def get_mel_scale(nfilt=20, samplerate=16000, lowfreq=20, highfreq=8000):
highfreq = highfreq or samplerate / 2
assert highfreq <= samplerate / 2, "highfreq is greater than samplerate/2"
# compute points evenly spaced in mels
lowmel = hz2mel(lowfreq)
highmel = hz2mel(highfreq)
melpoints = np.linspace(lowmel, highmel, nfilt + 2)
return melpoints
def __init__(self, input_dim, sr, num_filter, exp=False, filter_fix=False):
super(fBPLayer, self).__init__()
self.input_dim = input_dim
self.num_filter = num_filter
self.sr = sr
self.exp = exp
self.filter_fix = filter_fix
requires_grad = not filter_fix
input_freq = np.linspace(0, self.sr / 2, input_dim)
self.input_freq = nn.Parameter(torch.from_numpy(input_freq).expand(num_filter, input_dim).float(),
requires_grad=False)
borders = np.linspace(0, hz2mel(sr / 2), num_filter + 2)
borders = mel2hz(borders)
self.bandwidth_low = nn.Parameter(torch.from_numpy(borders[:-2]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
self.bandwidth = nn.Parameter(torch.from_numpy(borders[2:] - borders[:-2]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
def __init__(self, input_dim, sr, num_filter, exp=False, filter_fix=False):
super(fBLayer, self).__init__()
self.input_dim = input_dim
self.num_filter = num_filter
self.sr = sr
self.exp = exp
self.filter_fix = filter_fix
requires_grad = not filter_fix
input_freq = np.linspace(0, self.sr / 2, input_dim)
self.input_freq = nn.Parameter(torch.from_numpy(input_freq).expand(num_filter, input_dim).float(),
requires_grad=False)
centers = np.linspace(0, hz2mel(sr / 2), num_filter + 2)
centers = mel2hz(centers)
bandwidth = np.diff(centers)
self.frequency_center = nn.Parameter(torch.from_numpy(centers[1:-1]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
self.bandwidth_left = nn.Parameter(torch.from_numpy(bandwidth[:-1]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
self.bandwidth_right = nn.Parameter(torch.from_numpy(bandwidth[1:]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
def read_wav(wav_path, feature_type='logmelfbank', batch_size=1):
"""Read wav file & convert to MFCC or log mel filterbank features.
Args:
wav_path: path to a wav file
feature: logmelfbank or mfcc
Returns:
inputs: `[batch_size, max_time, feature_dim]`
inputs_seq_len: `[batch_size, frame_num]`
"""
# Load wav file
fs, audio = scipy.io.wavfile.read(wav_path)
if feature_type == 'mfcc':
features = mfcc(audio, samplerate=fs) # `[291, 13]`
elif feature_type == 'logmelfbank':
fbank_features, energy = fbank(audio, nfilt=40)
logfbank = np.log(fbank_features)
logenergy = np.log(energy)
logmelfbank = hz2mel(logfbank)
features = np.c_[logmelfbank, logenergy] # `[291, 41]`
delta1 = delta(features, N=2)
delta2 = delta(delta1, N=2)
input_data = np.c_[features, delta1, delta2] # `[291, 123]`
# Transform to 3D array
# `[1, 291, 39]` or `[1, 291, 123]`
inputs = np.zeros((batch_size, input_data.shape[0], input_data.shape[1]))
for i in range(batch_size):
inputs[i] = input_data
inputs_seq_len = [inputs.shape[1]] * batch_size # `[291]`
# Normalization
inputs = (inputs - np.mean(inputs)) / np.std(inputs)
return inputs, inputs_seq_len
def get_filterbanks(nfilt=20,
nfft=512,
samplerate=16000,
lowfreq=0,
highfreq=None,
filtertype='mel',
multi_weight=False):
"""Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond
to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param samplerate: the samplerate of the signal we are working with. Affects mel spacing.
:param lowfreq: lowest band edge of mel filters, default 0 Hz
:param highfreq: highest band edge of mel filters, default samplerate/2
:returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.
"""
highfreq = highfreq or samplerate / 2
assert highfreq <= samplerate / 2, "highfreq is greater than samplerate/2"
if filtertype == 'mel':
# compute points evenly spaced in mels
lowmel = hz2mel(lowfreq)
highmel = hz2mel(highfreq)
melpoints = np.linspace(lowmel, highmel, nfilt + 2)
# our points are in Hz, but we use fft bins, so we have to convert from Hz to fft bin number
bin = np.floor((nfft + 1) * mel2hz(melpoints) / samplerate)
elif filtertype == 'amel':
# compute points evenly spaced in mels
lowmel = hz2amel(lowfreq)
highmel = hz2amel(highfreq)
melpoints = np.linspace(lowmel, highmel, nfilt + 2)
# our points are in Hz, but we use fft bins, so we have to convert from Hz to fft bin number
bin = np.floor((nfft + 1) * amel2hz(melpoints) / samplerate)
elif filtertype == 'linear':
linearpoints = np.linspace(lowfreq, highfreq, nfilt + 2)
# our points are in Hz, but we use fft bins, so we have to convert from Hz to fft bin number
bin = np.floor((nfft + 1) * linearpoints / samplerate)
elif filtertype.startswith('dnn'):
x = np.arange(0, 161) * samplerate / 2 / 160
if filtertype.endswith('timit.fix'):
y = np.array(c.TIMIT_FIlTER_FIX)
elif filtertype.endswith('timit.var'):
y = np.array(c.TIMIT_FIlTER_VAR)
elif filtertype.endswith('timit.mdv'):
y = np.array(c.TIMIT_FIlTER_MDV)
elif filtertype.endswith('libri.fix'):
y = np.array(c.LIBRI_FILTER_FIX)
elif filtertype.endswith('libri.var'):
y = np.array(c.LIBRI_FILTER_VAR)
elif filtertype.endswith('vox1.soft'):
y = np.array(c.VOX_FILTER_SOFT)
elif filtertype == 'dnn.vox1':
y = np.array(c.VOX_FILTER)
f = interpolate.interp1d(x, y)
x_new = np.arange(nfft // 2 + 1) * samplerate / 2 / (nfft // 2)
lowfreq_idx = np.where(x_new >= lowfreq)[0]
highfreq_idx = np.where(x_new <= highfreq)[0]
ynew = f(x_new) # 计算插值结果
ynew[:int(lowfreq_idx[0])] = 0
if highfreq_idx[-1] < len(x_new) - 1:
ynew[int(highfreq[-1] + 1):] = 0
weight = ynew / np.sum(ynew)
bin = []
bin.append(lowfreq_idx[0])
for j in range(nfilt):
num_wei = 0.
