def refresh_cache(self):
if self.mode == "long":
dur = self.cached_end - self.cached_begin
self.signal, self.sr = librosa.load(self.path, sr=None, offset=self.cached_begin, duration=dur, mono=False)
if len(self.signal.shape) == 1:
self.signal = self.signal.reshape((self.signal.shape[0], 1))
else:
self.signal = self.signal.T
self.preemph_signal = lfilter([1.0, -0.95], 1, self.signal, axis=0)
self.downsampled_1000 = resample(self.signal, self.sr, 1000, filter="kaiser_fast", axis=0)
self.downsampled_100 = resample(self.downsampled_1000, 1000, 100, filter="kaiser_fast", axis=0)
def augment_audio_signal(signal, fs, augmentation):
"""Function that performs audio signal augmentation.
Args:
signal (np.array): np.array containing raw audio signal.
fs (float): frames per second.
augmentation (dict): dictionary of augmentation parameters. See
:func:`get_speech_features_from_file` for specification and example.
Returns:
np.array: np.array with augmented audio signal.
"""
signal_float = signal.astype(np.float32) / 32768.0
if augmentation['time_stretch_ratio'] > 0:
# time stretch (might be slow)
stretch_amount = 1.0 + (2.0 * np.random.rand() - 1.0) * \
augmentation['time_stretch_ratio']
signal_float = rs.resample(
signal_float,
fs,
int(fs * stretch_amount),
filter='kaiser_fast',
)
# noise
noise_level_db = np.random.randint(low=augmentation['noise_level_min'],
high=augmentation['noise_level_max'])
signal_float += np.random.randn(signal_float.shape[0]) * \
10.0 ** (noise_level_db / 20.0)
return (signal_float * 32768.0).astype(np.int16)
def test_good_window():
sr_orig = 100
sr_new = 200
x = np.random.randn(500)
y = resampy.resample(x, sr_orig, sr_new, filter='sinc_window', window=scipy.signal.blackman)
assert len(y) == 2 * len(x)
def update_max_len(file_path_list, max_len):
tmp_max_len = 0
# Update the max length based on the given dataset
signal_set = set()
for file_path in file_path_list:
file_list = open(file_path)
for line in file_list:
line = line.strip().split()
if len(line) < 2:
print 'Wrong audio list file record in the line:', line
continue
file_str = line[0]
if file_str in signal_set:
continue
signal_set.add(file_str)
signal, rate = sf.read(file_str) # signal: sample values，rate: sample rate
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != FRAME_RATE:
# up-sample or down-sample for predefined sample rate
signal = resampy.resample(signal, rate, FRAME_RATE, filter='kaiser_fast')
if len(signal) > tmp_max_len:
tmp_max_len = len(signal)
file_list.close()
if tmp_max_len < max_len:
max_len = tmp_max_len
return max_len
def create_tf_example(ds):
waveform = ds[:]
attrs = ds.attrs
sample_rate = attrs['sample_rate']
# Trim waveform.
start_index = int(round(EXAMPLE_START_OFFSET * sample_rate))
length = int(round(EXAMPLE_DURATION * sample_rate))
waveform = waveform[start_index:start_index + length]
# Resample if needed.
if sample_rate != EXAMPLE_SAMPLE_RATE:
waveform = resampy.resample(waveform, sample_rate, EXAMPLE_SAMPLE_RATE)
waveform_feature = create_bytes_feature(waveform.tostring())
classification = attrs['classification']
label = 1 if classification.startswith('Call') else 0
label_feature = create_int64_feature(label)
clip_id = attrs['clip_id']
clip_id_feature = create_int64_feature(clip_id)
features = tf.train.Features(
feature={
'waveform': waveform_feature,
'label': label_feature,
'clip_id': clip_id_feature
})
return tf.train.Example(features=features)
def load_bgd_wav(file_path):
signal, rate = sf.read(file_path) # signal: sample values，rate: sample rate
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != FRAME_RATE:
# up-sample or down-sample for predefined sample rate
signal = resampy.resample(signal, rate, FRAME_RATE, filter='kaiser_fast')
return signal
def __test(sr_orig, sr_new, fil, rms, x, y):
y_pred = resampy.resample(x, sr_orig, sr_new, filter=fil)
idx = slice(sr_new // 2, - sr_new//2)
err = np.mean(np.abs(y[idx] - y_pred[idx]))
assert err <= rms, '{:g} > {:g}'.format(err, rms)
def __test(axis, sr_orig, sr_new, X):
Y = resampy.resample(X, sr_orig, sr_new, axis=axis)
target_shape = list(X.shape)
target_shape[axis] = target_shape[axis] * sr_new // sr_orig
eq_(target_shape, list(Y.shape))
def test_resampy(samples, input_rate, output_rate, filter_name, pdf_file):
# Resample chirp.
samples = resampy.resample(
samples, input_rate, output_rate, filter=filter_name)
# Plot spectrogram of result.
title = 'Resampy {}'.format(filter_name)
plot_spectrogram(samples, output_rate, title, pdf_file)
async def to_f32_16k(wav : bytes) -> numpy.ndarray:
# converting the wav to ndarray, which is much easier to use for DSP
rate, data = wavfile.read(BytesIO(wav))
# casting the data array to the right format (float32, for usage by pysndfx)
data = (data / (2. ** 15)).astype('float32')
if rate != BASE_SAMPLING_RATE:
data = resample(data, rate, BASE_SAMPLING_RATE)
return BASE_SAMPLING_RATE, data
def test_shape(axis):
sr_orig = 100
sr_new = sr_orig // 2
X = np.random.randn(sr_orig, sr_orig, sr_orig)
Y = resampy.resample(X, sr_orig, sr_new, axis=axis)
target_shape = list(X.shape)
target_shape[axis] = target_shape[axis] * sr_new // sr_orig
assert target_shape == list(Y.shape)
def load_audio(path, sr):
"""
Load audio file
"""
data, sr_orig = sf.read(path, dtype='float32', always_2d=True)
data = data.mean(axis=-1)
if sr_orig != sr:
data = resampy.resample(data, sr_orig, sr)
return data
def test_quality_sweep(sr_orig, sr_new, fil, rms):
FREQ = 8192
DURATION = 5.0
x = make_sweep(FREQ, sr_orig, DURATION)
y = make_sweep(FREQ, sr_new, DURATION)
y_pred = resampy.resample(x, sr_orig, sr_new, filter=fil)
idx = slice(sr_new // 2, - sr_new//2)
err = np.mean(np.abs(y[idx] - y_pred[idx]))
assert err <= rms, '{:g} > {:g}'.format(err, rms)
def __init__(self, sound_file, initial_begin=None, initial_end=None):
self.path = sound_file.filepath
self.mode = None
self.duration = sound_file.duration
self.num_channels = sound_file.n_channels
if self.duration < self.cache_amount:
self.mode = "short"
self.signal, self.sr = librosa.load(self.path, sr=None)
if len(self.signal.shape) == 1:
self.signal = self.signal.reshape((self.signal.shape[0], 1))
else:
self.signal = self.signal.T
self.preemph_signal = lfilter([1.0, -0.95], 1, self.signal, axis=0)
self.downsampled_1000 = resample(self.signal, self.sr, 1000, filter="kaiser_fast", axis=0)
self.downsampled_100 = resample(self.downsampled_1000, 1000, 100, filter="kaiser_fast", axis=0)
self.cached_begin = 0
self.cached_end = self.duration
else:
self.mode = "long"
if initial_begin is not None:
if initial_end is not None:
padding = self.cache_amount - (initial_end - initial_begin)
self.cached_begin = initial_begin - padding
self.cached_end = initial_end + padding
else:
self.cached_begin = initial_begin - self.cache_amount / 2
self.cached_end = initial_begin + self.cache_amount / 2
if self.cached_begin < 0:
self.cached_end -= self.cached_begin
self.cached_begin = 0
if self.cached_end > self.duration:
diff = self.cached_end - self.duration
self.cached_end = self.duration
self.cached_begin -= diff
else:
self.cached_begin = 0
self.cached_end = self.cache_amount
self.refresh_cache()
def waveform_to_examples(data, sample_rate, target_sample_rate=16000,
log_offset=0.01, stft_win_len_sec=0.025,
stft_hop_len_sec=0.010, num_mel_bins=64,
mel_min_hz=125, mel_max_hz=7500, frame_win_sec=0.96,
frame_hop_sec=0.96, **params):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.
sample_rate: Sample rate of data.
Returns:
3-D np.array of shape [num_examples, num_frames, num_bands] which represents
a sequence of examples, each of which contains a patch of log mel
spectrogram, covering num_frames frames of audio and num_bands mel frequency
bands, where the frame length is stft_hop_len_sec.
