def params(cls, config=None):
"""
Set params.
:param config: contains one optional parameters: audio_channels(int, default=1).
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
audio_channels = 1
hparams = HParams(cls=cls)
hparams.add_hparam('type', 'ReadWav')
hparams.add_hparam('audio_channels', audio_channels)
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following two optional parameters
'type': 'ReadWav'.
'audio_channels': index of the desired channel. (default=1)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
audio_channels = 1
hparams = HParams(cls=cls)
hparams.add_hparam('type', 'ReadWav')
hparams.add_hparam('audio_channels', audio_channels)
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains one optional parameters:sample_rate(int, default=16000).
:return: An object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
sample_rate = 16000
hparams = HParams(cls=cls)
hparams.add_hparam('sample_rate', sample_rate)
if config is not None:
hparams.override_from_dict(config)
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following one optional parameter:
'sample_rate': the sample rate of the signal. (default=16000)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
sample_rate = 16000
hparams = HParams(cls=cls)
hparams.add_hparam('sample_rate', sample_rate)
if config is not None:
hparams.override_from_dict(config)
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains nine optional parameters：
--window_length : Window length in seconds. (float, default = 0.025)
--frame_length : Hop length in seconds. (float, default = 0.010)
--snip_edges : If 1, the last frame (shorter than window_length) will be cutoff.
If 2, 1 // 2 frame_length data will be padded to data. (int, default = 1)
---raw_energy : If 1, compute frame energy before preemphasis and windowing.
If 2, compute frame energy after preemphasis and windowing. (int, default = 1)
--preEph_coeff : Coefficient for use in frame-signal preemphasis. (float, default = 0.97)
--window_type : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria"). (string, default = "povey")
--remove_dc_offset : Subtract mean from waveform on each frame (bool, default = true)
--is_fbank : If true, compute power spetrum without frame energy. If false, using
the frame energy instead of the square of the constant component of the signal. (bool, default = false)
--output_type : If 1, return power spectrum. If 2, return log-power spectrum. (int, default = 2)
--dither : Dithering constant (0.0 means no dither) (float, default = 1) [add robust to training]
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
window_length = 0.025
frame_length = 0.010
output_type = 2
snip_edges = 1
raw_energy = 1
preEph_coeff = 0.97
window_type = "povey"
remove_dc_offset = True
is_fbank = False
dither = 0.0
hparams = HParams(cls=cls)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("output_type", output_type)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("raw_energy", raw_energy)
hparams.add_hparam("preEph_coeff", preEph_coeff)
hparams.add_hparam("window_type", window_type)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
hparams.add_hparam("is_fbank", is_fbank)
hparams.add_hparam("dither", dither)
# cmvn
hparams.append(CMVN.params())
if config is not None:
hparams.parse(config, True)
hparams.type = "Spectrum"
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following ten optional parameters:
'window_length': Window length in seconds. (float, default = 0.025),
'frame_length': Hop length in seconds. (float, default = 0.010),
'snip_edges': If 1, the last frame (shorter than window_length) will be
cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1),
'preEph_coeff': Coefficient for use in frame-signal preemphasis.
(float, default = 0.97),
'window_type': Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "povey")
'remove_dc_offset': Subtract mean from waveform on each frame.
(bool, default = true)
'is_fbank': If true, compute power spetrum without frame energy.
If false, using the frame energy instead of the
square of the constant component of the signal.
(bool, default = false)
'output_type': If 1, return power spectrum. If 2, return log-power
spectrum. If 3, return magnitude spectrum. (int, default = 2)
'upper_frequency_limit': High cutoff frequency for mel bins (if <= 0, offset
from Nyquist) (float, default = 0)
'dither': Dithering constant (0.0 means no dither).
