def __getitem__(self,index):
while True:
notnoise = 1
# Clean files
if len(self.clean_files) != 0:
# Randomly sample a clean file
f = random.choice(self.clean_files)
fs,audio = read('{}{}/{}_{}.wav'.format(self.clean_root_dir,'clean',f,'clean'))
audio = audio.astype('float32')
# Randomly sample a clean clip
r = random.random()
if r < self.pure_noise and self.flag == 'train':
normalized_clean = torch.zeros(LEN*self.sr).float()
notnoise = 0
else:
start = random.randint(START*fs,len(audio)-LEN*fs)
clip = resample(audio[start:start+LEN*fs],fs,self.sr)/1e5
if r >= self.pure_noise and np.sum(clip**2) < self.threshold and self.flag == 'train':
continue
mu, sigma = np.mean(clip), np.std(clip)
normalized_clean = torch.from_numpy((clip-mu)/sigma)
# Noise files
if len(self.noise_files) != 0:
nf = random.choice(self.noise_files)
audio_noise, fs = sf.read(nf)
if len(audio_noise.shape) > 1:
audio_noise = np.mean(audio_noise,axis=1)
audio_noise = audio_noise.astype('float32')
# Randomly sample a clip of noise
if len(audio_noise) < LEN*fs: continue
start = random.randint(0,len(audio_noise)-LEN*fs)
clip_noise = resample(audio_noise[start:start+LEN*fs],fs,self.sr)
mu_noise, sigma_noise = np.mean(clip_noise), np.std(clip_noise)
normalized_noise = torch.from_numpy((clip_noise-mu_noise)/(sigma_noise+EPS))
# Mix the noise with the clean audio clip at given SNR level
interference = 10**(-self.snr/20)*normalized_noise
if r < self.pure_noise and self.flag == 'train':
mixture = interference
else:
mixture = normalized_clean + interference
mu_mixture, sigma_mixture = torch.mean(mixture), torch.std(mixture)
mixture = (mixture-mu_mixture) / sigma_mixture
if len(self.noise_files) != 0:
if self.flag == 'train':
return mixture, normalized_clean, notnoise
if self.flag == 'test':
return mixture, normalized_clean
return normalized_clean
def SaveSpectrogram(y_mix, y_vocal, y_inst, fname, original_sr=44100):
"""extract features and save"""
y_mix = resample(y_mix, original_sr, C.SR)
y_vocal = resample(y_vocal, original_sr, C.SR)
y_inst = resample(y_inst, original_sr, C.SR)
S_mix = np.abs(stft(y_mix, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
S_vocal = np.abs(stft(y_vocal, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
S_inst = np.abs(stft(y_inst, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
norm = S_mix.max()
S_mix /= norm
S_vocal /= norm
S_inst /= norm
# np.savez(os.path.join(C.PATH_FFT, fname+".npz"), mix=S_mix, vocal=S_vocal, inst=S_inst)
# Generate sequence (1,512,128) and save
cnt = 1
i = 0
while i + C.PATCH_LENGTH < S_mix.shape[1]:
mix_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
#vocal_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
inst_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
mix_spec[0, :, :] = S_mix[1:, i:i + C.PATCH_LENGTH]
#vocal_spec[0, :, :] = S_vocal[1:, i:i + C.PATCH_LENGTH]
inst_spec[0, :, :] = S_inst[1:, i:i + C.PATCH_LENGTH]
np.savez(os.path.join(C.VAL_PATH_FFT, fname + str(cnt) + ".npz"),
data=mix_spec,
label=inst_spec)
i += C.PATCH_LENGTH
cnt += 1
if S_mix.shape[1] >= 128:
mix_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
#vocal_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
inst_spec = np.zeros((1, 512, C.PATCH_LENGTH), dtype=np.float32)
mix_spec[0, :, :] = S_mix[1:, S_mix.shape[1] -
C.PATCH_LENGTH:S_mix.shape[1]]
#vocal_spec[0, :, :] = S_vocal[1:, S_mix.shape[1] - C.PATCH_LENGTH:S_mix.shape[1]]
inst_spec[0, :, :] = S_inst[1:, S_mix.shape[1] -
C.PATCH_LENGTH:S_mix.shape[1]]
np.savez(os.path.join(C.VAL_PATH_FFT, fname + str(cnt) + ".npz"),
data=mix_spec,
label=inst_spec)
cnt += 1
def apply(self, sample, clock=0.0):
