def extract_ivectors_single(self, ubm, stat_server, uncertainty=False):
"""
Estimate i-vectors for a given StatServer using single process on a single node.
:param stat_server: sufficient statistics stored in a StatServer
:param ubm: Mixture object (the UBM)
:param uncertainty: boolean, if True, return an additional matrix with uncertainty matrices (diagonal of the matrices)
:return: a StatServer with i-vectors in the stat1 attribute and a matrix of uncertainty matrices (optional)
"""
assert(isinstance(stat_server, StatServer) and stat_server.validate()), \
"First argument must be a proper StatServer"
assert (isinstance(ubm, Mixture)
and ubm.validate()), "Second argument must be a proper Mixture"
gmm_covariance = "diag" if ubm.invcov.ndim == 2 else "full"
# Set useful variables
tv_rank = self.F.shape[1]
feature_size = ubm.mu.shape[1]
nb_distrib = ubm.w.shape[0]
# Whiten the statistics for diagonal or full models
if gmm_covariance == "diag":
stat_server.whiten_stat1(ubm.get_mean_super_vector(),
1. / ubm.get_invcov_super_vector())
elif gmm_covariance == "full":
stat_server.whiten_stat1(ubm.get_mean_super_vector(), ubm.invchol)
# Extract i-vectors
iv_stat_server = StatServer()
iv_stat_server.modelset = copy.deepcopy(stat_server.modelset)
iv_stat_server.segset = copy.deepcopy(stat_server.segset)
iv_stat_server.start = copy.deepcopy(stat_server.start)
iv_stat_server.stop = copy.deepcopy(stat_server.stop)
iv_stat_server.stat0 = numpy.ones((stat_server.modelset.shape[0], 1))
iv_stat_server.stat1 = numpy.ones(
(stat_server.modelset.shape[0], tv_rank))
iv_sigma = numpy.ones((stat_server.modelset.shape[0], tv_rank))
# Replicate self.stat0
index_map = numpy.repeat(numpy.arange(nb_distrib), feature_size)
for sess in tqdm(range(stat_server.segset.shape[0]),
desc="Processing"):
inv_lambda = scipy.linalg.inv(
numpy.eye(tv_rank) +
(self.F.T * stat_server.stat0[sess, index_map]).dot(self.F))
Aux = self.F.T.dot(stat_server.stat1[sess, :])
iv_stat_server.stat1[sess, :] = Aux.dot(inv_lambda)
iv_sigma[sess, :] = numpy.diag(inv_lambda + numpy.outer(
iv_stat_server.stat1[sess, :], iv_stat_server.stat1[sess, :]))
if uncertainty:
return iv_stat_server, iv_sigma
else:
return iv_stat_server
def _gaussian_backend_train(data, label):
"""
Take a StatServer of training examples as input
output a StatServer mean for each class and a tied co-variance matrix
"""
train_ss = StatServer()
train_ss.segset = label
train_ss.modelset = label
train_ss.stat1 = data
train_ss.stat0 = numpy.ones((data.shape[0], 1))
train_ss.start = numpy.empty(data.shape[0], dtype="object")
train_ss.stop = numpy.empty(data.shape[0], dtype="object")
return gaussian_backend_train(train_ss)
def extract_ivectors(self,
ubm,
stat_server_filename,
prefix='',
batch_size=300,
uncertainty=False,
num_thread=1):
"""
Parallel extraction of i-vectors using multiprocessing module
:param ubm: Mixture object (the UBM)
:param stat_server_filename: name of the file from which the input StatServer is read
:param prefix: prefix used to store the StatServer in its file
:param batch_size: number of sessions to process in a batch
:param uncertainty: a boolean, if True, return the diagonal of the uncertainty matrices
:param num_thread: number of process to run in parallel
:return: a StatServer with i-vectors in the stat1 attribute and a matrix of uncertainty matrices (optional)
"""
assert (isinstance(ubm, Mixture)
and ubm.validate()), "Second argument must be a proper Mixture"
tv_rank = self.F.shape[1]
# Set useful variables
with h5py.File(stat_server_filename,
'r') as fh: # open the first statserver to get size
_, sv_size = fh[prefix + 'stat1'].shape