(float, default = 1) [add robust to training]
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
window_length = 0.025
frame_length = 0.010
output_type = 2
snip_edges = 1
raw_energy = 1
preEph_coeff = 0.97
window_type = "povey"
remove_dc_offset = True
is_fbank = False
dither = 0.0
hparams = HParams(cls=cls)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("output_type", output_type)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("raw_energy", raw_energy)
hparams.add_hparam("preEph_coeff", preEph_coeff)
hparams.add_hparam("window_type", window_type)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
hparams.add_hparam("is_fbank", is_fbank)
hparams.add_hparam("dither", dither)
# cmvn
hparams.append(CMVN.params())
if config is not None:
hparams.parse(config, True)
hparams.type = "Spectrum"
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains twenty-nine optional parameters:t
--window_length : Window length in seconds. (float, default = 0.025)
--frame_length : Hop length in seconds. (float, default = 0.010)
--snip_edges : If 1, the last frame (shorter than window_length) will
be cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1)
---raw_energy : If 1, compute frame energy before preemphasis and
windowing. If 2, compute frame energy after preemphasis
and windowing. (int, default = 1)
--preEph_coeff : Coefficient for use in frame-signal preemphasis.
(float, default = 0.97)
--window_type : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "povey")
--remove_dc_offset : Subtract mean from waveform on each frame.
(bool, default = true)
--is_fbank : If true, compute power spetrum without frame
energy. If false, using the frame energy instead
of the square of the constant component of the
signal. (bool, default = true)
--output_type : If 1, return power spectrum. If 2, return
log-power spectrum. (int, default = 1)
--upper_frequency_limit : High cutoff frequency for mel bins.
(if <= 0, offset from Nyquist) (float, default = 0)
--lower_frequency_limit : Low cutoff frequency for mel bins.
(float, default = 20)
--filterbank_channel_count : Number of triangular mel-frequency bins.
(float, default = 23)
--dither : Dithering constant (0.0 means no dither).
(float, default = 1)
[add robust to training]
--delta-pitch : Smallest relative change in pitch that our
algorithm measures. (float, default = 0.005)
--frames-per-chunk : Only relevant for offline pitch extraction.
(e.g. compute-kaldi-pitch-feats), you can set it to a
small nonzero value, such as 10, for better feature
compatibility with online decoding (affects energy
normalization in the algorithm) (int, default = 0)
--lowpass-cutoff : cutoff frequency for LowPass filter (Hz).
(float, default = 1000)
--lowpass-filter-width : Integer that determines filter width of lowpass filter,
more gives sharper filter (int, default = 1)
--max-f0 : max. F0 to search for (Hz) (float, default = 400)
--max-frames-latency : Maximum number of frames of latency that we allow pitch
tracking to introduce into the feature processing
(affects output only if --frames-per-chunk > 0 and
--simulate-first-pass-online=true (int, default = 0)
--min-f0 : min. F0 to search for (Hz) (float, default = 50)
--nccf-ballast : Increasing this factor reduces NCCF for quiet frames.
(float, default = 7000)
--nccf-ballast-online : This is useful mainly for debug; it affects how the
NCCF ballast is computed. (bool, default = false)
--penalty-factor : cost factor for FO change. (float, default = 0.1)
--preemphasis-coefficient : Coefficient for use in signal preemphasis (deprecated)
(float, default = 0)
--recompute-frame : Only relevant for online pitch extraction, or for
compatibility with online pitch extraction. A
non-critical parameter; the frame at which we recompute
some of the forward pointers, after revising our
estimate of the signal energy. Relevant
if--frames-per-chunk > 0. (int, default = 500)
--resample-frequency : Frequency that we down-sample the signal to. Must be
more than twice lowpass-cutoff (float, default = 4000)
--simulate-first-pass-online : If true, compute-kaldi-pitch-feats will output features
that correspond to what an online decoder would see in
the first pass of decoding-- not the final version of
the features, which is the default. Relevant if