# late binding librosa and its dependencies
# pre-importing sklearn fixes https://github.com/scikit-learn/scikit-learn/issues/14485
import sklearn # pylint: disable=import-outside-toplevel
from librosa.core import resample # pylint: disable=import-outside-toplevel
sample.change_audio_type(new_audio_type=AUDIO_TYPE_NP)
rate = pick_value_from_range(self.rate, clock=clock)
audio = sample.audio
orig_len = len(audio)
audio = np.swapaxes(audio, 0, 1)
audio = resample(audio, sample.audio_format.rate, rate)
audio = resample(audio, rate, sample.audio_format.rate)
audio = np.swapaxes(audio, 0, 1)[0:orig_len]
sample.audio = audio
def validate(hp, args, generator, discriminator, valloader, writer, step):
generator.eval()
discriminator.eval()
torch.backends.cudnn.benchmark = False
loader = tqdm.tqdm(valloader, desc='Validation loop')
loss_g_sum = 0.0
loss_d_sum = 0.0
for mel, audio in loader:
mel = mel.cuda()
audio = audio.cuda()
# generator
fake_audio = generator(mel)
disc_fake = discriminator(fake_audio[:, :, :audio.size(2)])
disc_real = discriminator(audio)
loss_g = 0.0
loss_d = 0.0
for (feats_fake,
score_fake), (feats_real,
score_real) in zip(disc_fake, disc_real):
loss_g += torch.mean(
torch.sum(torch.pow(score_fake - 1.0, 2), dim=[1, 2]))
for feat_f, feat_r in zip(feats_fake, feats_real):
loss_g += hp.model.feat_match * torch.mean(
torch.abs(feat_f - feat_r))
loss_d += torch.mean(
torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
loss_d += torch.mean(
torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
loss_g_sum += loss_g.item()
loss_d_sum += loss_d.item()
loss_g_avg = loss_g_sum / len(valloader.dataset)
loss_d_avg = loss_d_sum / len(valloader.dataset)
audio = audio[0][0].cpu().detach().numpy()
fake_audio = fake_audio[0][0].cpu().detach().numpy()
audio_16k = resample(audio, hp.audio.sampling_rate, 16000)
fake_audio_16k = resample(fake_audio, hp.audio.sampling_rate, 16000)
pesq_score = pesq(16000, audio_16k, fake_audio_16k, 'wb')
writer.log_validation(loss_g_avg, loss_d_avg, pesq_score, generator,
discriminator, audio, fake_audio, step)
torch.backends.cudnn.benchmark = True
def preprocess_audio(self, x, audio_fps):
x = ap.to_mono(x.numpy())
if audio_fps != self.audio_fps:
x = ap.resample(x, audio_fps, self.audio_fps)
if x.shape[0] < self.audio_fps:
x = np.pad(x, (0, self.audio_fps - x.shape[0]))
return x.reshape((1, -1))
def generate_cqt(file_path, st_status):
st_status.text('Opening {}'.format(file_path))
data, sample_rate = auto_load(file_path, sr=None)
print('Sample Rate:', sample_rate, 'shape:', data.shape)
if len(data.shape) == 2:
print('Converting to mono channel...')
data = to_mono(data)
st_status.text('Resampling to {} Hz...'.format(TARGET_SAMPLE_RATE))
downsampled_data = resample(data,
orig_sr=sample_rate,
target_sr=TARGET_SAMPLE_RATE)
# downsampled_data = data
st_status.text('Downsampled to {} Hz, shape is now {}'.format(
TARGET_SAMPLE_RATE, downsampled_data.shape))
st_status.text('Generating CQT...')
cqt_result = np.abs(
cqt(downsampled_data,
sr=TARGET_SAMPLE_RATE,
hop_length=HOP_LENGTH,
n_bins=TOTAL_BINS,
bins_per_octave=BINS_PER_OCTAVE))
return cqt_result
def generate_cqt(i, file_path, offset=0, duration=None):
print('[{}] Opening'.format(i), file_path)
data, sample_rate = load(file_path,
sr=None,
offset=offset,
duration=duration)
print('[{}] Sample Rate:'.format(i), sample_rate, 'shape:', data.shape)
if len(data.shape) == 2:
with Timer('[{}] Converted to mono'.format(i)):
print('[{}] Converting to mono channel...'.format(i))
data = to_mono(data)
with Timer('[{}] Resampling'.format(i)):
print('[{}] Resampling to'.format(i), TARGET_SAMPLE_RATE, 'Hz...')