for i in range(nfft // 2 + 1):
num_wei += weight[i]
if num_wei > (j + 1) / (nfilt + 1):
bin.append(i - 1)
break
else:
continue
bin.append(highfreq_idx[-1])
fbank = np.zeros([nfilt, nfft // 2 + 1])
for j in range(0, nfilt):
for i in range(int(bin[j]), int(bin[j + 1])):
fbank[j, i] = (i - bin[j]) / (bin[j + 1] - bin[j])
for i in range(int(bin[j + 1]), int(bin[j + 2])):
fbank[j, i] = (bin[j + 2] - i) / (bin[j + 2] - bin[j + 1])
if multi_weight:
y = np.array(c.TIMIT_FIlTER_VAR)
fbank = fbank * (y / y.max())
return fbank
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from python_speech_features import get_filterbanks, hz2mel
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
nfilt, nfft, samplerate, lowfreq, highfreq = 7, 512, 16000, 0, 8000
fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
colors = sns.cubehelix_palette(7, start=2, rot=0, dark=0.1, light=.7)
x = np.arange(0, 8001, 1)
y = [hz2mel(i) for i in x]
ax1.scatter(1000, 1000, s=30, color='red', alpha=0.9)
ax1.vlines(1000,
ymin=0,
ymax=1000,
alpha=0.8,
color='red',
linestyle='--',
linewidth=1)
ax1.hlines(1000,
xmin=0,
xmax=1000,
alpha=0.8,
color='red',
linestyle='--',
linewidth=1)
csf_feat = csf.logfbank(audio) assert (np.shape(psf_feat) == np.shape(csf_feat)) error2d(psf_feat, csf_feat) print '' print 'ssc' print '===' psf_ssc = psf.ssc(audio) csf_ssc = csf.ssc(audio) assert (np.shape(psf_ssc) == np.shape(csf_ssc)) error2d(psf_ssc, csf_ssc) print '' print 'hz2mel' print '======' assert (get_error(psf.hz2mel(8000), csf.hz2mel(8000)) <= acceptable_error) assert (get_error(psf.hz2mel(16000), csf.hz2mel(16000)) <= acceptable_error) assert (get_error(csf.mel2hz(csf.hz2mel(8000)), 8000) <= acceptable_error) print ' ✓' print '' print 'mel2hz' print '======' assert (get_error(psf.mel2hz(2595), csf.mel2hz(2595)) <= acceptable_error) assert (get_error(csf.mel2hz(5190), csf.mel2hz(5190)) <= acceptable_error) assert (get_error(csf.hz2mel(csf.mel2hz(2595)), 2595) <= acceptable_error) print ' ✓' print '' print 'get_filterbanks' print '==============='
def getmelpoint(_n_filt=N_FILT):
lowmel = hz2mel(0)
highmel = hz2mel(SAMPLING_RATE / 2)
melpoints = np.linspace(lowmel, highmel, _n_filt + 1)
return mel2hz(melpoints)[1:_n_filt + 1]
self.input_dim = input_dim
self.num_filter = num_filter
self.sr = sr
<<<<<<< HEAD
=======
self.exp = exp
self.filter_fix = filter_fix
requires_grad = not filter_fix
>>>>>>> Server/Server
input_freq = np.linspace(0, self.sr / 2, input_dim)
self.input_freq = nn.Parameter(torch.from_numpy(input_freq).expand(num_filter, input_dim).float(),
requires_grad=False)
centers = np.linspace(0, hz2mel(sr / 2), num_filter + 2)
centers = mel2hz(centers)
<<<<<<< HEAD
self.frequency_center = nn.Parameter(torch.from_numpy(centers[1:-1]).float().reshape(num_filter, 1))
=======
self.frequency_center = nn.Parameter(torch.from_numpy(centers[1:-1]).float().reshape(num_filter, 1),
requires_grad=requires_grad)
>>>>>>> Server/Server
bandwidth = []
for i in range(2, len(centers)):
bandwidth.append(centers[i] - centers[i - 1])
<<<<<<< HEAD
self.bandwidth = nn.Parameter(torch.tensor(bandwidth).reshape(num_filter, 1).float())
self.gain = nn.Parameter(torch.ones(num_filter, dtype=torch.float32).reshape(num_filter, 1))