--frames-per-chunk > 0 (bool, default = false)
--soft-min-f0 : Minimum f0, applied in soft way, must not exceed
min-f0 (float, default = 10)
--upsample-filter-width : Integer that determines filter width when upsampling
NCCF (int, default = 5)
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
hparams = HParams(cls=cls)
hparams.append(CMVN.params())
upper_frequency_limit = 0
lower_frequency_limit = 20.0
filterbank_channel_count = 80.0
window_length = 0.025
frame_length = 0.010
raw_energy = 1
preEph_coeff = 0.97
window_type = 'povey'
remove_dc_offset = True
is_fbank = True
output_type = 1
dither = 0.0
snip_edges = True
preemph_coeff = 0.0
min_f0 = 50.0
max_f0 = 400.0
soft_min_f0 = 10.0
penalty_factor = 0.1
lowpass_cutoff = 1000.0
resample_freq = 4000.0
delta_pitch = 0.005
nccf_ballast = 7000.0
lowpass_filter_width = 1
upsample_filter_width = 5
max_frames_latency = 0
frames_per_chunk = 0
simulate_first_pass_online = False
recompute_frame = 500
nccf_ballast_online = False
# delta
delta_delta = False # True
order = 2
window = 2
hparams.add_hparam('delta_delta', delta_delta)
hparams.add_hparam('order', order)
hparams.add_hparam('window', window)
hparams.add_hparam('channel', 1)
if hparams.delta_delta:
hparams.channel = hparams.order + 1
hparams.add_hparam('snip_edges', snip_edges)
hparams.add_hparam('preemph_coeff', preemph_coeff)
hparams.add_hparam('min_f0', min_f0)
hparams.add_hparam('max_f0', max_f0)
hparams.add_hparam('dither', dither)
hparams.add_hparam('soft_min_f0', soft_min_f0)
hparams.add_hparam('penalty_factor', penalty_factor)
hparams.add_hparam('lowpass_cutoff', lowpass_cutoff)
hparams.add_hparam('resample_freq', resample_freq)
hparams.add_hparam('delta_pitch', delta_pitch)
hparams.add_hparam('nccf_ballast', nccf_ballast)
hparams.add_hparam('lowpass_filter_width', lowpass_filter_width)
hparams.add_hparam('upsample_filter_width', upsample_filter_width)
hparams.add_hparam('max_frames_latency', max_frames_latency)
hparams.add_hparam('frames_per_chunk', frames_per_chunk)
hparams.add_hparam('simulate_first_pass_online',
simulate_first_pass_online)
hparams.add_hparam('recompute_frame', recompute_frame)
hparams.add_hparam('nccf_ballast_online', nccf_ballast_online)
hparams.add_hparam('upper_frequency_limit', upper_frequency_limit)
hparams.add_hparam('lower_frequency_limit', lower_frequency_limit)
hparams.add_hparam('filterbank_channel_count',
filterbank_channel_count)
hparams.add_hparam('window_length', window_length)
hparams.add_hparam('frame_length', frame_length)
hparams.add_hparam('output_type', output_type)
hparams.add_hparam('raw_energy', raw_energy)
hparams.add_hparam('preEph_coeff', preEph_coeff)
hparams.add_hparam('window_type', window_type)
hparams.add_hparam('remove_dc_offset', remove_dc_offset)
hparams.add_hparam('is_fbank', is_fbank)
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains thirteen optional parameters:
--window_length : Window length in seconds. (float, default = 0.025)
--frame_length : Hop length in seconds. (float, default = 0.010)
--snip_edges : If True, the last frame (shorter than window_length) will be
cutoff. If False, 1 // 2 frame_length data will be padded
to data. (bool, default = True)
---raw_energy : If 1, compute frame energy before preemphasis and
windowing. If 2, compute frame energy after
preemphasis and windowing. (int, default = 1)
--preEph_coeff : Coefficient for use in frame-signal preemphasis.
(float, default = 0.0)
--window_type : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "hann")
--remove_dc_offset : Subtract mean from waveform on each frame.
(bool, default = false)
--is_fbank : If true, compute power spetrum without frame energy.
If false, using the frame energy instead of the
square of the constant component of the signal.
(bool, default = true)
--output_type : If 1, return power spectrum. If 2, return log-power
spectrum. If 3, return magnitude spectrum. (int, default = 3)
--upper_frequency_limit : High cutoff frequency for mel bins (if <= 0, offset
from Nyquist) (float, default = 0)
--lower_frequency_limit : Low cutoff frequency for mel bins (float, default = 20)
--filterbank_channel_count : Number of triangular mel-frequency bins.
(float, default = 23)
--dither : Dithering constant (0.0 means no dither).