"""
# Convert to mono.
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sample_rate != target_sample_rate:
data = resampy.resample(data, sample_rate, target_sample_rate)
# Compute log mel spectrogram features.
log_mel = mel_features.log_mel_spectrogram(
data,
audio_sample_rate=target_sample_rate,
log_offset=log_offset,
window_length_secs=stft_win_len_sec,
hop_length_secs=stft_hop_len_sec,
num_mel_bins=num_mel_bins,
lower_edge_hertz=mel_min_hz,
upper_edge_hertz=mel_max_hz)
# Frame features into examples.
features_sample_rate = 1.0 / stft_hop_len_sec
example_window_length = int(round(
frame_win_sec * features_sample_rate))
example_hop_length = int(round(
frame_hop_sec * features_sample_rate))
log_mel_examples = mel_features.frame(
log_mel,
window_length=example_window_length,
hop_length=example_hop_length)
return log_mel_examples
def wavfile_to_waveform(wav_file):
wav_data, sr = wav_read(wav_file)
assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
data = wav_data / 32768.0 # Convert to [-1.0, +1.0]
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sr != vggish_params.SAMPLE_RATE:
data = resampy.resample(data, sr, vggish_params.SAMPLE_RATE)
sr = vggish_params.SAMPLE_RATE
return data, sr
def waveform_to_examples(data, sample_rate):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.
sample_rate: Sample rate of data.
Returns:
3-D np.array of shape [num_examples, num_frames, num_bands] which represents
a sequence of examples, each of which contains a patch of log mel
spectrogram, covering num_frames frames of audio and num_bands mel frequency
bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
"""
import resampy
# Convert to mono.
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sample_rate != vggish_params.SAMPLE_RATE:
data = resampy.resample(data, sample_rate, vggish_params.SAMPLE_RATE)
# Compute log mel spectrogram features.
log_mel = mel_features.log_mel_spectrogram(
data,
audio_sample_rate=vggish_params.SAMPLE_RATE,
log_offset=vggish_params.LOG_OFFSET,
window_length_secs=vggish_params.STFT_WINDOW_LENGTH_SECONDS,
hop_length_secs=vggish_params.STFT_HOP_LENGTH_SECONDS,
num_mel_bins=vggish_params.NUM_MEL_BINS,
lower_edge_hertz=vggish_params.MEL_MIN_HZ,
upper_edge_hertz=vggish_params.MEL_MAX_HZ,
)
# Frame features into examples.
features_sample_rate = 1.0 / vggish_params.STFT_HOP_LENGTH_SECONDS
example_window_length = int(
round(vggish_params.EXAMPLE_WINDOW_SECONDS * features_sample_rate)
)
example_hop_length = int(
round(vggish_params.EXAMPLE_HOP_SECONDS * features_sample_rate)
)
log_mel_examples = mel_features.frame(
log_mel, window_length=example_window_length, hop_length=example_hop_length
)
return log_mel_examples
def resample_to_24000_hz(samples, input_rate):
case = _24000_HZ_SPECIAL_CASES.get(float(input_rate))
if case is not None:
# input rate is a special case for which we can resample
# efficiently using a polyphase filter
up, down, filter_ = case
# print('Resampling from {} Hz to 24000 Hz...'.format(input_rate))
return signal.resample_poly(samples, up, down, window=filter_)
else:
return resampy.resample(samples, input_rate, 24000)
def sync_with_pilot(seed, constl, ts_symbol_length, sample, sps):
from resampy import resample
np.random.seed(seed)
constl = np.atleast_2d(constl)
constl = constl[0]
sample = np.atleast_2d(sample)
index = np.random.randint(0, len(constl) - 1, (1, ts_symbol_length))
pilot = np.empty((sample.shape[0], ts_symbol_length * sps))
for i in pilot.shape[0]:
pilot[i] = resample(constl[index], 1, sps)
sample_after_sync = sync_correlation(pilot, sample, sps)
return sample_after_sync
def waveform_to_examples(self, data, sample_rate):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.
sample_rate: Sample rate of data.
Returns:
3-D np.array of shape [num_examples, num_frames, num_bands] which represents
a sequence of examples, each of which contains a patch of log mel
spectrogram, covering num_frames frames of audio and num_bands mel frequency
bands, where the frame length is vggish_params.STFT_HOP_LENGTH_SECONDS.
"""
from .vggish_example_helper import mel_features
import resampy
# Convert to mono.
print(type(data))
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sample_rate != self.sample_rate:
data = resampy.resample(data, sample_rate, self.sample_rate)
# Compute log mel spectrogram features.
log_mel = mel_features.log_mel_spectrogram(
data,
audio_sample_rate=self.sample_rate,
log_offset=self.log_offset,
window_length_secs=self.stft_window_length_seconds,
hop_length_secs=self.stft_hop_length_seconds,
num_mel_bins=self.num_mel_binds,
lower_edge_hertz=self.mel_min_hz,
upper_edge_hertz=self.mel_max_hz)
# Frame features into examples.
features_sample_rate = 1.0 / self.stft_hop_length_seconds
example_window_length = int(
round(self.example_window_seconds * features_sample_rate))
example_hop_length = int(
round(self.example_hop_seconds * features_sample_rate))
log_mel_examples = mel_features.frame(
log_mel,
window_length=example_window_length,
hop_length=example_hop_length)
return log_mel_examples
def resample(y,
orig_sr,
target_sr,
res_type='kaiser_best',
fix=True,
scale=False,
**kwargs):
# First, validate the audio buffer
utils.valid_audio(y, mono=False)
if orig_sr == target_sr:
return y
ratio = float(target_sr) / orig_sr
n_samples = int(np.ceil(y.shape[-1] * ratio))
if res_type in ('scipy', 'fft'):
y_hat = scipy.signal.resample(y, n_samples, axis=-1)
elif res_type == 'polyphase':
if int(orig_sr) != orig_sr or int(target_sr) != target_sr:
raise ParameterError(
'polyphase resampling is only supported for integer-valued sampling rates.'
)
# For polyphase resampling, we need up- and down-sampling ratios
# We can get those from the greatest common divisor of the rates
# as long as the rates are integrable
orig_sr = int(orig_sr)
target_sr = int(target_sr)
gcd = np.gcd(orig_sr, target_sr)
y_hat = scipy.signal.resample_poly(y,
target_sr // gcd,
orig_sr // gcd,
axis=-1)
else:
y_hat = resampy.resample(y,
orig_sr,
target_sr,
filter=res_type,
axis=-1)
if fix:
y_hat = utils.fix_length(y_hat, n_samples, **kwargs)
if scale:
y_hat /= np.sqrt(ratio)
return np.ascontiguousarray(y_hat, dtype=y.dtype)
def waveform_to_examples(data, sample_rate):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.
sample_rate: Sample rate of data.
Returns:
3-D np.array of shape [num_examples, num_frames, num_bands] which represents
a sequence of examples, each of which contains a patch of log mel
spectrogram, covering num_frames frames of audio and num_bands mel frequency
bands, where the frame length is params.STFT_HOP_LENGTH_SECONDS.
"""
# Convert to mono.
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sample_rate != params.SAMPLE_RATE:
data = resampy.resample(data, sample_rate, params.SAMPLE_RATE)
# Compute log mel spectrogram features.
log_mel = mel_features.log_mel_spectrogram(
data,
audio_sample_rate=params.SAMPLE_RATE, # 16000
log_offset=params.
LOG_OFFSET, # 0.01 Offset used for stabilized log of input mel-spectrogram.
window_length_secs=params.STFT_WINDOW_LENGTH_SECONDS, # 25ms
hop_length_secs=params.STFT_HOP_LENGTH_SECONDS, # 10ms
num_mel_bins=params.
NUM_MEL_BINS, # 64 # Frequency bands in input mel-spectrogram patch.
lower_edge_hertz=params.MEL_MIN_HZ, # 125Hz
upper_edge_hertz=params.MEL_MAX_HZ) # 7500Hz
# Frame features into examples.
features_sample_rate = 1.0 / params.STFT_HOP_LENGTH_SECONDS # 100 frame/s
example_window_length = int(
round(params.EXAMPLE_WINDOW_SECONDS * features_sample_rate)
) # 0.96 # Each example contains 96 10ms frames
example_hop_length = int(
round(params.EXAMPLE_HOP_SECONDS *
features_sample_rate)) # 0.96 # with zero overlap.
