array of processed data from Database
        get_y                       Fetch subdir i's from Database
    `
    Init:
        Yaafe(sample_rate, feature_plan)
    '''
    _features = {
        'spec_rolloff': ("SpectralRolloff blockSize=512 stepSize=128 "
                         "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spec_shape': ("SpectralShapeStatistics blockSize=512 stepSize=128 "
                       "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spec_flux': ("SpectralFlux blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'amp_mod': ("AmplitudeModulation blockSize=512 stepSize=128 >"
                    "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'auto': ("AutoCorrelation  blockSize=512 stepSize=128 >"
                 "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'lpc': ("LPC  blockSize=512 stepSize=128 > StatisticalIntegrator "
                "NbFrames=40 StepNbFrames=8"),
        'loudness': ("Loudness blockSize=512 stepSize=128 >"
                     "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'mfcc': ("MFCC blockSize=512 stepSize=128 > StatisticalIntegrator "
                 "NbFrames=40 StepNbFrames=8"),
        'mel_spectrum': ("MelSpectrum blockSize=512, stepSize=128 >"
                         "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'obsi': ("OBSI blockSize=512 stepSize=128 > StatisticalIntegrator "
                 "NbFrames=40 StepNbFrames=8"),
        'obsir': ("OBSIR blockSize=512 stepSize=128 >"
                  "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'perc_sharp': ("PerceptualSharpness blockSize=512 stepSize=128 >"
                       "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'perc_spread': ("PerceptualSpread blockSize=512 stepSize=128 >"
                        "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_crest': ("SpectralCrestFactorPerBand blockSize=512 "
                        "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                        "StepNbFrames=8"),
        'spec_decr': ("SpectralDecrease blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_flat': ("SpectralFlatness blockSize=512 stepSize=128 >"
                       "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_flat_band': ("SpectralFlatnessPerBand blockSize=512 "
                            "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                            "StepNbFrames=8"),
        'spect_slope': ("SpectralSlope blockSize=512 stepSize=128 >"
                        "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'spect_var': ("SpectralVariation blockSize=512 stepSize=128 >"
                      "StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'temp_shape': ("TemporalShapeStatistics blockSize=512 stepSize=128 "
                       "> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'zcr': ("ZCR blockSize=512 stepSize=128 > StatisticalIntegrator "
                "NbFrames=40 StepNbFrames=8"),
        'env_shape': ("EnvelopeShapeStatistics blockSize=512 stepSize=128"
                      " > StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
        'comp_onest': ("ComplexDomainOnsetDetection blockSize=512 "
                       "stepSize=128 > StatisticalIntegrator NbFrames=40 "
                       "StepNbFrames=8"),
        }

    def __init__(self, sample_rate, features=None):
        if features is None:
            features = self._features
        self.sample_rate = sample_rate
        self.initialize(features)
        self.features = features

    def initialize(self, feature_dict):
        """ Run the required boilerplate for yaafe """
        self.feature_dict = feature_dict
        self.fp = FeaturePlan(
            sample_rate=self.sample_rate,
            normalize=0.98)
        for name, desc in self.feature_dict.items():
            self.fp.addFeature("{0}: {1}".format(name, desc))
        self.df = self.fp.getDataFlow()
        self.engine = Engine()
        self.engine.load(self.df)
        self.afp = AudioFileProcessor()
        return self

    def save_fplan(self, name):
        """ Save a feature plan (text file) """
        text_file = open("{}.txt".format(name), 'w')
        for name, desc in self.features.items():
            text_file.write("{}: {}".format(name, desc))
        text_file.close()

    def process(self, audiofile):
        """ Process function for running a file through yaafe's
            feature extractor
        """
        self.afp.processFile(self.engine, audiofile)
        out = self.engine.readAllOutputs()
        self.engine.flush()
        return sorted(out)

示例#3

文件： plotter.py 项目： sotlampr/generic_audio_classifier

import numpy as np
import matplotlib.pyplot as plt
from yaafelib import FeaturePlan, Engine, AudioFileProcessor


FPLAN = FeaturePlan(sample_rate=44100)
FPLAN.addFeature("mfcc: MFCC blockSize=512 stepSize=128")
FPLAN.addFeature("mfcc_stat: MFCC blockSize=512 stepSize=128 > "
                 "StatisticalIntegrator NbFrames=40 StepNbFrames=8")
ENGINE = Engine()
ENGINE.load(FPLAN.getDataFlow())
PROCESSOR = AudioFileProcessor()

PROCESSOR.processFile(ENGINE, 'track.wav')
DATA = ENGINE.readAllOutputs()
ENGINE.flush()

X_MFCC = DATA['mfcc']
X_MFCC_STAT = DATA['mfcc_stat']

MFCC_DESC = list()
for i in range(1, 14):
    desc = "Average for Band {}".format(i)
    MFCC_DESC.append(desc)
for i in range(1, 14):
    desc = "Standard Dev. for Band {}".format(i)
    MFCC_DESC.append(desc)

plt.ion()
FIG = plt.figure()
FIG.set_size_inches(14, 8)

示例#4

""" Stand-alone application to demonstrate yaafe's transformations """
import numpy as np
import matplotlib.pyplot as plt
from yaafelib import FeaturePlan, Engine, AudioFileProcessor

FPLAN = FeaturePlan(sample_rate=44100)
FPLAN.addFeature("mfcc: MFCC blockSize=512 stepSize=128")
FPLAN.addFeature("mfcc_stat: MFCC blockSize=512 stepSize=128 > "
                 "StatisticalIntegrator NbFrames=40 StepNbFrames=8")
ENGINE = Engine()
ENGINE.load(FPLAN.getDataFlow())
PROCESSOR = AudioFileProcessor()

PROCESSOR.processFile(ENGINE, 'track.wav')
DATA = ENGINE.readAllOutputs()
ENGINE.flush()

X_MFCC = DATA['mfcc']
X_MFCC_STAT = DATA['mfcc_stat']

MFCC_DESC = list()
for i in range(1, 14):
    desc = "Average for Band {}".format(i)
    MFCC_DESC.append(desc)
for i in range(1, 14):
    desc = "Standard Dev. for Band {}".format(i)
    MFCC_DESC.append(desc)

plt.ion()
FIG = plt.figure()
FIG.set_size_inches(14, 8)