(float, default = 0) [add robust to training]
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
hparams = HParams(cls=cls)
# spectrum
hparams.append(
Spectrum.params({
'output_type': 3,
'is_fbank': True,
'preEph_coeff': 0.0,
'window_type': 'hann',
'dither': 0.0,
'remove_dc_offset': False
}))
# mel_spectrum
upper_frequency_limit = 0
lower_frequency_limit = 60
filterbank_channel_count = 40
sample_rate = -1
hparams.add_hparam('upper_frequency_limit', upper_frequency_limit)
hparams.add_hparam('lower_frequency_limit', lower_frequency_limit)
hparams.add_hparam('filterbank_channel_count',
filterbank_channel_count)
hparams.add_hparam('sample_rate', sample_rate)
# delta
delta_delta = False # True
order = 2
window = 2
hparams.add_hparam('delta_delta', delta_delta)
hparams.add_hparam('order', order)
hparams.add_hparam('window', window)
if config is not None:
hparams.parse(config, True)
hparams.type = 'MelSpectrum'
hparams.add_hparam('channel', 1)
if hparams.delta_delta:
hparams.channel = hparams.order + 1
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following fifteen optional parameters:
'window_length': Window length in seconds. (float, default = 0.025),
'frame_length': Hop length in seconds. (float, default = 0.010),
'snip_edges': If 1, the last frame (shorter than window_length) will be
cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1),
'preEph_coeff': Coefficient for use in frame-signal preemphasis.
(float, default = 0.97),
'window_type': Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "povey")
'remove_dc_offset': Subtract mean from waveform on each frame.
(bool, default = true)
'is_fbank': If true, compute power spetrum without frame energy.
If false, using the frame energy instead of the
square of the constant component of the signal.
(bool, default = true)
'coefficient_count': Number of cepstra in MFCC computation. (int, default = 13)
'output_type': If 1, return power spectrum. If 2, return log-power
spectrum. (int, default = 1)
'upper_frequency_limit': High cutoff frequency for mel bins (if <= 0, offset
from Nyquist) (float, default = 0)
'lower_frequency_limit': Low cutoff frequency for mel bins. (float, default = 20)
'filterbank_channel_count': Number of triangular mel-frequency bins.
(float, default = 23)
'dither': Dithering constant (0.0 means no dither).
(float, default = 1) [add robust to training]
'cepstral_lifter': Constant that controls scaling of MFCCs. (float, default = 22)
'use_energy': Use energy (not C0) in MFCC computation. (bool, default = True)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
upper_frequency_limit = 0.0
lower_frequency_limit = 20.0
filterbank_channel_count = 23.0
window_length = 0.025
frame_length = 0.010
output_type = 1
snip_edges = 1
raw_energy = 1
preEph_coeff = 0.97
window_type = "povey"
remove_dc_offset = True
is_fbank = True
cepstral_lifter = 22.0
coefficient_count = 13
use_energy = True
dither = 0.0
delta_delta = False
order = 2
window = 2
hparams = HParams(cls=cls)
hparams.add_hparam("upper_frequency_limit", upper_frequency_limit)
hparams.add_hparam("lower_frequency_limit", lower_frequency_limit)
hparams.add_hparam("filterbank_channel_count",
filterbank_channel_count)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("output_type", output_type)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("raw_energy", raw_energy)
hparams.add_hparam("preEph_coeff", preEph_coeff)
hparams.add_hparam("window_type", window_type)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
hparams.add_hparam("is_fbank", is_fbank)
hparams.add_hparam("cepstral_lifter", cepstral_lifter)
hparams.add_hparam("coefficient_count", coefficient_count)
hparams.add_hparam("use_energy", use_energy)
hparams.add_hparam("dither", dither)
hparams.add_hparam("delta_delta", delta_delta)
hparams.add_hparam("order", order)
hparams.add_hparam("window", window)
hparams.add_hparam("channel", 1)
hparams.append(CMVN.params())
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains thirteen optional parameters:upper_frequency_limit(float, default=0),
lower_frequency_limit(float, default=60.0), filterbank_channel_count(float, default=40.0),
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
hparams = HParams(cls=cls)
# spectrum
hparams.append(Spectrum.params({"output_type": 1, "is_fbank": True}))
# fbank
upper_frequency_limit = 0
lower_frequency_limit = 60
filterbank_channel_count = 40
hparams.add_hparam("upper_frequency_limit", upper_frequency_limit)
hparams.add_hparam("lower_frequency_limit", lower_frequency_limit)
hparams.add_hparam("filterbank_channel_count",
filterbank_channel_count)
# delta
delta_delta = False # True
order = 2
window = 2
hparams.add_hparam("delta_delta", delta_delta)
hparams.add_hparam("order", order)
hparams.add_hparam("window", window)
if config is not None:
hparams.parse(config, True)
hparams.type = "Fbank"
hparams.add_hparam("channel", 1)
if hparams.delta_delta:
hparams.channel = hparams.order + 1
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains fourteen optional parameters.