log_mel_examples = mel_features.frame( # [3, 96, 64] change log-mel to batch whose length is 1s
log_mel,
window_length=example_window_length, # 96
hop_length=example_hop_length) # 96
return log_mel_examples
def recognize_array(self, frames, sr):
t = time.time()
if decoder.CONVERT_TO_MONO:
frames = np.array([np.mean(frames, axis=0)], dtype=frames.dtype)
if decoder.RESAMPLE and sr != fingerprint.DEFAULT_FS and len(frames[-1]) > 0:
frames = resample(frames, sr, fingerprint.DEFAULT_FS, axis=-1)
self.Fs = fingerprint.DEFAULT_FS
if decoder.NORMALIZE and len(frames[-1]) > 0:
gain = (-np.iinfo(frames.dtype).min) / np.max(np.abs(frames))
frames = np.array(frames * gain, dtype=frames.dtype)
match = self._recognize(*frames)
t = time.time() - t
if match:
match['match_time'] = t
return match
def read_wav(fname, output_sr, use_rosa=False):
if use_rosa:
waveform, sr = librosa.load(fname, sr=output_sr)
else:
wav_data, sr = sf.read(fname, dtype=np.int16)
if wav_data.ndim > 1:
# (ns, 2)
wav_data = wav_data.mean(1)
if sr != output_sr:
wav_data = resampy.resample(wav_data, sr, output_sr)
waveform = wav_data / 32768.0
return waveform.astype(np.float64)
def read_wav(filename, target_sample_rate=16000, verbose=False):
rate, data = wavfile.read(filename)
assert (data.dtype == np.int16)
data = np.float32(data) / 32768.0
# stereo -> mono
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the correct sample rate if necessary
if rate != target_sample_rate:
data = resampy.resample(data, rate, target_sample_rate)
return data
def test_nnresample():
""" Compare matlab and nnresample resample : FAILING """
from nnresample import resample
from pystoi.stoi import FS
import matlab_wrapper
matlab = matlab_wrapper.MatlabSession()
matlab.put('FS', float(FS))
RTOL = 1e-4
for fs in [8000, 11025, 16000, 22050, 32000, 44100, 48000]:
x = np.random.randn(2*fs,)
x_r = resample(x, FS, fs)
matlab.put('x', x)
matlab.put('fs', float(fs))
matlab.eval('x_r = resample(x, FS, fs)')
assert_allclose(x_r, matlab.get('x_r'), atol=ATOL, rtol=RTOL)
def resample(self, new_fs):
"""
Resample time series to ``new_fs``.
"""
if self.start_time != 0:
raise (NotImplementedError(
'The method resample is implemented only for time series '
'objects with start_time equal to 0.'))
if self.fs == new_fs:
return self
new = self.copy()
new.data = resampy.resample(self.data, float(self.fs), float(new_fs))
new.fs = new_fs
return new
def handle(self, data):
in_data, frame_count, time_info, status, divisor = data
try:
decoded = np.frombuffer(in_data, dtype=np.int16) / divisor
decimated = resampy.resample(decoded,
self.fs,
self.new_fs,
filter=load_resampy_filter())
logger.info(
f"Decimated {decoded.size} to {decimated.size} samples")
self.protocol.sendLine(
f"DAT|{'|'.join([str(d) for d in decimated])}".encode())
except:
logger.exception(
f"Unserialisable data type {data.__class__.__name__}")
def yamnet_transform(waveform, sample_rate):
'''
Args:
waveform: np tsr [num_steps, num_channels]
sample_rate: per second sample rate
'''
import tensorflow as tf
# tf.enable_eager_execution()
data = waveform.mean(axis=0)
if sample_rate != YAMNetParams.SAMPLE_RATE:
data = resampy.resample(data, sample_rate, VGGishParams.SAMPLE_RATE)
spectrogram = features_lib.waveform_to_log_mel_spectrogram(
data, YAMNetParams)
patches = features_lib.spectrogram_to_patches(spectrogram, YAMNetParams)
return patches
def resample_data(y, orig_sr, target_sr, **kwargs):
if orig_sr == target_sr:
return y
ratio = float(target_sr) / orig_sr
n_samples = int(np.ceil(y.shape[-1] * ratio))
y_hat = resampy.resample(y,
orig_sr,
target_sr,
filter='kaiser_best',
axis=-1)
y_hat = fix_length(y_hat, n_samples, **kwargs)
return np.ascontiguousarray(y_hat, dtype=y.dtype)
def song_downsampler(input_song,
n_out_channels=256,
input_freq=44100,
output_freq=8000):
'''
Uses resampys downsampler to convert to lower number of data points without ruining the
audio file
'''
ds_ch = resampy.resample(input_song.astype(np.float), input_freq,
output_freq)
ds_song = ds_ch.transpose()
return ds_song
def augment_audio_signal(signal, sample_freq, augmentation):
"""Function that performs audio signal augmentation.
Args:
signal (np.array): np.array containing raw audio signal.
sample_freq (float): frames per second.
augmentation (dict, optional): None or dictionary of augmentation parameters.
If not None, has to have 'speed_perturbation_ratio',
'noise_level_min', or 'noise_level_max' fields, e.g.::
augmentation={
'speed_perturbation_ratio': 0.2,
'noise_level_min': -90,
'noise_level_max': -46,
}
'speed_perturbation_ratio' can either be a list of possible speed
perturbation factors or a float. If float, a random value from
U[1-speed_perturbation_ratio, 1+speed_perturbation_ratio].
Returns:
np.array: np.array with augmented audio signal.
"""
signal_float = normalize_signal(signal.astype(np.float32))
if 'speed_perturbation_ratio' in augmentation:
stretch_amount = -1
if isinstance(augmentation['speed_perturbation_ratio'], list):
stretch_amount = np.random.choice(
augmentation['speed_perturbation_ratio'])
elif augmentation['speed_perturbation_ratio'] > 0:
# time stretch (might be slow)
stretch_amount = 1.0 + (2.0 * np.random.rand() - 1.0) * \
augmentation['speed_perturbation_ratio']
if stretch_amount > 0:
signal_float = rs.resample(
signal_float,
sample_freq,
int(sample_freq * stretch_amount),
filter='kaiser_best',
)
# noise
if 'noise_level_min' in augmentation and 'noise_level_max' in augmentation:
noise_level_db = np.random.randint(
low=augmentation['noise_level_min'],
high=augmentation['noise_level_max'])
signal_float += np.random.randn(signal_float.shape[0]) * \
10.0 ** (noise_level_db / 20.0)
return normalize_signal(signal_float)
def preprocess_audio_batch(audio, sr, center=True, hop_size=0.1, sampler="julian"):
if audio.ndim == 3:
audio = torch.mean(audio, axis=2)
if sr != TARGET_SR:
if sampler == "julian":
audio = julius.resample_frac(audio, sr, TARGET_SR)
elif sampler == "resampy":
audio = torch.tensor(
resampy.resample(
audio.detach().cpu().numpy(),
sr_orig=sr,
sr_new=TARGET_SR,
filter="kaiser_best",
),
dtype=audio.dtype,
device=audio.device,
)
else:
raise ValueError("Only julian and resampy works!")
frame_len = TARGET_SR
hop_len = int(hop_size * TARGET_SR)
if center:
audio = center_audio(audio, frame_len)
audio = pad_audio(audio, frame_len, hop_len)
n_frames = 1 + int((audio.size()[1] - frame_len) / float(hop_len))
x = []
xframes_shape = None
for i in range(audio.shape[0]):
xframes = (
torch.as_strided(
audio[i],
size=(frame_len, n_frames),
stride=(1, hop_len),
)
.transpose(0, 1)
.unsqueeze(1)
)
if xframes_shape is None:
xframes_shape = xframes.shape
assert xframes.shape == xframes_shape
x.append(xframes)
x = torch.vstack(x)
return x
def get_frames(audio, sr, center=True, step_size=10, normalize: bool = True):
"""Split the provided audio into frames
Parameters
----------
audio : np.ndarray [shape=(N,) or (N, C)]
The audio samples. Multichannel audio will be downmixed.
sr : int
Sample rate of the audio samples. The audio will be resampled if
the sample rate is not 16 kHz, which is expected by the model.
center : boolean
- If `True` (default), the signal `audio` is padded so that frame
`D[:, t]` is centered at `audio[t * hop_length]`.
- If `False`, then `D[:, t]` begins at `audio[t * hop_length]`
step_size : int
The step size in milliseconds for running pitch estimation.
