Python wavfile.read方法代碼示例

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def wavefile_to_waveform(wav_file, features_type):
    data, sr = sf.read(wav_file)
    if features_type == 'vggish':
        tmp_name = str(int(np.random.rand(1)*1000000)) + '.wav'
        sf.write(tmp_name, data, sr, subtype='PCM_16')
        sr, wav_data = wavfile.read(tmp_name)
        os.remove(tmp_name)
        # sr, wav_data = wavfile.read(wav_file) # as done in VGGish Audioset
        assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
        data = wav_data / 32768.0  # Convert to [-1.0, +1.0]
  
    # at least one second of samples, if not repead-pad
    src_repeat = data
    while (src_repeat.shape[0] < sr): 
        src_repeat = np.concatenate((src_repeat, data), axis=0)
        data = src_repeat[:sr]

    return data, sr 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="gray", channel=0, name='tmp.png', alpha=1, offset=0):
    samplerate, samples = wav.read(audiopath)
    samples = samples[:, channel]
    s = stft(samples, binsize)

    sshow, freq = logscale_spec(s, factor=1, sr=samplerate, alpha=alpha)
    sshow = sshow[2:, :]
    ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
    timebins, freqbins = np.shape(ims)
    
    ims = np.transpose(ims)
    # ims = ims[0:256, offset:offset+768] # 0-11khz, ~9s interval
    ims = ims[0:256, :] # 0-11khz, ~10s interval
    #print "ims.shape", ims.shape
    
    image = Image.fromarray(ims) 
    image = image.convert('L')
    image.save(name) 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def read_file(filename):
    """
    Read wave file as mono.

    Args:
        - filename (str) : wave file / path.

    Returns:
        tuple of sampling rate and audio data.
    """
    fs, sig = read(filename=filename)
    if (sig.ndim == 1):
        samples = sig
    else:
        samples = sig[:, 0]
    return fs, samples 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def triangle(t, randfunc=np.random.rand, t0_fac=None): # ramp up then down
    height = (0.4 * randfunc() + 0.4) * np.random.choice([-1,1])
    width = randfunc()/4 * t[-1]     # half-width actually
    t0 = 2*width + 0.4 * randfunc()*t[-1] if t0_fac is None else t0_fac*t[-1]
    x = height * (1 - np.abs(t-t0)/width)
    x[np.where(t < (t0-width))] = 0
    x[np.where(t > (t0+width))] = 0
    amp_n = (0.1*randfunc()+0.02)   # add noise
    return x + amp_n*pinknoise(t.shape[0])


# Prelude to read_audio_file
# Tried lots of ways of doing this.. most are slow.
#signal, rate = librosa.load(filename, sr=sr, mono=True, res_type='kaiser_fast') # Librosa's reader is incredibly slow. do not use
#signal, rate = torchaudio.load(filename)#, normalization=True)   # Torchaudio's reader is pretty fast but normalization is a problem
#signal = signal.numpy().flatten()
#reader = io_methods.AudioIO   # Stylios' file reader. Haven't gotten it working yet
#signal, rate = reader.audioRead(filename, mono=True)
#signal, rate = sf.read('existing_file.wav') 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def __init__(self, path, sr=44100, ):
        super(FileEffect, self).__init__()
        print("  FileEffect: path = ",path)
        if (path is None) or (not glob.glob(path+"/Train/target*")) \
            or (not glob.glob(path+"/Val/target*")) or ((not glob.glob(path+"/effect_info.ini"))):
            print(f"Error: can't file target output files or effect_info.ini in path = {path}")
            sys.exit(1)   # Yea, this is fatal

        self.sr = sr
        # read the effect info config file  "effect_info.ini"
        config = configparser.ConfigParser()
        config.read(path+'/effect_info.ini')
        self.name = config['effect']['name']+"(files)"   # tack on "(files)" to the effect name
        #TODO: note that use of 'eval' below could be a potential security issue
        self.knob_names = eval(config.get("effect","knob_names"))
        self.knob_ranges = np.array(eval(config.get("effect","knob_ranges")))
        try:
            self.is_inverse = (True == bool(config['effect']['inverse']) )
            self.name = "De-"+self.name
        except:
            pass   # Ignore errors we don't require that 'inverse' be defined anywhere in the file 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def wavread(fn):
    """Emulate the parts of the matlab wavread function that we need.

        y, Fs = wavread(fn)

    y is the vector of audio samples, Fs is the frame rate.

    Matlab's wavread is used by voicesauce to read in the wav files for
    processing.  As a consequence, all the translated algorithms assume the
    data from the wav file is in matlab form, which in this case means a double
    precision float between -1 and 1.  The corresponding scipy function returns
    the actual integer PCM values from the file, which range between -32768 and
    32767.  (matlab's wavread *can* return the integers, but does not by
    default and voicesauce uses the default).  Consequently, after reading the
    data using scipy's io.wavfile, we convert to float by dividing each integer
    by 32768.

    """
    # For reference, I figured this out from:
    # http://mirlab.org/jang/books/audiosignalprocessing/matlab4waveRead.asp?title=4-2%20Reading%20Wave%20Files
    # XXX: if we need to handle 8 bit files we'll need to detect them and
    # special case them here.
    Fs, y = wavfile.read(fn)
    return y/numpy.float64(32768.0), Fs 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def test_read_1():
    for mmap in [False, True]:
        warn_ctx = WarningManager()
        warn_ctx.__enter__()
        try:
            warnings.simplefilter('ignore', wavfile.WavFileWarning)
            rate, data = wavfile.read(datafile('test-44100-le-1ch-4bytes.wav'),
                                      mmap=mmap)
        finally:
            warn_ctx.__exit__()

        assert_equal(rate, 44100)
        assert_(np.issubdtype(data.dtype, np.int32))
        assert_equal(data.shape, (4410,))

        del data 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def fbank(wav_path, flat=True):
    """ Currently grabs log Mel filterbank, deltas and double deltas."""