downsampled_data = resample(data,
orig_sr=sample_rate,
target_sr=TARGET_SAMPLE_RATE)
# downsampled_data = data
print('[{}] Downsampled to'.format(i), TARGET_SAMPLE_RATE,
'Hz shape is now', downsampled_data.shape)
with Timer('[{}] CQT'.format(i)):
print('[{}] Generating CQT...'.format(i))
cqt_result = np.abs(
cqt(downsampled_data,
sr=TARGET_SAMPLE_RATE,
hop_length=HOP_LENGTH,
n_bins=TOTAL_BINS,
bins_per_octave=BINS_PER_OCTAVE))
return cqt_result
def separate(PATH_INPUT, PATH_OUTPUT, MODEL, SR=16000, FFT_SIZE = 1024, H = 512):
if os.path.isdir( PATH_INPUT):
# 入力がディレクトリーの場合、ファイルリストをつくる
filelist_mixdown = find_files(PATH_INPUT, ext="wav", case_sensitive=True)
else:
# 入力が単一ファイルの場合
filelist_mixdown=[PATH_INPUT]
print ('number of mixdown file', len(filelist_mixdown))
# 出力用のディレクトリーがない場合は 作成する。
_, path_output_ext = os.path.splitext(PATH_OUTPUT)
print ('path_output_ext',path_output_ext)
if len(path_output_ext)==0 and not os.path.exists(PATH_OUTPUT):
os.mkdir(PATH_OUTPUT)
# モデルの読み込み
unet = train.UNet()
chainer.serializers.load_npz( MODEL,unet)
config.train = False
config.enable_backprop = False
# ミックスされたものを読み込み、vocal(speech)の分離を試みる
for fmixdown in filelist_mixdown:
# audioread でエラーが発生した場合は、scipyを使う。
try:
y_mixdown, _ = load(fmixdown, sr=SR, mono=True)
except:
sr_mixdown, y_mixdown = read(fmixdown)
if not sr_mixdown == SR:
y_mixdown = resample(y_mixdown, sr_mixdown, SR)
# 入力の短時間スペクトラムを計算して、正規化する。
spec = stft(y_mixdown, n_fft=FFT_SIZE, hop_length=H, win_length=FFT_SIZE)
mag = np.abs(spec)
mag /= np.max(mag)
phase = np.exp(1.j*np.angle(spec))
print ('mag.shape', mag.shape)
start = 0
end = 128 * (mag.shape[1] // 128) # 入力のフレーム数以下で、networkの定義に依存して 適切な値を選ぶこと。
# speech(vocal)を分離するためのマスクを求める
mask = unet(mag[:, start:end][np.newaxis, np.newaxis, 1:, :]).data[0, 0, :, :]
mask = np.vstack((np.zeros(mask.shape[1], dtype="float32"), mask))
# 入力の短時間スペクトラムにマスクを掛けて、逆FFTで波形を合成する。
mag2=mag[:, start:end]*mask
phase2=phase[:, start:end]
y = istft(mag2*phase2, hop_length=H, win_length=FFT_SIZE)
# 分離した speech(vocal)を出力ファイルとして保存する。
if len(path_output_ext)==0:
# ディレクトリーへ出力
foutname, _ = os.path.splitext( os.path.basename(fmixdown) )
fname= os.path.join(PATH_OUTPUT, (foutname + '.wav'))
else:
# 指定されたファイルへ出力
fname= PATH_OUTPUT
print ('saving... ', fname)
write_wav(fname, y, SR, norm=True)
def LoadAudio(fname):
y, sr = load(fname, sr=None)
if sr != C.SR:
y = resample(y, sr, C.SR)
spec = stft(y, n_fft=C.FFT_SIZE, hop_length=C.H, win_length=C.FFT_SIZE)
mag = np.abs(spec)
mag /= np.max(mag)
phase = np.exp(1.j * np.angle(spec))
return mag, phase
def transpose(audio_data, rate=1.0, mul=1.0, **kwargs):
"""
see https://librosa.github.io/librosa/generated/librosa.core.resample.html
"""
return resample(
y=audio_data,
orig_sr=44100.0, # dummy value for resampler
target_sr=44100.0 / rate,
**kwargs) * mul
def apply(self, sample, clock=0.0):
# late binding librosa and its dependencies
# pre-importing sklearn fixes https://github.com/scikit-learn/scikit-learn/issues/14485
from librosa.core import resample # pylint: disable=import-outside-toplevel
sample.change_audio_type(new_audio_type=AUDIO_TYPE_NP)
rate = pick_value_from_range(self.rate, clock=clock)
audio = sample.audio
orig_len = len(audio)
audio = np.swapaxes(audio, 0, 1)