nb_sessions = fh[prefix + "modelset"].shape[0]
iv_server = StatServer()
iv_server.modelset = fh.get(prefix + 'modelset').value
iv_server.segset = fh.get(prefix + 'segset').value
tmpstart = fh.get(prefix + "start").value
tmpstop = fh.get(prefix + "stop").value
iv_server.start = numpy.empty(fh[prefix + "start"].shape, '|O')
iv_server.stop = numpy.empty(fh[prefix + "stop"].shape, '|O')
iv_server.start[tmpstart != -1] = tmpstart[tmpstart != -1]
iv_server.stop[tmpstop != -1] = tmpstop[tmpstop != -1]
iv_server.stat0 = numpy.ones((nb_sessions, 1), dtype=STAT_TYPE)
with warnings.catch_warnings():
iv_server.stat1 = serialize(numpy.zeros(
(nb_sessions, tv_rank)))
iv_sigma = serialize(numpy.zeros((nb_sessions, tv_rank)))
nb_sessions = iv_server.modelset.shape[0]
batch_nb = int(numpy.floor(nb_sessions / float(batch_size) +
0.999))
batch_indices = numpy.array_split(numpy.arange(nb_sessions),
batch_nb)
manager = multiprocessing.Manager()
q = manager.Queue()
pool = multiprocessing.Pool(num_thread + 2)
# put listener to work first
watcher = pool.apply_async(iv_collect,
((iv_server.stat1, iv_sigma), q))
# fire off workers
jobs = []
# Load data per batch to reduce the memory footprint
for batch_idx in batch_indices:
# Create list of argument for a process
arg = batch_idx, fh["stat0"][batch_idx, :], fh["stat1"][
batch_idx, :], ubm, self.F
job = pool.apply_async(iv_extract_on_batch, (arg, q))
jobs.append(job)
# collect results from the workers through the pool result queue
for job in jobs:
job.get()
# now we are done, kill the listener
q.put((None, None, None))
pool.close()
iv_server.stat1, iv_sigma = watcher.get()
if uncertainty:
return iv_server, iv_sigma
else:
return iv_server
def extract_ivector(tv,
stat_server_file_name,
ubm,
output_file_name,
uncertainty=False,
prefix=''):
"""
Estimate i-vectors for a given StatServer using multiple process on multiple nodes.
:param comm: MPI.comm object defining the group of nodes to use
:param stat_server_file_name: file name of the sufficient statistics StatServer HDF5 file
:param ubm: Mixture object (the UBM)
:param output_file_name: name of the file to save the i-vectors StatServer in HDF5 format
:param uncertainty: boolean, if True, saves a matrix with uncertainty matrices (diagonal of the matrices)
:param prefix: prefixe of the dataset to read from in HDF5 file
"""
assert (isinstance(ubm, Mixture)
and ubm.validate()), "Second argument must be a proper Mixture"
comm = MPI.COMM_WORLD
comm.Barrier()
gmm_covariance = "diag" if ubm.invcov.ndim == 2 else "full"
# Set useful variables
tv_rank = tv.F.shape[1]
feature_size = ubm.mu.shape[1]
nb_distrib = ubm.w.shape[0]
# Get the number of sessions to process
with h5py.File(stat_server_file_name, 'r') as fh:
nb_sessions = fh["segset"].shape[0]
# Work on each node with different data
indices = numpy.array_split(numpy.arange(nb_sessions), comm.size, axis=0)
sendcounts = numpy.array([idx.shape[0] * tv.F.shape[1] for idx in indices])
displacements = numpy.hstack((0, numpy.cumsum(sendcounts)[:-1]))
stat_server = StatServer.read_subset(stat_server_file_name,
indices[comm.rank])
# Whiten the statistics for diagonal or full models
if gmm_covariance == "diag":
stat_server.whiten_stat1(ubm.get_mean_super_vector(),
1. / ubm.get_invcov_super_vector())
elif gmm_covariance == "full":
stat_server.whiten_stat1(ubm.get_mean_super_vector(), ubm.invchol)
# Estimate i-vectors
if comm.rank == 0:
iv = numpy.zeros((nb_sessions, tv_rank))
iv_sigma = numpy.zeros((nb_sessions, tv_rank))
else:
iv = None
iv_sigma = None
local_iv = numpy.zeros((stat_server.modelset.shape[0], tv_rank))
local_iv_sigma = numpy.ones((stat_server.modelset.shape[0], tv_rank))