    (rate, sig) = wav.read(wav_path)
    if len(sig) == 0:
        logger.warning("Empty wav: {}".format(wav_path))
    fbank_feat = python_speech_features.logfbank(sig, rate, nfilt=40)
    energy = extract_energy(rate, sig)
    feat = np.hstack([energy, fbank_feat])
    delta_feat = python_speech_features.delta(feat, 2)
    delta_delta_feat = python_speech_features.delta(delta_feat, 2)
    all_feats = [feat, delta_feat, delta_delta_feat]
    if not flat:
        all_feats = np.array(all_feats)
        # Make time the first dimension for easy length normalization padding
        # later.
        all_feats = np.swapaxes(all_feats, 0, 1)
        all_feats = np.swapaxes(all_feats, 1, 2)
    else:
        all_feats = np.concatenate(all_feats, axis=1)

    # Log Mel Filterbank, with delta, and double delta
    feat_fn = wav_path[:-3] + "fbank.npy"
    np.save(feat_fn, all_feats) 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def detect_MIDI_notes(self):
        """
            The algorithm for calculating midi notes from a given wav file.
        """

        (framerate, sample) = wav.read(self.wav_file)
        # We need to change the 2 channels into one because STFT works only
        # for 1 channel. We could also do STFT for each channel separately.
        monoChannel = sample.mean(axis=1)
        duration = getDuration(self.wav_file)
        midi_notes = []

        # Consider only files with a duration longer than 0.2 seconds.
        if duration > 0.18:
            frequency_power = self.calculateFFT(duration, framerate, monoChannel)
            filtered_frequencies = [f for (f, p) in frequency_power]
            #self.plot_power_spectrum(frequency_power)
            #self.plot_power_spectrum_dB(frequency_power)
            f0_candidates = self.get_pitch_candidates_remove_highest_peak(frequency_power)
            midi_notes = self.matchWithMIDINotes(f0_candidates)
        return midi_notes 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def get_data():
    """The data files come from the TSP dataset 16k set"""
    _, man = wavfile.read('./wav_data/MA02_04.wav')
    rate, woman = wavfile.read('./wav_data/FA01_03.wav')
    man = man.astype('float32')
    woman = woman.astype('float32')
    man_max = np.max(man)
    woman_max = np.max(woman)
    man /= man_max
    woman /= woman_max
    shortest = min(len(man), len(woman))
    woman = woman[:shortest]
    man = man[:shortest]
    np.random.seed(101)
    noise = np.random.uniform(-1, 1, len(man))
    sources = np.stack((woman, man, noise))
    A = np.random.uniform(-1, 1, (3, 3))
    linear_mix = np.dot(A, sources)
    pnl_mix = linear_mix.copy()
    pnl_mix[0] = np.tanh(pnl_mix[0])
    pnl_mix[1] = (pnl_mix[1] + pnl_mix[1]**3) / 2
    pnl_mix[2] = np.exp(pnl_mix[2])
    return linear_mix, pnl_mix, A, sources 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def mainParkinson():
	general_feature_list = []
	general_label_list = []
	folder = raw_input('Give the name of the folder that you want to read data: ')
	if(folder == 'PD'):
		healthyCases = os.listdir('/home/gionanide/Theses_2017-2018_2519/Gkagkos/Audio_Files/PD')
		for x in healthyCases:
			wav = '/'+folder+'/'+str(x)
			mfcc_features,inputWav = mfcc_features_extraction(wav)
			mean_features(mfcc_features,inputWav,folder,general_feature_list,general_label_list)
		folder = raw_input('Give the name of the folder that you want to read data: ')
		if(folder == 'HC'):
			parkinsonCases = os.listdir('/home/gionanide/Theses_2017-2018_2519/Gkagkos/Audio_Files/HC')
			for x in parkinsonCases:
				wav = '/'+folder+'/'+str(x)
				mfcc_features,inputWav = mfcc_features_extraction(wav)
				mean_features(mfcc_features,inputWav,folder,general_feature_list,general_label_list)
		#print general_feature_list, general_label_list
		#writeFeatures(general_feature_list,general_label_list,wav,folder)
		classifyPHC(general_feature_list,general_label_list) 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def wavfile_to_examples(wav_file):
    """Convenience wrapper around waveform_to_examples() for a common WAV format.

    Args:
      wav_file: String path to a file, or a file-like object. The file
      is assumed to contain WAV audio data with signed 16-bit PCM samples.

    Returns:
      See waveform_to_examples.
    """
    try:
        wav_file = BytesIO(wav_file)
        sr, wav_data = wavfile.read(wav_file)
    except IOError:
        print("Error reading WAV file!")
        print("The specified WAV file type is not supported by scipy.io.wavfile.read()")
        sys.exit(1)

    if wav_data.dtype != np.int16:
        raise TypeError('Bad sample type: %r' % wav_data.dtype)
    samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
    return waveform_to_examples(samples, sr) 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def load_wav(input_wav_file):
    # Load the inputs that we're given
    fs, audio = wav.read(input_wav_file)
    assert fs == 16000
    print('source dB', db(audio))
    return audio 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def load(path):
        bps, data = wav.read(path)
        if len(data.shape) != 1:
            data = data[:,0] + data[:,1]
        return bps, data 

# 需要導入模塊: from scipy.io import wavfile [as 別名]
# 或者: from scipy.io.wavfile import read [as 別名]
def load(path):
    bps, data = wav.read(path)
    if len(data.shape) != 1:
        data = data[:,0] + data[:,1]
    return bps, data