if audio.shape[0] < 2:
# since v0.8 librosa enforces a shape of (samples,) instead of (channels, samples) for mono samples
resampled = resample(audio[0], sample.audio_format.rate, rate)
audio[0] = resample(resampled, rate, sample.audio_format.rate)[:orig_len]
else:
audio = resample(audio, sample.audio_format.rate, rate)
audio = resample(audio, rate, sample.audio_format.rate)
audio = np.swapaxes(audio, 0, 1)[0:orig_len]
sample.audio = audio
def SaveSpectrogram(y_mix, y_vocal, y_inst, fname, original_sr=44100):
y_mix = resample(y_mix, original_sr, C.SR)
y_vocal = resample(y_vocal, original_sr, C.SR)
y_inst = resample(y_inst, original_sr, C.SR)
S_mix = np.abs(
stft(y_mix, n_fft=C.FFT_SIZE, hop_length=C.H)).astype(np.float32)
S_vocal = np.abs(
stft(y_vocal, n_fft=C.FFT_SIZE, hop_length=C.H)).astype(np.float32)
S_inst = np.abs(
stft(y_inst, n_fft=C.FFT_SIZE, hop_length=C.H)).astype(np.float32)
norm = S_mix.max()
S_mix /= norm
S_vocal /= norm
S_inst /= norm
np.savez(os.path.join(C.PATH_FFT, fname+".npz"),
mix=S_mix, vocal=S_vocal, inst=S_inst)
def __getitem__(self,index):
while True:
notnoise = 1
# Randomly sample a file
f = random.choice(self.files)
fs, audio = read('{}{}/{}_{}.wav'.format(self.root_dir,self.version[0],f,self.version[0]))
audio = audio.astype('float32')
# Randomly sample a clip
r = random.random()
is_silence = False
if r < self.pure_noise and self.flag == 'train':
start = random.randint(0, START*fs-LEN*fs)
is_silence = True
notnoise = 0
else:
start = random.randint(START*fs,len(audio)-LEN*fs)
# Resample the clip
clip = resample(audio[start:start+LEN*fs],fs,self.sr) / 1e5
# Thresholding: discard clip if the clip contains too much silence
if not is_silence and np.sum(clip**2) < self.threshold:
continue
# Normalize the clip
mu, sigma = np.mean(clip), np.std(clip)
normalized = torch.from_numpy((clip-mu)/sigma)
if len(self.version) > 1:
fs, audio_clean = read('{}{}/{}_{}.wav'.format(self.root_dir,self.version[1],f,self.version[1]))
audio_clean = audio_clean.astype('float32')
# Extract the corresponding clean clip
if is_silence:
normalized_clean = torch.zeros(LEN*self.sr).float()
else:
clip_clean = resample(audio_clean[start:start+LEN*fs],fs,self.sr)
mu_clean, sigma_clean = np.mean(clip_clean), np.std(clip_clean)
normalized_clean = torch.from_numpy((clip_clean-mu_clean)/sigma_clean)
if self.flag == 'train':
return normalized, normalized_clean, notnoise
else:
return normalized, normalized_clean
return normalized
def LoadAudio_Arg(fname, pitch_shift, time_stretch):
y, sr = load(fname, sr=C.SR)
if sr != C.SR:
y = resample(y, sr, C.SR)
y = pitch_shift(y, C.SR, pitch_shift)
y = time_stretch(y, time_stretch)
spec = stft(y, n_fft=C.FFT_SIZE, hop_length=C.H, win_length=C.FFT_SIZE)
mag = np.abs(spec)
mag /= np.max(mag)
phase = np.exp(1.j * np.angle(spec))
return mag, phase
def downsample_mono(path, sr):
rate, wav = wavfile.read(path)
wav = resample(wav.astype(np.float32), rate, sr)
wav = wav.astype(np.int16)
# checks stereo and converts to mono if nessesary
try:
tmp = wav.shape[1]
wav = wav[:, 0] + wav[:, 1] / 2
except:
pass
return sr, wav
def downsample_mono(path, sr):
rate, wav = wavfile.read(path)
wav = wav.astype(np.float32, order='F')
try:
tmp = wav.shape[1]
wav = to_mono(wav.T)
except:
pass