--window_length : Window length in seconds. (float, default = 0.025)
--frame_length : Hop length in seconds. (float, default = 0.010)
--snip_edges : If 1, the last frame (shorter than window_length) will
be cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1)
---raw_energy : If 1, compute frame energy before preemphasis and
windowing. If 2, compute frame energy after
preemphasis and windowing. (int, default = 1)
--preEph_coeff : Coefficient for use in frame-signal preemphasis.
(float, default = 0.97)
--window_type : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "povey")
--remove_dc_offset : Subtract mean from waveform on each frame
(bool, default = true)
--is_fbank : If true, compute power spetrum without frame energy. If
false, using the frame energy instead of the square of the
constant component of the signal. (bool, default = true)
--output_type : If 1, return power spectrum. If 2, return log-power
spectrum. (int, default = 1)
--upper_frequency_limit : High cutoff frequency for mel bins (if < 0, offset from
Nyquist) (float, default = 0)
--lower_frequency_limit : Low cutoff frequency for mel bins (float, default = 20)
--filterbank_channel_count : Number of triangular mel-frequency bins.
(float, default = 23)
--coefficient_count : Number of cepstra in MFCC computation.
(int, default = 13)
--cepstral_lifter : Constant that controls scaling of MFCCs.
(float, default = 22)
--use_energy :Use energy (not C0) in MFCC computation.
(bool, default = True)
:return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
upper_frequency_limit = 0.0
lower_frequency_limit = 20.0
filterbank_channel_count = 23.0
window_length = 0.025
frame_length = 0.010
output_type = 1
snip_edges = 1
raw_energy = 1
preEph_coeff = 0.97
window_type = "povey"
remove_dc_offset = True
is_fbank = True
cepstral_lifter = 22.0
coefficient_count = 13
use_energy = True
dither = 0.0
delta_delta = False
order = 2
window = 2
hparams = HParams(cls=cls)
hparams.add_hparam("upper_frequency_limit", upper_frequency_limit)
hparams.add_hparam("lower_frequency_limit", lower_frequency_limit)
hparams.add_hparam("filterbank_channel_count",
filterbank_channel_count)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("output_type", output_type)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("raw_energy", raw_energy)
hparams.add_hparam("preEph_coeff", preEph_coeff)
hparams.add_hparam("window_type", window_type)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
hparams.add_hparam("is_fbank", is_fbank)
hparams.add_hparam("cepstral_lifter", cepstral_lifter)
hparams.add_hparam("coefficient_count", coefficient_count)
hparams.add_hparam("use_energy", use_energy)
hparams.add_hparam("dither", dither)
hparams.add_hparam("delta_delta", delta_delta)
hparams.add_hparam("order", order)
hparams.add_hparam("window", window)
hparams.add_hparam("channel", 1)
hparams.append(CMVN.params())
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
""" Set params.
Args:
config: contains the following four optional parameters:
'type': Type of Opration. (string, default = 'CMVN')
'global_mean': Global mean of features. (float, default = 0.0)
'global_variance': Global variance of features. (float, default = 1.0)
'local_cmvn': If ture, local cmvn will be done on features. (bool, default = False)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
hparams = HParams(cls=cls)
hparams.add_hparam("type", "CMVN")
hparams.add_hparam("global_mean", [0.0])
hparams.add_hparam("global_variance", [1.0])
hparams.add_hparam("local_cmvn", False)
if config is not None:
hparams.parse(config, True)
assert len(hparams.global_mean) == len(
hparams.global_variance
), "Error, global_mean length {} is not equals to global_variance length {}".format(
len(hparams.global_mean), len(hparams.global_variance)
)
hparams.global_variance = (np.sqrt(hparams.global_variance) + 1e-6).tolist()
return hparams
def params(cls, config=None):
""" set params """
hparams = HParams(cls=cls)
hparams.add_hparam("type", "CMVN")
hparams.add_hparam("global_mean", [0.0])
hparams.add_hparam("global_variance", [1.0])
hparams.add_hparam("local_cmvn", False)
if config is not None:
hparams.parse(config, True)
assert len(hparams.global_mean) == len(
hparams.global_variance
), "Error, global_mean length {} is not equals to global_variance length {}".format(
len(hparams.global_mean), len(hparams.global_variance))
hparams.global_variance = (np.sqrt(hparams.global_variance) +
1e-6).tolist()
return hparams
def params(cls, config=None):
"""
Set params.