Returns
-------
frames : np.ndarray [shape=(T, 1024)]
"""
if len(audio.shape) == 2:
audio = audio.mean(1) # make mono
audio = audio.astype(np.float32)
if sr != model_srate:
# resample audio if necessary
from resampy import resample
audio = resample(audio, sr, model_srate)
# pad so that frames are centered around their timestamps (i.e. first frame
# is zero centered).
if center:
audio = np.pad(audio, 512, mode='constant', constant_values=0)
# make 1024-sample frames of the audio with hop length of 10 milliseconds
hop_length = int(model_srate * step_size / 1000)
n_frames = 1 + int((len(audio) - 1024) / hop_length)
frames = as_strided(audio,
shape=(1024, n_frames),
strides=(audio.itemsize, hop_length * audio.itemsize),
writeable=False).copy()
frames = frames.transpose()
if normalize:
# normalize each frame -- this is expected by the model
frames = frames - frames.mean(axis=-1, keepdims=True)
frames = frames / (frames.std(axis=-1, keepdims=True) + 1e-6)
return frames
def use_audio_path_specified_audio(wav_path,
wav_word,
rms_normalize=None,
SR=20000):
"""
Loads an example wav specified by wav_path
Inputs:
wav_path (string) : filepath to the audio to load
wav_word (string) : label for the audio in wav_path
rms_normalize (float) : the rms value to set the audio to
SR (int) : sampling rate of the desired audio. The file at
wav_path will be resampled to this value
Output:
audio_dict (dictionary) : a dictionary containing the loaded
audio and preprocessing parameters
"""
metamer_word_encodings = pickle.load(
open('assets/metamer_word_encodings.pckl', 'rb'))
word_to_int = metamer_word_encodings['word_to_word_idx']
print("Loading: %s" % wav_path)
SR_loaded, wav_f = scipy.io.wavfile.read(wav_path)
if SR_loaded != SR:
wav_f = resampy.resample(wav_f, SR_loaded, SR)
SR_loaded = SR
if rms_normalize is not None:
wav_f = wav_f - np.mean(wav_f.ravel())
wav_f = wav_f / (np.sqrt(np.mean(wav_f.ravel()**2))) * rms_normalize
rms = rms_normalize
else:
rms = np.sqrt(np.mean(wav_f.ravel()**2))
audio_dict = {}
audio_dict['wav'] = wav_f
audio_dict['SR'] = SR
audio_dict['word_int'] = word_to_int[wav_word]
audio_dict['word'] = wav_word
audio_dict['rms'] = rms
audio_dict['filename'] = wav_path
audio_dict['filename_short'] = wav_path.split('/')[-1]
audio_dict['correct_response'] = wav_word
return audio_dict
def _process_input_chunk(self, samples):
input_length = len(samples)
if self._classifier_sample_rate != self._input_sample_rate:
# start_time = time.time()
samples = resampy.resample(samples,
self._input_sample_rate,
self._classifier_sample_rate,
filter='kaiser_fast')
# processing_time = time.time() - start_time
# input_duration = input_length / self._input_sample_rate
# rate = input_duration / processing_time
# print((
# 'Resampled {:.1f} seconds of input in {:.1f} seconds, '
# 'or {:.1f} times faster than real time.').format(
# input_duration, processing_time, rate))
self._waveforms = _get_analysis_records(
samples, self._classifier_waveform_length, self._hop_size)
# print('Scoring chunk waveforms...')
# start_time = time.time()
scores = classifier_utils.score_dataset_examples(
self._estimator, self._create_dataset)
# elapsed_time = time.time() - start_time
# num_waveforms = self._waveforms.shape[0]
# rate = num_waveforms / elapsed_time
# print((
# 'Scored {} waveforms in {:.1f} seconds, a rate of {:.1f} '
# 'waveforms per second.').format(
# num_waveforms, elapsed_time, rate))
if _SCORE_OUTPUT_ENABLED:
self._score_file_writer.write(samples, scores)
for threshold in self._thresholds:
peak_indices = signal_utils.find_peaks(scores, threshold)
peak_scores = scores[peak_indices]
self._notify_listener_of_clips(peak_indices, peak_scores,
input_length, threshold)
self._input_chunk_start_index += input_length
def resample(self, target_sample_rate, filter='kaiser_best'):
"""Resample the audio to a target sample rate.
Note that this is an in-place transformation.
:param target_sample_rate: Target sample rate.
:type target_sample_rate: int
:param filter: The resampling filter to use one of {'kaiser_best',
'kaiser_fast'}.
:type filter: str
"""
self._samples = resampy.resample(self.samples,
self.sample_rate,
target_sample_rate,
filter=filter)
self._sample_rate = target_sample_rate
def test(table, old_sr, new_sr, device="cpu"):
x = th.randn(16, 8 * old_sr * int(math.ceil(44_100 / old_sr)), device=device)
with Chrono() as chrono:
y = resample_frac(x, old_sr, new_sr, zeros=56)
dur_julius = int(1000 * chrono.duration)
if device == "cpu":
with Chrono() as chrono:
y_resampy = th.from_numpy(resampy.resample(x.numpy(), old_sr, new_sr))
dur_resampy = int(1000 * chrono.duration)
delta = (y_resampy - y).abs().mean()
table.line([old_sr, new_sr, dur_julius, dur_resampy, format(delta, ".1%")])
else:
table.line([old_sr, new_sr, dur_julius])
def test_resample(input, input_sample_rate, output_sample_rate, method, axis):
t = transforms.Resample(input_sample_rate, output_sample_rate,
method=method, axis=axis)
output_length = int(input.shape[axis] * output_sample_rate
/ float(input_sample_rate))
print(input.shape)
if method == 'scipy':
expected_output = scipy.signal.resample(input, output_length,
axis=axis)
else:
expected_output = resampy.resample(input, input_sample_rate,
output_sample_rate, method=method,
axis=axis)
transformed_input = t(input)
assert transformed_input.shape[axis] == output_length
assert np.array_equal(transformed_input, expected_output)
def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
"""
Resample `x` to a new sampling rate.
:param x: Sample to resample of shape `(batch_size, length, channel)` or `(batch_size, channel, length)`.
:param y: Labels of the sample `x`. This function does not affect them in any way.
:return: Resampled audio sample.
"""
import resampy
if x.ndim != 3:
raise ValueError("Resampling can only be applied to temporal data across at least one channel.")
sample_index = 2 if self.channels_first else 1
return resampy.resample(x, self.sr_original, self.sr_new, axis=sample_index, filter="sinc_window"), y
def wav_stream(files):
for file in files:
srate, data = wavfile.read(os.path.join(args.input_path, file))
if len(data.shape) == 2:
data = data.mean(axis=1)
if srate != 16000:
data = resample(data, srate, 16000)
srate = 16000
hop_length = int(srate / 100)
n_frames = 1 + int((len(data) - 1024) / hop_length)
frames = as_strided(data,
shape=(1024, n_frames),
strides=(data.itemsize,
hop_length * data.itemsize))
frames = frames.transpose().astype(np.float32)
yield (file, frames)
def audio_to_midi_melodia(infile, outfile, bpm, smooth=0.25, minduration=0.1):
# define analysis parameters
fs = 44100
hop = 128
# load audio using librosa
print("Loading audio...")
data, sr = soundfile.read(infile)
# mixdown to mono if needed
if len(data.shape) > 1 and data.shape[1] > 1:
data = data.mean(axis=1)
# resample to 44100 if needed
if sr != fs:
data = resampy.resample(data, sr, fs)
sr = fs
# extract melody using melodia vamp plugin
print("Extracting melody f0 with MELODIA...")
melody = vamp.collect(data,
sr,
"mtg-melodia:melodia",
parameters={"voicing": 0.2})
# hop = melody['vector'][0]
pitch = melody['vector'][1]
# impute missing 0's to compensate for starting timestamp
pitch = np.insert(pitch, 0, [0] * 8)
# debug
# np.asarray(pitch).dump('f0.npy')
# print(len(pitch))
# convert f0 to midi notes
print("Converting Hz to MIDI notes...")
midi_pitch = hz2midi(pitch)
# segment sequence into individual midi notes
notes = midi_to_notes(midi_pitch, fs, hop, smooth, minduration)
# print notes
# save note sequence to a midi file
print("Saving MIDI to disk...")
save_midi(outfile, notes, bpm)
print("Conversion complete.")
def annotated_spectrogram(audio_file,
annotations_file,
fs_spec=16000,
figsize=(10, 5),
height_ratios=[3, 1]):
n = np.genfromtxt(annotations_file, dtype=np.str)
unique_labels = np.unique(n[:, 2])
audio, fs = sf.read(audio_file)
# extract mono from multichannel audio
if audio.ndim > 1 and np.min(audio.shape) > 1:
if np.where(audio.shape == np.min(audio.shape))[0][0] == 0:
audio = audio[0, :]
else:
audio = audio[:, 0]
audio_lowres = resampy.resample(audio, fs, fs_spec)
f, t, sxx = spectrogram(audio_lowres, fs_spec, nperseg=1024)
gs = gridspec.GridSpec(2, 1, height_ratios=height_ratios)
gs.update(hspace=0.1)
fig = plt.figure(figsize=figsize)
ax = plt.subplot(gs[0])
plt.xlim([0, 60])
plt.ylabel('Frequency (Hz)')
plt.xticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.pcolormesh(t, f, np.log10(sxx), cmap='GnBu')
ax2 = plt.subplot(gs[1])
for i, label in enumerate(unique_labels):
# get list of start/stop times for each label
a = n[:, 0][n[:, 2] == label].astype(float)
b = n[:, 1][n[:, 2] == label].astype(float)
ax2.hlines(np.zeros(len(a)) + i, a, b, colors=plt.cm.tab20(i), lw=10)
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
plt.xlim([0, 60])
plt.yticks(np.arange(i + 1), unique_labels)
plt.xlabel('Time (seconds)')
def predict_offline(model,
audio,
sr,
center=True,
step_size=10,
viterbi=False):
if len(audio.shape) == 2:
audio = audio.mean(1) # make mono
audio = audio.astype(np.float32)
if sr != model_srate:
# resample audio if necessary
from resampy import resample
audio = resample(audio, sr, model_srate)