wav = resample(wav, rate, sr)
wav = wav.astype(np.int16)
return sr, wav
def SaveSpectrogram(y_mix, y_vocal, y_inst, fname, original_sr=44100):
y_mix = resample(y_mix, original_sr, C.SR)
y_vocal = resample(y_vocal, original_sr, C.SR)
y_inst = resample(y_inst, original_sr, C.SR)
S_mix = np.abs(stft(y_mix, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
S_vocal = np.abs(stft(y_vocal, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
S_inst = np.abs(stft(y_inst, n_fft=C.FFT_SIZE,
hop_length=C.H)).astype(np.float32)
norm = S_mix.max()
S_mix /= norm
S_vocal /= norm
S_inst /= norm
np.savez(os.path.join(C.PATH_FFT, fname + ".npz"),
mix=S_mix,
vocal=S_vocal,
inst=S_inst)
def from_flac_to_tfrecords(train_r=0.8, valid_test_r=0.2):
# Extract the information about this subset (speakers, chapters)
# Dictionary with the following shape:
# {speaker_key: {chapters: [...], sex:'M/F', ... } }
folder = config.data_root+'/'+config.data_subset
speakers_info = data_tools.read_metadata(config.data_subset)
keys_to_index = {}
for i, key in enumerate(speakers_info.keys()):
keys_to_index[key] = i
sex = ['M' for i in range(len(speakers_info))]
for k, v in speakers_info.items():
i = keys_to_index[k]
sex[i] = v['sex']
np.save('genders_index.arr', sex)
# exit()
allfiles = np.array([os.path.join(r,f) for r,dirs,files in os.walk(folder) for f in files if f.endswith(".flac")])
L = len(allfiles)
np.random.shuffle(allfiles)
train = allfiles[:int(L*train_r)]
valid = allfiles[int(L*train_r):int(L*(train_r+valid_test_r/2))]
test = allfiles[int(L*(train_r+valid_test_r/2)):]
print len(train), len(valid), len(test)
for group_name, data_split in [("train", train),("test", test), ("valid", valid)]:
for s in ['M', 'F']:
writer = tf.python_io.TFRecordWriter(group_name + '_' + s +'.tfrecords')
for file in data_split:
splits = file.split('/')
key = splits[-3]
sex = speakers_info[key]['sex']
if sex == s:
raw_audio, sr = load(file, sr=16000)
raw_audio = resample(raw_audio, sr, config.fs)
raw_audio = raw_audio.astype(np.float32).tostring()
feature = tf.train.Example(features=tf.train.Features(
feature = { 'audio' : tf.train.Feature(bytes_list=tf.train.BytesList(value=[raw_audio])),
'key' : tf.train.Feature(int64_list=tf.train.Int64List(value=[keys_to_index[key]]))
}))
print group_name, s, key, keys_to_index[key]
writer.write(feature.SerializeToString())
writer.close()
def save_mp3(path, y, sr):
if sr != 44100:
y = core.resample(y, sr, 44100)
yint = (y / np.max(y) * 0.49 * (2**16)).astype(np.int16)
audio = AudioSegment(data=yint,
frame_rate=44100,
sample_width=2,
channels=1)
audio.export(path + '.mp3', format='mp3')
return
def downsample_mono(path, sr):
obj = wavio.read(path)
wav = obj.data.astype(np.float32, order='F')
rate = obj.rate
try:
# tmp = wav.shape[1]
wav = to_mono(wav.T)
except:
pass
wav = resample(wav, rate, sr)
wav = wav.astype(np.int16)
return sr, wav
def preprocess_noise(noise_audio, fs_noise, fs):
"""[summary]
Args:
noise_list ([type]): [description]
Returns:
[type]: [description]
"""
# Downsample to 16kHz
if fs != fs_noise:
noise_audio_resamp = resample(noise_audio, fs_noise, fs)
return noise_audio_resamp
def set_samplerate(datadir, words, samplerate):