:param config: contains four optional parameters:
--window_length : Window length in seconds. (float, default = 0.025)
--frame_length : Hop length in seconds. (float, default = 0.010)
--snip_edges : If True, the last frame (shorter than window_length)
will be cutoff. If False, 1 // 2 frame_length data will
be padded to data. (int, default = True)
--remove_dc_offset : Subtract mean from waveform on each frame (bool, default = true)
:return:An object of class HParams, which is a set of hyperparameters as name-value pairs.
"""
window_length = 0.025
frame_length = 0.010
snip_edges = 1
remove_dc_offset = True
hparams = HParams(cls=cls)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following thirteen optional parameters:
'window_length': Window length in seconds. (float, default = 0.025)
'frame_length': Hop length in seconds. (float, default = 0.010)
'snip_edges': If 1, the last frame (shorter than window_length) will be
cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1)
'preEph_coeff': Coefficient for use in frame-signal preemphasis.
(float, default = 0.97)
'window_type': Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
(string, default = "povey")
'remove_dc_offset': Subtract mean from waveform on each frame.
(bool, default = true)
'is_fbank': If true, compute power spetrum without frame energy.
If false, using the frame energy instead of the
square of the constant component of the signal.
(bool, default = true)
'is_log10': If true, using log10 to fbank. If false, using loge.
(bool, default = false)
'output_type': If 1, return power spectrum. If 2, return log-power
spectrum. (int, default = 1)
'upper_frequency_limit': High cutoff frequency for mel bins (if <= 0, offset
from Nyquist) (float, default = 0)
'lower_frequency_limit': Low cutoff frequency for mel bins (float, default = 20)
'filterbank_channel_count': Number of triangular mel-frequency bins.
(float, default = 23)
'dither': Dithering constant (0.0 means no dither).
(float, default = 1) [add robust to training]
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
hparams = HParams(cls=cls)
# spectrum
hparams.append(Spectrum.params({"output_type": 1, "is_fbank": True}))
# fbank
upper_frequency_limit = 0
lower_frequency_limit = 60
filterbank_channel_count = 40
is_log10 = False
hparams.add_hparam("upper_frequency_limit", upper_frequency_limit)
hparams.add_hparam("lower_frequency_limit", lower_frequency_limit)
hparams.add_hparam("filterbank_channel_count",
filterbank_channel_count)
hparams.add_hparam('is_log10', is_log10)
# delta
delta_delta = False # True
order = 2
window = 2
hparams.add_hparam("delta_delta", delta_delta)
hparams.add_hparam("order", order)
hparams.add_hparam("window", window)
if config is not None:
hparams.parse(config, True)
hparams.type = "Fbank"
hparams.add_hparam("channel", 1)
if hparams.delta_delta:
hparams.channel = hparams.order + 1
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following nineteen optional parameters:
'delta_pitch': Smallest relative change in pitch that our algorithm
measures (float, default = 0.005)
'window_length': Frame length in seconds (float, default = 0.025)
'frame_length': Frame shift in seconds (float, default = 0.010)
'frames-per-chunk': Only relevant for offline pitch extraction (e.g.
compute-kaldi-pitch-feats), you can set it to a small
nonzero value, such as 10, for better feature
compatibility with online decoding (affects energy
normalization in the algorithm) (int, default = 0)
'lowpass-cutoff': cutoff frequency for LowPass filter (Hz). (float, default = 1000)
'lowpass-filter-width': Integer that determines filter width of lowpass filter,
more gives sharper filter (int, default = 1)
'max-f0': max. F0 to search for (Hz) (float, default = 400)
'max-frames-latency': Maximum number of frames of latency that we allow pitch
tracking to introduce into the feature processing
(affects output only if --frames-per-chunk > 0 and
--simulate-first-pass-online=true (int, default = 0)
'min-f0': min. F0 to search for (Hz) (float, default = 50)
'nccf-ballast': Increasing this factor reduces NCCF for quiet frames.