# pad so that frames are centered around their timestamps (i.e. first frame
# is zero centered).
if center:
audio = np.pad(audio, 512, mode='constant', constant_values=0)
# make 1024-sample frames of the audio with hop length of 10 milliseconds
hop_length = int(model_srate * step_size / 1000)
n_frames = 1 + int((len(audio) - 1024) / hop_length)
frames = as_strided(audio,
shape=(1024, n_frames),
strides=(audio.itemsize, hop_length * audio.itemsize))
frames = frames.transpose().copy()
# normalize each frame -- this is expected by the model
frames -= np.mean(frames, axis=1)[:, np.newaxis]
frames /= np.std(frames, axis=1)[:, np.newaxis]
# run prediction and convert the frequency bin weights to Hz
activation = model.predict(frames, verbose=0)
confidence = activation.max(axis=1)
if viterbi:
cents = to_viterbi_cents(activation)
else:
cents = to_local_average_cents(activation)
frequency = 10 * 2**(cents / 1200)
frequency[np.isnan(frequency)] = 0
time = np.arange(confidence.shape[0]) * step_size / 1000.0
return frequency, confidence
def main(argv):
assert argv, 'Usage: inference.py <wav file> <wav file> ...'
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'yamnet.h5')
classes_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'yamnet_class_map.csv')
event_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'event.json')
params = yamnet_params.Params()
yamnet = yamnet_model.yamnet_frames_model(params)
yamnet.load_weights(model_path)
yamnet_classes = yamnet_model.class_names(classes_path)
for file_name in argv:
# Decode the WAV file.
wav_data, sr = sf.read(file_name, dtype=np.int16)
assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
waveform = wav_data / 32768.0 # Convert to [-1.0, +1.0]
waveform = waveform.astype('float32')
# Convert to mono and the sample rate expected by YAMNet.
if len(waveform.shape) > 1:
waveform = np.mean(waveform, axis=1)
if sr != params.sample_rate:
waveform = resampy.resample(waveform, sr, params.sample_rate)
# Predict YAMNet classes.
scores, embeddings, spectrogram = yamnet(waveform)
# Scores is a matrix of (time_frames, num_classes) classifier scores.
# Average them along time to get an overall classifier output for the clip.
prediction = np.mean(scores, axis=0)
# Report the highest-scoring classes and their scores.
top5_i = np.argsort(prediction)[::-1][:5]
print(file_name, ':\n' +
'\n'.join(' {:12s}: {:.3f}'.format(yamnet_classes[i], prediction[i])
for i in top5_i))
# print all classes
b = prediction.tolist() # nested lists with same data, indices
pred = []
for (i,cls) in enumerate(yamnet_classes):
item={}
item['label']=cls
item['value']=round(b[i], 6)
pred.append(item)
pred = sorted(pred, key=lambda x: x['value'], reverse=True)
json.dump(pred, codecs.open(event_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4) ### this saves the array in .json format
def pitchBend(frame, pitch_to_bend):
if pitch_to_bend == 0:
return frame
freq_oitar = np.exp(-pitch_to_bend * 0.057762265046662105)
# The inverse of 'ratio'
frame = resample(frame, SR, SR * freq_oitar, filter=FILTER)
left = frame[:CROSS_FADE_TAILS]
left_mid = frame[CROSS_FADE_TAILS:CROSS_FADE_TAILS + CROSS_FADE_OVERLAP]
right_mid = frame[-CROSS_FADE_TAILS - CROSS_FADE_OVERLAP:-CROSS_FADE_TAILS]
right = frame[-CROSS_FADE_TAILS:]
frame = np.concatenate((
left,
np.multiply(left_mid, FADE_OUT_WINDOW) +
np.multiply(right_mid, FADE_IN_WINDOW),
right,
))
return frame
def waveform_to_examples(data, sample_rate):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.
sample_rate: Sample rate of data.
Returns:
3-D np.array of shape [num_examples, num_frames, num_bands] which represents
a sequence of examples, each of which contains a patch of log mel
spectrogram, covering num_frames frames of audio and num_bands mel frequency
bands, where the frame length is params.STFT_HOP_LENGTH_SECONDS.
"""
# Convert to mono.
if len(data.shape) > 1:
data = np.mean(data, axis=1)
# Resample to the rate assumed by VGGish.
if sample_rate != params.SAMPLE_RATE:
data = resampy.resample(data, sample_rate, params.SAMPLE_RATE)
# Compute log mel spectrogram features.
log_mel = mel_features.log_mel_spectrogram(
data,
audio_sample_rate=params.SAMPLE_RATE,
log_offset=params.LOG_OFFSET,
window_length_secs=params.STFT_WINDOW_LENGTH_SECONDS,
hop_length_secs=params.STFT_HOP_LENGTH_SECONDS,
num_mel_bins=params.NUM_MEL_BINS,
lower_edge_hertz=params.MEL_MIN_HZ,
upper_edge_hertz=params.MEL_MAX_HZ)
# Frame features into examples.
features_sample_rate = 1.0 / params.STFT_HOP_LENGTH_SECONDS
example_window_length = int(round(
params.EXAMPLE_WINDOW_SECONDS * features_sample_rate))
example_hop_length = int(round(
params.EXAMPLE_HOP_SECONDS * features_sample_rate))
log_mel_examples = mel_features.frame(
log_mel,
window_length=example_window_length,
hop_length=example_hop_length)
return log_mel_examples
def _get_clip_samples(self, clip):
clip_sample_rate = clip.sample_rate
classifier_sample_rate = self._settings.waveform_sample_rate
s2f = signal_utils.seconds_to_frames
start_offset = s2f(self._waveform_start_time, clip_sample_rate)
if clip_sample_rate != classifier_sample_rate:
# need to resample
# Get clip samples, including a millisecond of padding at
# the end. I don't know what if any guarantees the
# `resampy.resample` function offers about the relationship
# between its input and output lengths, so we add the padding
# to try to ensure that we don't wind up with too few samples
# after resampling.
length = s2f(self._waveform_duration + .001, clip_sample_rate)
samples = self._clip_manager.get_samples(
clip, start_offset=start_offset, length=length)
# Resample clip samples to classifier sample rate.
samples = resampy.resample(
samples, clip_sample_rate, classifier_sample_rate)
# Discard any extra trailing samples we wound up with.
samples = samples[:self._waveform_length]
if len(samples) < self._waveform_length:
raise ValueError('Resampling produced too few samples.')
else:
# don't need to resample
samples = self._clip_manager.get_samples(
clip, start_offset=start_offset, length=self._waveform_length)
return samples
def _process_input_chunk(self, samples):
input_length = len(samples)
if self._classifier_sample_rate != self._input_sample_rate:
samples = resampy.resample(
samples, self._input_sample_rate, self._classifier_sample_rate,
filter='kaiser_fast')
self._waveforms = _get_analysis_records(
samples, self._classifier_waveform_length, self._hop_size)
# print('Scoring chunk waveforms...')
# start_time = time.time()
scores = classifier_utils.score_dataset_examples(
self._estimator, self._create_dataset)
# elapsed_time = time.time() - start_time
# num_waveforms = self._waveforms.shape[0]
# rate = num_waveforms / elapsed_time
# print((
# 'Scored {} waveforms in {:.1f} seconds, a rate of {:.1f} '
# 'waveforms per second.').format(
# num_waveforms, elapsed_time, rate))
if _SCORE_OUTPUT_ENABLED:
self._score_file_writer.write(samples, scores)
for threshold in self._thresholds:
peak_indices = signal_utils.find_peaks(scores, threshold)
peak_scores = scores[peak_indices]
self._notify_listener_of_clips(
peak_indices, peak_scores, input_length, threshold)
self._input_chunk_start_index += input_length
def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
original sampling rate of `y`
target_sr : number > 0 [scalar]
target sampling rate
res_type : str
resample type (see note)
.. note::
By default, this uses `resampy`'s high-quality mode ('kaiser_best').
To use `scipy.signal.resample`, set `res_type='scipy'`.
fix : bool
adjust the length of the resampled signal to be of size exactly
`ceil(target_sr * len(y) / orig_sr)`
scale : bool
Scale the resampled signal so that `y` and `y_hat` have approximately
equal total energy.
kwargs : additional keyword arguments
If `fix==True`, additional keyword arguments to pass to
`librosa.util.fix_length`.