# only check samplerate for files in word list
wav_files = [f for word in words for f in glob.glob(os.path.join('data', word,'*.wav'))]
print(f'Verifying data is at {samplerate} Hz')
for wav_file in tqdm(wav_files):
samples, sr = sf.read(wav_file)
if sr != samplerate:
samples = resample(samples, sr, samplerate)
sf.write(wav_file, samples, samplerate)
print(f'All Data sampled at {samplerate} Hz')
def downsample_mono(path, sr):
rate, wav = wavfile.read(path)
# print(type(wav))
# np.asfortranarray(wav[0,0:Length-1,:].copy())
wav = resample(wav.astype(np.float32), rate, sr)
# print(type(wav))
wav = wav.astype(np.int16)
# checks stereo and converts to mono if nessesary
try:
tmp = wav.shape[1]
wav = wav[:, 0] + wav[:, 1] / 2
except:
pass
return sr, wav
def SaveSpectrogramA(y_mix,
y_target,
fname,
original_sr=44100,
generate_high_data=False):
if original_sr != C.SR:
y_mix = resample(y_mix, original_sr, C.SR)
y_target = resample(y_target, original_sr, C.SR)
S_mix = np.abs(
stft(y_mix, n_fft=C.FFT_SIZE, hop_length=C.H,
window=C.WINDOW)).astype(np.float32)
S_target = np.abs(
stft(y_target, n_fft=C.FFT_SIZE, hop_length=C.H,
window=C.WINDOW)).astype(np.float32)
S_mix_low = S_mix[:C.SP]
S_target_low = S_target[:C.SP]
norm = S_mix_low.max()
S_mix_low /= norm
S_target_low /= norm
np.savez(C.PATH_TRAINDATA / (fname + "-low.npz"),
mix=S_mix_low,
target=S_target_low)
del S_mix_low, S_target_low
if generate_high_data:
S_mix_high = S_mix[C.SP:C.SP + C.SP]
S_target_high = S_target[C.SP:C.SP + C.SP]
norm = S_mix_high.max()
S_mix_high /= norm
S_target_high /= norm
np.savez(C.PATH_TRAINDATA / (fname + "-high.npz"),
mix=S_mix_high,
target=S_target_high)
def save_as_torch_file(self):
"""Download the yesno data if it doesn't exist in processed_folder already."""
import tarfile
if self._check_exists():
return
raw_abs_dir = os.path.join(self.root, self.raw_folder)
processed_abs_dir = os.path.join(self.root, self.processed_folder)
# dset_abs_path = os.path.join(
# self.root, self.raw_folder)
dset_abs_path = YOUTUBE_PIANOS_RAW
# process and save as torch files
print('Processing...')
# shutil.copyfile(
# os.path.join(dset_abs_path, "README"),
# os.path.join(processed_abs_dir, "YESNO_README")
# )
audios = [x for x in os.listdir(dset_abs_path) if ".wav" in x]
print("Found {} audio files".format(len(audios)))
tensors = []
labels = []
lengths = []
for i, f in enumerate(audios):
if i >= self.dataset_size:
break
print("Reading: {0}".format(f))
full_path = os.path.join(dset_abs_path, f)
sig, sr = read_audio(full_path, 44100)
sig = resample(sig.numpy(), sr, self.sample_rate)
sig = sig.reshape((1, -1))
sig = torch.FloatTensor(sig)
tensors.append(sig)
lengths.append(sig.size(1))
labels.append(os.path.basename(f).split(".", 1)[0].split("_"))
# sort sigs/labels: longest -> shortest
tensors, labels = zip(*[(b, c) for (a, b, c) in sorted(
zip(lengths, tensors, labels), key=lambda x: x[0], reverse=True)])
self.max_len = tensors[0].size(1)
torch.save((tensors, labels),
os.path.join(self.processed_folder, self.processed_file))
print('Done!')
def create_raw_audio_dataset(output_fn,
subset=config.data_subset,
data_root=config.data_root):
"""
Create a H5 file from the LibriSpeech dataset and the subset given:
Inputs:
output_fn: filename for the created file
subset: LibriSpeech subset : 'dev-clean' , ...