(float, default = 7000)
'nccf-ballast-online': This is useful mainly for debug; it affects how the NCCF
ballast is computed. (bool, default = false)
'penalty-factor': cost factor for FO change. (float, default = 0.1)
'preemphasis-coefficient': Coefficient for use in signal preemphasis (deprecated).
(float, default = 0)
'recompute-frame': Only relevant for online pitch extraction, or for
compatibility with online pitch extraction. A
non-critical parameter; the frame at which we recompute
some of the forward pointers, after revising our
estimate of the signal energy. Relevant
if--frames-per-chunk > 0. (int, default = 500)
'resample-frequency': Frequency that we down-sample the signal to. Must be
more than twice lowpass-cutoff (float, default = 4000)
'simulate-first-pass-online': If true, compute-kaldi-pitch-feats will output features
that correspond to what an online decoder would see in
the first pass of decoding-- not the final version of
the features, which is the default. Relevant if
--frames-per-chunk > 0 (bool, default = false)
'snip-edges': If this is set to false, the incomplete frames near the
ending edge won't be snipped, so that the number of
frames is the file size divided by the frame-shift.
This makes different types of features give the same
number of frames. (bool, default = true)
'soft-min-f0': Minimum f0, applied in soft way, must not exceed min-f0.
(float, default = 10)
'upsample-filter-width': Integer that determines filter width when upsampling
NCCF. (int, default = 5)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
hparams = HParams(cls=cls)
window_length = 0.025
frame_length = 0.010
sample_rate = 16000
snip_edges = True
preemph_coeff = 0.0
min_f0 = 50.0
max_f0 = 400.0
soft_min_f0 = 10.0
penalty_factor = 0.1
lowpass_cutoff = 1000.0
resample_freq = 4000.0
delta_pitch = 0.005
nccf_ballast = 7000.0
lowpass_filter_width = 1
upsample_filter_width = 5
max_frames_latency = 0
frames_per_chunk = 0
simulate_first_pass_online = False
recompute_frame = 500
nccf_ballast_online = False
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("sample_rate", sample_rate)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("preemph_coeff", preemph_coeff)
hparams.add_hparam("min_f0", min_f0)
hparams.add_hparam("max_f0", max_f0)
hparams.add_hparam("soft_min_f0", soft_min_f0)
hparams.add_hparam("penalty_factor", penalty_factor)
hparams.add_hparam("lowpass_cutoff", lowpass_cutoff)
hparams.add_hparam("resample_freq", resample_freq)
hparams.add_hparam("delta_pitch", delta_pitch)
hparams.add_hparam("nccf_ballast", nccf_ballast)
hparams.add_hparam("lowpass_filter_width", lowpass_filter_width)
hparams.add_hparam("upsample_filter_width", upsample_filter_width)
hparams.add_hparam("max_frames_latency", max_frames_latency)
hparams.add_hparam("frames_per_chunk", frames_per_chunk)
hparams.add_hparam("simulate_first_pass_online",
simulate_first_pass_online)
hparams.add_hparam("recompute_frame", recompute_frame)
hparams.add_hparam("nccf_ballast_online", nccf_ballast_online)
if config is not None:
hparams.parse(config, True)
return hparams
def params(cls, config=None):
"""Set params.
Args:
config: contains the following four optional parameters:
'window_length': Window length in seconds. (float, default = 0.025)
'frame_length': Hop length in seconds. (float, default = 0.010)
'snip_edges': If 1, the last frame (shorter than window_length) will be
cutoff. If 2, 1 // 2 frame_length data will be padded
to data. (int, default = 1)
'remove_dc_offset': Subtract mean from waveform on each frame.
(bool, default = true)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
window_length = 0.025
frame_length = 0.010
snip_edges = 1
remove_dc_offset = True
hparams = HParams(cls=cls)
hparams.add_hparam("window_length", window_length)
hparams.add_hparam("frame_length", frame_length)
hparams.add_hparam("snip_edges", snip_edges)
hparams.add_hparam("remove_dc_offset", remove_dc_offset)
if config is not None:
hparams.parse(config, True)
return hparams