Returns
-------
y_hat : np.ndarray [shape=(n * target_sr / orig_sr,)]
`y` resampled from `orig_sr` to `target_sr`
See Also
--------
librosa.util.fix_length
scipy.signal.resample
resampy.resample
Examples
--------
Downsample from 22 KHz to 8 KHz
>>> y, sr = librosa.load(librosa.util.example_audio_file(), sr=22050)
>>> y_8k = librosa.resample(y, sr, 8000)
>>> y.shape, y_8k.shape
((1355168,), (491671,))
"""
# First, validate the audio buffer
util.valid_audio(y, mono=False)
if orig_sr == target_sr:
return y
ratio = float(target_sr) / orig_sr
n_samples = int(np.ceil(y.shape[-1] * ratio))
if res_type == 'scipy':
y_hat = scipy.signal.resample(y, n_samples, axis=-1)
else:
y_hat = resampy.resample(y, orig_sr, target_sr, filter=res_type, axis=-1)
if fix:
y_hat = util.fix_length(y_hat, n_samples, **kwargs)
if scale:
y_hat /= np.sqrt(ratio)
return np.ascontiguousarray(y_hat, dtype=y.dtype)
def test_short_signal():
x = np.zeros(2)
resampy.resample(x, 4, 1)
def test_dtype(dtype):
x = np.random.randn(100).astype(dtype)
y = resampy.resample(x, 100, 200)
assert x.dtype == y.dtype
def test_bad_window():
x = np.zeros(100)
resampy.resample(x, 100, 200, filter='sinc_window', window=np.ones(50))
def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
original sampling rate of `y`
target_sr : number > 0 [scalar]
target sampling rate
res_type : str
resample type (see note)
.. note::
By default, this uses `resampy`'s high-quality mode ('kaiser_best').
If `res_type` is not recognized by `resampy.resample`, it then
falls back on `scikits.samplerate` (if it is installed)
If both of those fail, it will fall back on `scipy.signal.resample`.
To force use of `scipy.signal.resample`, set `res_type='scipy'`.
fix : bool
adjust the length of the resampled signal to be of size exactly
`ceil(target_sr * len(y) / orig_sr)`
scale : bool
Scale the resampled signal so that `y` and `y_hat` have approximately
equal total energy.
kwargs : additional keyword arguments
If `fix==True`, additional keyword arguments to pass to
`librosa.util.fix_length`.
Returns
-------
y_hat : np.ndarray [shape=(n * target_sr / orig_sr,)]
`y` resampled from `orig_sr` to `target_sr`
See Also
--------
librosa.util.fix_length
scipy.signal.resample
Examples
--------
Downsample from 22 KHz to 8 KHz
>>> y, sr = librosa.load(librosa.util.example_audio_file(), sr=22050)
>>> y_8k = librosa.resample(y, sr, 8000)
>>> y.shape, y_8k.shape
((1355168,), (491671,))
"""
# First, validate the audio buffer
util.valid_audio(y, mono=False)
if orig_sr == target_sr:
return y
ratio = float(target_sr) / orig_sr
n_samples = int(np.ceil(y.shape[-1] * ratio))
try:
y_hat = resampy.resample(y, orig_sr, target_sr, filter=res_type, axis=-1)
except NotImplementedError:
if _HAS_SAMPLERATE and (res_type != 'scipy'):
warnings.warn('scikits.samplerate resampling is deprecated as '
'of librosa version 0.4.3.\n\tSupport will be '
'removed in librosa version 0.5.',
category=DeprecationWarning)
y_hat = samplerate.resample(y.T, ratio, res_type).T
else:
y_hat = scipy.signal.resample(y, n_samples, axis=-1)
if fix:
y_hat = util.fix_length(y_hat, n_samples, **kwargs)
if scale:
y_hat /= np.sqrt(ratio)
return np.ascontiguousarray(y_hat, dtype=y.dtype)
def test_bad_num_zeros():
x = np.zeros(100)
resampy.resample(x, 100, 50, filter='sinc_window', num_zeros=0)
def test_bad_precision():
x = np.zeros(100)
resampy.resample(x, 100, 50, filter='sinc_window', precision=-1)
def get_feature(audio_list, spk_to_idx, min_mix=2, max_mix=2, batch_size=1):
"""
:param audio_list: audio file list
path/to/1st.wav spk1
path/to/2nd.wav spk2
path/to/3rd.wav spk1
:param spk_to_idx: dict, spk1:0, spk2:1, ...
:param min_mix:
:param max_mix:
:param batch_size:
:return:
"""
speaker_audios = {}
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_input_clean_fea = []
batch_target_spec = []
batch_input_len = []
batch_count = 0
while True:
mix_k = np.random.randint(min_mix, max_mix+1)
if mix_k > len(speaker_audios):
speaker_audios = {}
file_list = open(audio_list)
for line in file_list:
line = line.strip().split()
if len(line) != 2:
print 'Wrong audio list file record in the line:', line
continue
file_str, spk = line
if spk not in speaker_audios:
speaker_audios[spk] = []
speaker_audios[spk].append(file_str)
file_list.close()
for spk in speaker_audios:
random.shuffle(speaker_audios[spk])
wav_mix = None
target_spk = None
mix_len = 0
target_sig = None
for spk in random.sample(speaker_audios.keys(), mix_k):
file_str = speaker_audios[spk].pop()
if not speaker_audios[spk]:
del(speaker_audios[spk])
signal, rate = sf.read(file_str)
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
signal = resampy.resample(signal, rate, config.FRAME_RATE, filter='kaiser_best')
signal = list(signal)
if len(signal) > config.MAX_LEN:
signal = signal[:config.MAX_LEN]
if len(signal) > mix_len:
mix_len = len(signal)
signal = np.array(signal)
signal -= np.mean(signal)
signal /= np.max(np.abs(signal))
signal = list(signal)
if config.AUGMENT_DATA:
random_shift = random.sample(range(len(signal)), 1)[0]
signal = signal[random_shift:] + signal[:random_shift]
if len(signal) < config.MAX_LEN:
signal.extend(np.zeros(config.MAX_LEN - len(signal)))
signal = np.array(signal)
if wav_mix is None:
wav_mix = signal
target_sig = signal
target_spk = spk_to_idx[spk]
else:
wav_mix = wav_mix + signal
if config.IS_LOG_SPECTRAL:
feature_mix = np.log(np.transpose(np.abs(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
+ np.spacing(1))
else:
feature_mix = np.transpose(np.abs(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
spec_mix = np.transpose(np.abs(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT, window=config.WINDOWS)))
if config.IS_LOG_SPECTRAL:
feature_inp_clean = np.log(np.transpose(np.abs(librosa.core.spectrum.stft(target_sig, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
+ np.spacing(1))
else:
feature_inp_clean = np.transpose(np.abs(librosa.core.spectrum.stft(target_sig, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
spec_clean = np.transpose(np.abs(librosa.core.spectrum.stft(target_sig, config.FRAME_LENGTH,
config.FRAME_SHIFT, window=config.WINDOWS)))
batch_input_mix_fea.append(feature_mix)
batch_input_mix_spec.append(spec_mix)
batch_input_spk.append(target_spk)
batch_input_clean_fea.append(feature_inp_clean)
batch_target_spec.append(spec_clean)
batch_input_len.append(mix_len)
batch_count += 1
if batch_count == batch_size:
# mix_input_fea (batch_size, time_steps, feature_dim)
mix_input_fea = np.array(batch_input_mix_fea).reshape((batch_size, ) + feature_mix.shape)
# mix_input_spec (batch_size, time_steps, spectrum_dim)
mix_input_spec = np.array(batch_input_mix_spec).reshape((batch_size, ) + spec_mix.shape)
# target_input_spk (batch_size, 1)
target_input_spk = np.array(batch_input_spk, dtype=np.int32).reshape((batch_size, 1))
# clean_input_fea (batch_size, time_steps, feature_dim)
clean_input_fea = np.array(batch_input_clean_fea).reshape((batch_size, ) + feature_inp_clean.shape)
# clean_target_spec (batch_size, time_steps, spectrum_dim)
clean_target_spec = np.array(batch_target_spec).reshape((batch_size, ) + spec_clean.shape)
yield ({'input_mix_feature': mix_input_fea, 'input_mix_spectrum': mix_input_spec,
'input_target_spk': target_input_spk, 'input_clean_feature': clean_input_fea},
{'target_clean_spectrum': clean_target_spec})
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_input_clean_fea = []
batch_target_spec = []
batch_input_len = []
batch_count = 0
def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
original sampling rate of `y`
target_sr : number > 0 [scalar]
target sampling rate
res_type : str
resample type (see note)
.. note::
By default, this uses `resampy`'s high-quality mode ('kaiser_best').
To use a faster method, set `res_type='kaiser_fast'`.
To use `scipy.signal.resample`, set `res_type='fft'` or `res_type='scipy'`.
To use `scipy.signal.resample_poly`, set `res_type='polyphase'`.
.. note::
When using `res_type='polyphase'`, only integer sampling rates are
supported.
fix : bool
adjust the length of the resampled signal to be of size exactly
`ceil(target_sr * len(y) / orig_sr)`
scale : bool
Scale the resampled signal so that `y` and `y_hat` have approximately
equal total energy.
kwargs : additional keyword arguments
If `fix==True`, additional keyword arguments to pass to
`librosa.util.fix_length`.
