def convert(input_file, output_file, class_file):
r = DRF.create(input_file)
seg_set, score_mat = r.read(0, squeeze=True)
with open(class_file, 'r') as f:
model_set = [line.rstrip().split()[0] for line in f]
scores = TrialScores(model_set, seg_set, score_mat.T)
scores.save(output_file)
def extract_embed(seq_file, model_file, preproc_file, output_path,
max_seq_length, pooling_output, write_format, **kwargs):
set_float_cpu('float32')
sr_args = SDRF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SDRF.create(seq_file, transform=preproc, **sr_args)
t1 = time.time()
model = SeqQEmbed.load(model_file)
model.build(max_seq_length)
model.build_embed(pooling_output)
y_dim = model.embed_dim
_, seq_lengths = sr.read_num_rows()
sr.reset()
num_seqs = len(seq_lengths)
p1_y = np.zeros((num_seqs, y_dim), dtype=float_keras())
p2_y = np.zeros((num_seqs, y_dim), dtype=float_keras())
keys = []
for i in xrange(num_seqs):
ti1 = time.time()
key, data = sr.read(1)
ti2 = time.time()
logging.info('Extracting embeddings %d/%d for %s, num_frames: %d' %
(i, num_seqs, key[0], data[0].shape[0]))
keys.append(key[0])
p1_y[i], p2_y[i] = model.predict_embed(data[0])
ti4 = time.time()
logging.info('Elapsed time embeddings %d/%d for %s, total: %.2f read: %.2f, vae: %.2f' %
(i, num_seqs, key, ti4-ti1, ti2-ti1, ti4-ti2))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
if write_format == 'p1':
y = p1_y
elif write_format == 'p1+p2':
y = np.hstack((p1_y, p2_y))
else:
y = p2_y
hw = DWF.create(output_path)
hw.write(keys, y)
def compute_mfcc_feats(input_path, output_path,
compress, compression_method, write_num_frames, **kwargs):
mfcc_args = MFCC.filter_args(**kwargs)
mfcc = MFCC(**mfcc_args)
if mfcc.input_step == 'wave':
input_args = AR.filter_args(**kwargs)
reader = AR(input_path, **input_args)
else:
input_args = DRF.filter_args(**kwargs)
reader = DRF.create(input_path, **input_args)
writer = DWF.create(output_path, scp_sep=' ',
compress=compress,
compression_method=compression_method)
if write_num_frames is not None:
f_num_frames = open(write_num_frames, 'w')
for data in reader:
if mfcc.input_step == 'wave':
key, x, fs = data
else:
key, x = data
logging.info('Extracting MFCC for %s' % (key))
t1 = time.time()
y = mfcc.compute(x)
dt = (time.time() - t1)*1000
rtf = mfcc.frame_shift*y.shape[0]/dt
logging.info('Extracted MFCC for %s num-frames=%d elapsed-time=%.2f ms. real-time-factor=%.2f' %
(key, y.shape[0], dt, rtf))
writer.write([key], [y])
if write_num_frames is not None:
f_num_frames.write('%s %d\n' % (key, y.shape[0]))
mfcc.reset()
if write_num_frames is not None:
f_num_frames.close()
def compute_mfcc_feats(input_path, output_path, compress, compression_method,
write_num_frames, use_gpu, nn_model_path, chunk_size,
context, **kwargs):
#open device
if use_gpu and torch.cuda.is_available():
logging.info('CUDA_VISIBLE_DEVICES=%s' %
os.environ['CUDA_VISIBLE_DEVICES'])
logging.info('init gpu device')
device = torch.device('cuda', 0)
torch.tensor([0]).to(device)
else:
logging.info('init cpu device')
device = torch.device('cpu')
mfcc_args = MFCC.filter_args(**kwargs)
mfcc = MFCC(**mfcc_args)