data_root: LibriSpeech folder path
"""
from librosa.core import resample, load
# Extract the information about this subset (speakers, chapters)
# Dictionary with the following shape:
# {speaker_key: {chapters: [...], sex:'M/F', ... } }
speakers_info = data_tools.read_metadata(subset)
with h5py.File(output_fn, 'w') as data_file:
for key, elements in tqdm(speakers_info.items(),
total=len(speakers_info),
desc='Speakers'):
if key not in data_file:
# Create an H5 Group for each key/speaker
data_file.create_group(key)
# Current speaker folder path
folder = data_root + '/' + subset + '/' + key
# For all the chapters read by this speaker
for i, chapter in enumerate(
tqdm(elements['chapters'], desc='Chapters')):
# Find all .flac audio
for root, dirs, files in os.walk(folder + '/' + chapter):
for file in tqdm(files, desc='Files'):
if file.endswith(".flac"):
path = os.path.join(root, file)
raw_audio, sr = load(path, sr=16000)
raw_audio = resample(raw_audio, sr, config.fs)
data_file[key].create_dataset(
file,
shape=raw_audio.shape,
data=raw_audio,
chunks=raw_audio.shape,
maxshape=raw_audio.shape,
compression="gzip",
compression_opts=9)
print 'Dataset for the subset: ' + subset + ' has been built'
def downsample(path, down_sample):
sample_rate, wave = wavfile.read(path)
wave = wave.astype(np.float32, order='F')
## wave, sample_rate = librosa.load(path, sr = args.down_sample, mono=True)
try:
tmp = wave.shape[1]
wave = to_mono(wave.T)
except:
pass
wave = resample(wave, sample_rate, down_sample)
wave = wave.astype(np.int16)
return wave, down_sample
def read_wav(wav_path, fs):
"""Read a single-channel wav file from given path. Perform resampling and amp normalization
:param wav_path: Path where the single-channel wav file is located
:param fs: Desired sampling rate
:return: Amp normalized wav at specified sampling rate
"""
fs_wav, wav = wavfile.read(wav_path)
wav = wav / np.max(np.abs(wav))
if fs_wav != fs:
warnings.warn("Sampling rate of wav file is not ", fs, ". Will be resampled")
wav = resample(wav, wav, fs)
return fs_wav, wav / np.max(np.abs(wav))
def read_test_data():
data = np.empty((4512, 862, 40))
for i in np.arange(4512):
audio = np.load('audio/' + str(i) + '.npy')
# Resampling to 44100
audio = core.resample(audio, orig_sr=48000, target_sr=44100)
audio = audio * 1 / np.max(np.abs(audio))
spec = melspectrogram(y=audio,
sr=44100,
n_fft=1024,
hop_length=512,
n_mels=40)
spec = spec.T
data[i, :, :] = spec
return data
def vad(data, fs, fs_vad=16000, hop_length=30, vad_mode=0):
# # check data
# if data.dtype.kind == 'i':
# if data.max() > 2**15 - 1 or data.min() < -2**15:
# raise ValueError(
# 'When data.type is int, data must be -32768 < data < 32767.')
# data = data.astype('f') / 2.0**15
# elif data.dtype.kind == 'f':
# if np.abs(data).max() > 1:
# raise ValueError(
# 'When data.type is float, data must be -1.0 <= data <= 1.0.')
# data = data.astype('f')
# else:
# raise ValueError('data.dtype must be int or float.')
data = data.squeeze()
if not data.ndim == 1:
raise ValueError('data must be mono (1 ch).')
# resampling
if fs != fs_vad:
resampled = resample(data, fs, fs_vad)
if np.abs(resampled).max() > 1.0:
resampled *= (0.99 / np.abs(resampled).max())
# warn('Resampling causes data clipping. data was rescaled.')
else:
resampled = data
resampled = (resampled * 2.0**15).astype('int16')
hop = fs_vad * hop_length // 1000
framelen = resampled.size // hop + 1
padlen = framelen * hop - resampled.size
paded = np.lib.pad(resampled, (0, padlen), 'constant', constant_values=0)
framed = frame(paded, frame_length=hop, hop_length=hop).T
vad = webrtcvad.Vad()
vad.set_mode(vad_mode)
valist = [vad.is_speech(tmp.tobytes(), fs_vad) for tmp in framed]
hop_origin = fs * hop_length // 1000
va_framed = np.zeros([len(valist), hop_origin])
va_framed[valist] = 1
return va_framed.reshape(-1)[:data.size]
def load_audio_file(file_path):
data_l = os.listdir(file_path)
input_length = 16000
x = 1
for i in data_l:
rate, data_in = wavfile.read(file_path + i)
data_in = data_in.astype(np.float32, order='F')
try:
tmp = data_in.shape[1]
data_in = to_mono(data_in.T)
except:
pass
data_in = resample(data_in, rate, 16000)
data_in = data_in.astype(np.float32)
data_ap.append(data_in)
x += 1
return data_ap