Returns
-------
y_hat : np.ndarray [shape=(n * target_sr / orig_sr,)]
`y` resampled from `orig_sr` to `target_sr`
Raises
------
ParameterError
If `res_type='polyphase'` and `orig_sr` or `target_sr` are not both
integer-valued.
See Also
--------
librosa.util.fix_length
scipy.signal.resample
resampy.resample
Notes
-----
This function caches at level 20.
Examples
--------
Downsample from 22 KHz to 8 KHz
>>> y, sr = librosa.load(librosa.util.example_audio_file(), sr=22050)
>>> y_8k = librosa.resample(y, sr, 8000)
>>> y.shape, y_8k.shape
((1355168,), (491671,))
"""
# First, validate the audio buffer
util.valid_audio(y, mono=False)
if orig_sr == target_sr:
return y
ratio = float(target_sr) / orig_sr
n_samples = int(np.ceil(y.shape[-1] * ratio))
if res_type in ('scipy', 'fft'):
y_hat = scipy.signal.resample(y, n_samples, axis=-1)
elif res_type == 'polyphase':
if int(orig_sr) != orig_sr or int(target_sr) != target_sr:
raise ParameterError('polyphase resampling is only supported for integer-valued sampling rates.')
# For polyphase resampling, we need up- and down-sampling ratios
# We can get those from the greatest common divisor of the rates
# as long as the rates are integrable
orig_sr = int(orig_sr)
target_sr = int(target_sr)
gcd = np.gcd(orig_sr, target_sr)
y_hat = scipy.signal.resample_poly(y, target_sr // gcd, orig_sr // gcd, axis=-1)
else:
y_hat = resampy.resample(y, orig_sr, target_sr, filter=res_type, axis=-1)
if fix:
y_hat = util.fix_length(y_hat, n_samples, **kwargs)
if scale:
y_hat /= np.sqrt(ratio)
return np.ascontiguousarray(y_hat, dtype=y.dtype)
def eval_loss(model, audio_list, valid_test, epoch_num, log_file, spk_to_idx, batch_size=1, unk_spk=False):
if unk_spk:
batch_size = 1
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_target_spec = []
batch_input_len = []
batch_clean_wav = []
batch_count = 0
mse_loss = 0
file_list_len = 0
file_list = open(audio_list)
for line in file_list:
file_list_len += 1
file_list.close()
file_list = open(audio_list)
time_start = time.time()
for line_idx, line in enumerate(file_list):
line = line.strip().split()
if len(line) < 2:
raise Exception('Wrong audio list file record in the line:', ''.join(line))
file_tar_sounds_str = None
if not unk_spk:
file_tar_str, file_bg_str, tar_spk_str = line
file_bg_str = file_bg_str.strip().split(',')[0]
else:
file_tar_str, file_bg_str, tar_spk_str, file_tar_sounds_str = line
wav_mix = None
target_spk = None
mix_len = 0
target_sig = None
tar_supp_sig = None
for file_str in [file_tar_str, file_bg_str]:
signal, rate = sf.read(file_str)
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
signal = resampy.resample(signal, rate, config.FRAME_RATE, filter='kaiser_best')
signal = list(signal)
if len(signal) > config.MAX_LEN:
signal = signal[:config.MAX_LEN]
if len(signal) > mix_len:
mix_len = len(signal)
signal = np.array(signal)
signal -= np.mean(signal)
signal /= np.max(np.abs(signal))
signal = list(signal)
if len(signal) < config.MAX_LEN:
signal.extend(np.zeros(config.MAX_LEN - len(signal)))
signal = np.array(signal)
if wav_mix is None:
wav_mix = signal
target_sig = signal
if not unk_spk:
tar_supp_sig = signal
batch_clean_wav.append(tar_supp_sig)
target_spk = spk_to_idx[tar_spk_str]
else:
target_spk = 0
else:
wav_mix = wav_mix + signal
if unk_spk:
tmp_unk_spk_supp = 0
for file_str in file_tar_sounds_str.strip().split(','):
tmp_unk_spk_supp += 1
if tmp_unk_spk_supp > config.UNK_SPK_SUPP:
break
signal, rate = sf.read(file_str)
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
signal = resampy.resample(signal, rate, config.FRAME_RATE, filter='kaiser_best')
if tar_supp_sig is None:
tar_supp_sig = signal
else:
tar_supp_sig = tar_supp_sig + signal
batch_clean_wav.append(tar_supp_sig)
if config.IS_LOG_SPECTRAL:
feature_mix = np.log(np.transpose(np.abs(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
+ np.spacing(1))
else:
feature_mix = np.transpose(np.abs(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
spec_mix = np.transpose(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS))
spec_clean = np.transpose(np.abs(librosa.core.spectrum.stft(target_sig, config.FRAME_LENGTH,
config.FRAME_SHIFT, window=config.WINDOWS)))
batch_input_mix_fea.append(feature_mix)
batch_input_mix_spec.append(np.abs(spec_mix))
batch_input_spk.append(target_spk)
batch_target_spec.append(spec_clean)
batch_input_len.append(mix_len)
batch_count += 1
if (batch_count == batch_size) or (line_idx == (file_list_len-1)):
# mix_input_fea (batch_size, time_steps, feature_dim)
_tmp_batch_size = len(batch_input_mix_fea)
mix_input_fea = np.array(batch_input_mix_fea).reshape((_tmp_batch_size, ) + feature_mix.shape)
# mix_input_spec (batch_size, time_steps, spectrum_dim)
mix_input_spec = np.array(batch_input_mix_spec).reshape((_tmp_batch_size, ) + spec_mix.shape)
# target_input_spk (batch_size, 1)
target_input_spk = np.array(batch_input_spk).reshape((_tmp_batch_size, 1))
# clean_input_fea (batch_size, time_steps, feature_dim)
batch_input_clean_fea, inp_clean_shape = compute_batch_clean_fea(batch_clean_wav)
clean_input_fea = np.array(batch_input_clean_fea).reshape((_tmp_batch_size, ) + inp_clean_shape)
# clean_target_spec (batch_size, time_steps, spectrum_dim)
clean_target_spec = np.array(batch_target_spec).reshape((_tmp_batch_size, ) + spec_clean.shape)
if not unk_spk:
clean_input_fea = np.zeros_like(clean_input_fea)
mse_loss += model.evaluate({'input_mix_feature': mix_input_fea, 'input_mix_spectrum': mix_input_spec,
'input_target_spk': target_input_spk, 'input_clean_feature': clean_input_fea},
{'target_clean_spectrum': clean_target_spec}, batch_size=_tmp_batch_size,
verbose=0)
time_end = time.time()
print '\rCurrent evaluate:' + str(line_idx+1) + ' of ' + audio_list + \
' and cost time: %.4f sec.' % (time_end - time_start),
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_target_spec = []
batch_input_len = []
batch_clean_wav = []
batch_count = 0
print '\n[Epoch-%s: %d] - MSE Loss:%f' % \
(valid_test, epoch_num+1, mse_loss)
log_file.write('[Epoch-%s: %d] - MSE Loss:%f\n' %
(valid_test, epoch_num+1, mse_loss))
log_file.flush()
file_list.close()
return mse_loss
def eval_separation(model, audio_list, valid_test, epoch_num, log_file, spk_to_idx, batch_size=1, spk_num=2
, unk_spk=False, supp_time=1, add_bgd_noise=False):
if unk_spk:
batch_size = 1
if spk_num < 2:
spk_num = 2
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_input_len = []
batch_mix_spec = []
batch_mix_wav = []
batch_target_wav = []
batch_noise_wav = []
batch_clean_wav = []
batch_count = 0
batch_sdr_0 = []
batch_sir_0 = []
batch_sar_0 = []
batch_nsdr_0 = []
batch_sdr = []
batch_sir = []
batch_sar = []
batch_nsdr = []
file_list_len = 0
file_list = open(audio_list)
for line in file_list:
file_list_len += 1
file_list.close()
file_list = open(audio_list)
time_start = time.time()
for line_idx, line in enumerate(file_list):
line = line.strip().split()
if len(line) < 2:
raise Exception('Wrong audio list file record in the line:', ''.join(line))
file_tar_sounds_str = None
if not unk_spk:
# if not test unk_spk
file_tar_str, file_bg_str, tar_spk_str = line
file_bg_str = file_bg_str.strip().split(',')
else:
# if test unk_spk
file_tar_str, file_bg_str, tar_spk_str, file_tar_sounds_str = line
file_bg_str = [file_bg_str]
wav_mix = None
target_spk = None
mix_len = 0
target_sig = None
noise_sig = None
tar_supp_sig = None
for file_str in ([file_tar_str]+file_bg_str)[:spk_num]:
signal, rate = sf.read(file_str)
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
signal = resampy.resample(signal, rate, config.FRAME_RATE, filter='kaiser_best')
signal = list(signal)
if len(signal) > config.MAX_LEN:
signal = signal[:config.MAX_LEN]
if len(signal) > mix_len:
mix_len = len(signal)
signal = np.array(signal)
signal -= np.mean(signal)
signal /= np.max(np.abs(signal))
signal = list(signal)
if len(signal) < config.MAX_LEN:
signal.extend(np.zeros(config.MAX_LEN - len(signal)))
signal = np.array(signal)
if wav_mix is None:
wav_mix = signal
target_sig = signal
if not unk_spk:
tar_supp_sig = signal
batch_clean_wav.append(tar_supp_sig)
target_spk = spk_to_idx[tar_spk_str]
else:
# idx of unk_spk: 0
target_spk = 0
else:
wav_mix = wav_mix + signal
if noise_sig is None:
noise_sig = signal
else:
noise_sig = noise_sig + signal
if add_bgd_noise:
bg_noise = config.BGD_NOISE_WAV[:config.MAX_LEN]
bg_noise -= np.mean(bg_noise)
bg_noise /= np.max(np.abs(bg_noise))
wav_mix = wav_mix + bg_noise
noise_sig = noise_sig + bg_noise
if unk_spk:
tmp_unk_spk_supp = 0
for file_str in file_tar_sounds_str.strip().split(','):
tmp_unk_spk_supp += 1
if tmp_unk_spk_supp > config.UNK_SPK_SUPP:
break
signal, rate = sf.read(file_str)
if len(signal.shape) > 1:
signal = signal[:, 0]
if rate != config.FRAME_RATE:
signal = resampy.resample(signal, rate, config.FRAME_RATE, filter='kaiser_best')
signal = list(signal)
if tar_supp_sig is None:
tar_supp_sig = signal
else:
tar_supp_sig = tar_supp_sig + signal
if len(tar_supp_sig) < supp_time*config.FRAME_RATE:
raise Exception('the supp_time is too greater than the target supplemental sounds!')