# PUT YOUR NNET MODEL HERE!!!!
enhancer = CAN(num_channels=45)
enhancer.load_state_dict(
torch.load(nn_model_path, map_location=device)['state_dict'])
enhancer.to(device)
enhancer.eval()
if mfcc.input_step == 'wave':
input_args = AR.filter_args(**kwargs)
reader = AR(input_path, **input_args)
else:
input_args = DRF.filter_args(**kwargs)
reader = DRF.create(input_path, **input_args)
writer = DWF.create(output_path,
scp_sep=' ',
compress=compress,
compression_method=compression_method)
if write_num_frames is not None:
f_num_frames = open(write_num_frames, 'w')
for data in reader:
if mfcc.input_step == 'wave':
key, x, fs = data
else:
key, x = data
logging.info('Extracting filter-banks for %s' % (key))
t1 = time.time()
y = mfcc.compute(x)
#we apply dummy identity network to fb
logging.info('Running enhancement network')
y = apply_nnet(y, enhancer, chunk_size, context, device)
dt = (time.time() - t1) * 1000
rtf = mfcc.frame_shift * y.shape[0] / dt
logging.info(
'Extracted filter-banks for %s num-frames=%d elapsed-time=%.2f ms. real-time-factor=%.2f'
% (key, y.shape[0], dt, rtf))
writer.write([key], [y])
if write_num_frames is not None:
f_num_frames.write('%s %d\n' % (key, y.shape[0]))
mfcc.reset()
if write_num_frames is not None:
f_num_frames.close()
def extract_embed(seq_file, model_file, preproc_file, output_path, max_length,
layer_names, **kwargs):
set_float_cpu('float32')
sr_args = SDRF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SDRF.create(seq_file, transform=preproc, **sr_args)
t1 = time.time()
model = SeqEmbed.load(model_file)
model.build()
model.build_embed(layer_names)
y_dim = model.embed_dim
_, seq_lengths = sr.read_num_rows()
sr.reset()
num_seqs = len(seq_lengths)
max_length = np.minimum(np.max(seq_lengths), max_length)
y = np.zeros((num_seqs, y_dim), dtype=float_keras())
xx = np.zeros((1, max_length, model.x_dim), dtype=float_keras())
keys = []
for i in xrange(num_seqs):
ti1 = time.time()
data = sr.read(1)
key = data[0][0]
x = data[1][0]
ti2 = time.time()
logging.info('Extracting embeddings %d/%d for %s, num_frames: %d' %
(i, num_seqs, key, x.shape[0]))
keys.append(key)
xx[:, :, :] = 0
if x.shape[0] <= max_length:
xx[0, :x.shape[0]] = x
y[i] = model.predict_embed(xx, batch_size=1)
else:
num_chunks = int(np.ceil(float(x.shape[0]) / max_length))
chunk_size = int(np.ceil(float(x.shape[0]) / num_chunks))
for j in xrange(num_chunks - 1):
start = j * chunk_size
xx[0, :chunk_size] = x[start:start + chunk_size]
y[i] += model.predict_embed(xx, batch_size=1).ravel()
xx[0, :chunk_size] = x[-chunk_size:]
y[i] += model.predict_embed(xx, batch_size=1).ravel()
y[i] /= num_chunks
ti4 = time.time()
logging.info(
'Elapsed time embeddings %d/%d for %s, total: %.2f read: %.2f, vae: %.2f'
% (i, num_seqs, key, ti4 - ti1, ti2 - ti1, ti4 - ti2))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = DWF.create(output_path)
hw.write(keys, y)
def compute_mfcc_feats(input_path, output_path,
compress, compression_method, write_num_frames,
use_gpu, nn_model_path, chunk_size, context,
**kwargs):
#open device
if use_gpu and torch.cuda.is_available():
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
max_tries = 100
for g in range(max_tries):
try:
gpu_ids = find_free_gpus()
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_ids
logging.info('CUDA_VISIBLE_DEVICES=%s' % os.environ['CUDA_VISIBLE_DEVICES'])
logging.info('init gpu device')
device = torch.device('cuda', 0)
torch.tensor([0]).to(device)
break
except:
if g < max_tries-1:
logging.info('failing init gpu, trying again')
time.sleep(10)
else:
logging.info('failing init gpu, using cpu')
device = torch.device('cpu')
else:
logging.info('init cpu device')
device = torch.device('cpu')
mfcc_args1 = MFCC.filter_args(**kwargs)
mfcc_args2 = copy.deepcopy(mfcc_args1)
mfcc_args1['output_step'] = 'logfb'
mfcc_args2['input_step'] = 'logfb'
print(kwargs)
print(mfcc_args1)
print(mfcc_args2)
mfcc1 = MFCC(**mfcc_args1)
mfcc2 = MFCC(**mfcc_args2)
mvn = MVN(norm_var=False, left_context=150, right_context=150)
# PUT YOUR NNET MODEL HERE!!!!
enhancer = CGN()
#enhancer.load_state_dict(torch.load(nn_model_path, map_location=device)['state_dict'])
enhancer.load_state_dict(torch.load(nn_model_path, map_location=device))
enhancer.to(device)
enhancer.eval()
if mfcc1.input_step == 'wave':
input_args = AR.filter_args(**kwargs)
reader = AR(input_path, **input_args)
else:
input_args = DRF.filter_args(**kwargs)
reader = DRF.create(input_path, **input_args)
writer = DWF.create(output_path, scp_sep=' ',
compress=compress,
compression_method=compression_method)
if write_num_frames is not None:
f_num_frames = open(write_num_frames, 'w')
for data in reader:
if mfcc1.input_step == 'wave':
key, x, fs = data
else:
key, x = data
logging.info('Extracting filter-banks for %s' % (key))
t1 = time.time()
y = mfcc1.compute(x)
# separate logE and filterbanks
logE = y[:,0]
y = y[:,1:]
#estimate log energy from filterbanks
logEy1 = logsumexp(y, axis=-1)
#we apply dummy identity network to fb
logging.info('Running enhancement network')
y = mvn.normalize(y)
y = apply_nnet(y, enhancer, chunk_size, context, device)
#lets rescale the logE based on enhanced filterbanks
logEy2 = logsumexp(y, axis=-1)
logE = logE + (logEy2 - logEy1)
# concatenate logE and filterbanks
y = np.concatenate((logE[:,None], y), axis=-1)
#apply DCT
logging.info('Applying DCT')
y = mfcc2.compute(y)
dt = (time.time() - t1)*1000
rtf = mfcc1.frame_shift*y.shape[0]/dt
logging.info('Extracted filter-banks for %s num-frames=%d elapsed-time=%.2f ms. real-time-factor=%.2f' %
(key, y.shape[0], dt, rtf))
writer.write([key], [y])
if write_num_frames is not None:
f_num_frames.write('%s %d\n' % (key, y.shape[0]))
mfcc1.reset()
if write_num_frames is not None:
f_num_frames.close()
mfcc1 = MFCC(**mfcc_args1)
mfcc2 = MFCC(**mfcc_args2)
# PUT YOUR NNET MODEL HERE!!!!
enhancer = CGN()
#enhancer.load_state_dict(torch.load(nn_model_path, map_location=device)['state_dict'])
enhancer.load_state_dict(torch.load(nn_model_path, map_location=device))
enhancer.to(device)
enhancer.eval()
if mfcc1.input_step == 'wave':
input_args = AR.filter_args(**kwargs)
reader = AR(input_path, **input_args)
else:
input_args = DRF.filter_args(**kwargs)
reader = DRF.create(input_path, **input_args)
writer = DWF.create(output_path, scp_sep=' ',
compress=compress,
compression_method=compression_method)
if write_num_frames is not None:
f_num_frames = open(write_num_frames, 'w')
for data in reader:
if mfcc1.input_step == 'wave':
key, x, fs = data
else:
key, x = data
logging.info('Extracting filter-banks for %s' % (key))
t1 = time.time()