batch_clean_wav.append(tar_supp_sig[:int(supp_time * config.FRAME_RATE)])
if config.IS_LOG_SPECTRAL:
feature_mix = np.log(np.abs(np.transpose(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
+ np.spacing(1))
else:
feature_mix = np.abs(np.transpose(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS)))
spec_mix = np.transpose(librosa.core.spectrum.stft(wav_mix, config.FRAME_LENGTH,
config.FRAME_SHIFT,
window=config.WINDOWS))
batch_input_mix_fea.append(feature_mix)
batch_input_mix_spec.append(np.abs(spec_mix))
batch_input_spk.append(target_spk)
batch_input_len.append(mix_len)
batch_mix_spec.append(spec_mix)
batch_mix_wav.append(wav_mix)
batch_target_wav.append(target_sig)
batch_noise_wav.append(noise_sig)
batch_count += 1
if (batch_count == batch_size) or (line_idx == (file_list_len-1)):
# mix_input_fea (batch_size, time_steps, feature_dim)
_tmp_batch_size = len(batch_input_mix_fea)
mix_input_fea = np.array(batch_input_mix_fea).reshape((_tmp_batch_size, ) + feature_mix.shape)
# mix_input_spec (batch_size, time_steps, spectrum_dim)
mix_input_spec = np.array(batch_input_mix_spec).reshape((_tmp_batch_size, ) + spec_mix.shape)
# bg_input_mask = np.array(batch_input_silence_mask).reshape((_tmp_batch_size, ) + spec_mix.shape)
# target_input_spk (batch_size, 1)
target_input_spk = np.array(batch_input_spk).reshape((_tmp_batch_size, 1))
# clean_input_fea (batch_size, time_steps, feature_dim)
batch_input_clean_fea, inp_clean_shape = compute_batch_clean_fea(batch_clean_wav)
clean_input_fea = np.array(batch_input_clean_fea).reshape((batch_size, ) + inp_clean_shape)
if not unk_spk:
clean_input_fea = np.log(np.zeros_like(clean_input_fea)+np.spacing(1))
target_pred = model.predict({'input_mix_feature': mix_input_fea, 'input_mix_spectrum': mix_input_spec,
'input_target_spk': target_input_spk, 'input_clean_feature': clean_input_fea})
batch_idx = 0
for _pred_output in list(target_pred):
_mix_spec = batch_mix_spec[batch_idx]
phase_mix = np.angle(_mix_spec)
_pred_spec = _pred_output * np.exp(1j * phase_mix)
_pred_wav = librosa.core.spectrum.istft(np.transpose(_pred_spec), config.FRAME_SHIFT,
window=config.WINDOWS)
_target_wav = batch_target_wav[batch_idx]
min_len = np.min((len(_target_wav), len(_pred_wav), batch_input_len[batch_idx]))
_pred_wav = _pred_wav[:min_len]
batch_target_wav[batch_idx] = _target_wav[:min_len]
batch_noise_wav[batch_idx] = batch_noise_wav[batch_idx][:min_len]
batch_mix_wav[batch_idx] = batch_mix_wav[batch_idx][:min_len]
mix_wav = matlab.double(batch_mix_wav[batch_idx].tolist())
target_wav = matlab.double(batch_target_wav[batch_idx].tolist())
noise_wav = matlab.double(batch_noise_wav[batch_idx].tolist())
pred_wav = matlab.double(_pred_wav.tolist())
if epoch_num == 0:
# BSS_EVAL (truth_signal, truth_noise, pred_signal, mix)
bss_eval_resuts = config.MAT_ENG.BSS_EVAL(target_wav, noise_wav, mix_wav, mix_wav)
batch_sdr_0.append(bss_eval_resuts['SDR'])
batch_sir_0.append(bss_eval_resuts['SIR'])
batch_sar_0.append(bss_eval_resuts['SAR'])
batch_nsdr_0.append(bss_eval_resuts['NSDR'])
if (line_idx < _tmp_batch_size) and (batch_idx == 0):
sf.write(config.TMP_PRED_WAV_FOLDER + '/test_pred_%s_ep%04d_bs%04d_idx%03d' %
(config.DATASET, (epoch_num+1), 1, (batch_idx+1)) +
'.wav', _pred_wav, config.FRAME_RATE)
# BSS_EVAL (truth_signal, truth_noise, pred_signal, mix)
bss_eval_resuts = config.MAT_ENG.BSS_EVAL(target_wav, noise_wav, pred_wav, mix_wav)
batch_sdr.append(bss_eval_resuts['SDR'])
batch_sir.append(bss_eval_resuts['SIR'])
batch_sar.append(bss_eval_resuts['SAR'])
batch_nsdr.append(bss_eval_resuts['NSDR'])
batch_idx += 1
time_end = time.time()
sdr = np.float(np.mean(batch_sdr))
sir = np.float(np.mean(batch_sir))
sar = np.float(np.mean(batch_sar))
nsdr = np.float(np.mean(batch_nsdr))
print '\rCurrent predict:' + str(line_idx+1) + ' of ' + audio_list + \
' and cost time: %.4f sec. - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f' % ((time_end - time_start),
sdr, sir, sar, nsdr),
if (line_idx+1) % 200 == 0:
log_file.write('Have evaluated %05d mixture wavs, and cost time: %.4f sec\n'
% ((line_idx+1), (time_end - time_start)))
log_file.flush()
batch_input_mix_fea = []
batch_input_mix_spec = []
batch_input_spk = []
batch_input_len = []
batch_mix_spec = []
batch_mix_wav = []
batch_target_wav = []
batch_noise_wav = []
batch_clean_wav = []
batch_count = 0
if epoch_num == 0:
sdr_0 = np.float(np.mean(batch_sdr_0))
sir_0 = np.float(np.mean(batch_sir_0))
sar_0 = np.float(np.mean(batch_sar_0))
nsdr_0 = np.float(np.mean(batch_nsdr_0))
print '\n[Epoch-%s: %d] - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f' % \
(valid_test, epoch_num, sdr_0, sir_0, sar_0, nsdr_0)
log_file.write('[Epoch-%s: %d] - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f\n' %
(valid_test, epoch_num, sdr_0, sir_0, sar_0, nsdr_0))
log_file.flush()
sdr = np.float(np.mean(batch_sdr))
sir = np.float(np.mean(batch_sir))
sar = np.float(np.mean(batch_sar))
nsdr = np.float(np.mean(batch_nsdr))
if epoch_num == 0:
print '[Epoch-%s: %d] - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f' % \
(valid_test, epoch_num+1, sdr, sir, sar, nsdr)
else:
print '\n[Epoch-%s: %d] - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f' % \
(valid_test, epoch_num+1, sdr, sir, sar, nsdr)
log_file.write('[Epoch-%s: %d] - GSDR:%f, GSIR:%f, GSAR:%f, GNSDR:%f\n' %
(valid_test, epoch_num+1, sdr, sir, sar, nsdr))
log_file.flush()
file_list.close()