# Replicate stat0
index_map = numpy.repeat(numpy.arange(nb_distrib), feature_size)
for sess in range(stat_server.segset.shape[0]):
inv_lambda = scipy.linalg.inv(
numpy.eye(tv_rank) +
(tv.F.T * stat_server.stat0[sess, index_map]).dot(tv.F))
Aux = tv.F.T.dot(stat_server.stat1[sess, :])
local_iv[sess, :] = Aux.dot(inv_lambda)
local_iv_sigma[sess, :] = numpy.diag(
inv_lambda + numpy.outer(local_iv[sess, :], local_iv[sess, :]))
comm.Barrier()
comm.Gatherv(local_iv, [iv, sendcounts, displacements, MPI.DOUBLE], root=0)
comm.Gatherv(local_iv_sigma,
[iv_sigma, sendcounts, displacements, MPI.DOUBLE],
root=0)
if comm.rank == 0:
with h5py.File(stat_server_file_name, 'r') as fh:
iv_stat_server = StatServer()
iv_stat_server.modelset = fh.get(prefix + "modelset").value
iv_stat_server.segset = fh.get(prefix + "segset").value
# if running python 3, need a conversion to unicode
if sys.version_info[0] == 3:
iv_stat_server.modelset = iv_stat_server.modelset.astype(
'U', copy=False)
iv_stat_server.segset = iv_stat_server.segset.astype(
'U', copy=False)
tmpstart = fh.get(prefix + "start").value
tmpstop = fh.get(prefix + "stop").value
iv_stat_server.start = numpy.empty(fh[prefix + "start"].shape,
'|O')
iv_stat_server.stop = numpy.empty(fh[prefix + "stop"].shape, '|O')
iv_stat_server.start[tmpstart != -1] = tmpstart[tmpstart != -1]
iv_stat_server.stop[tmpstop != -1] = tmpstop[tmpstop != -1]
iv_stat_server.stat0 = numpy.ones((nb_sessions, 1))
iv_stat_server.stat1 = iv
iv_stat_server.write(output_file_name)
if uncertainty:
path = os.path.splitext(output_file_name)
write_matrix_hdf5(iv_sigma, path[0] + "_uncertainty" + path[1])
def extract_parallel(args, fs_params):
emb_a_size = 512
emb_b_size = 512
idmap = IdMap(args.idmap)
x_server_1 = StatServer(idmap, 1, emb_a_size)
x_server_2 = StatServer(idmap, 1, emb_b_size)
x_server_3 = StatServer(idmap, 1, emb_b_size)
x_server_4 = StatServer(idmap, 1, emb_b_size)
x_server_5 = StatServer(idmap, 1, emb_b_size)
x_server_6 = StatServer(idmap, 1, emb_b_size)
x_server_1.stat0 = numpy.ones(x_server_1.stat0.shape)
x_server_2.stat0 = numpy.ones(x_server_2.stat0.shape)
x_server_3.stat0 = numpy.ones(x_server_3.stat0.shape)
x_server_4.stat0 = numpy.ones(x_server_4.stat0.shape)
x_server_5.stat0 = numpy.ones(x_server_5.stat0.shape)
x_server_6.stat0 = numpy.ones(x_server_6.stat0.shape)
# Split the indices
mega_batch_size = idmap.leftids.shape[0] // args.num_processes
logging.critical("Number of sessions to process: {}".format(idmap.leftids.shape[0]))
segment_idx = []
for ii in range(args.num_processes):
segment_idx.append(
numpy.arange(ii * mega_batch_size, numpy.min([(ii + 1) * mega_batch_size, idmap.leftids.shape[0]])))
for idx, si in enumerate(segment_idx):
logging.critical("Number of session on process {}: {}".format(idx, len(si)))
# Extract x-vectors in parallel
output_queue = mp.Queue()
processes = []
for rank in range(args.num_processes):
p = mp.Process(target=extract_idmap,
args=(args, rank, segment_idx[rank], fs_params, args.idmap, output_queue)
)
# We first train the model across `num_processes` processes
p.start()
processes.append(p)
# Get the x-vectors and fill the StatServer
for ii in range(args.num_processes):
indices, seg_1, seg_2, seg_3, seg_4, seg_5, seg_6 = output_queue.get()
x_server_1.stat1[indices, :] = seg_1
x_server_2.stat1[indices, :] = seg_2
x_server_3.stat1[indices, :] = seg_3
x_server_4.stat1[indices, :] = seg_4
x_server_5.stat1[indices, :] = seg_5
x_server_6.stat1[indices, :] = seg_6
for p in processes:
p.join()
print("Process parallel fini")
return x_server_1, x_server_2, x_server_3, x_server_4, x_server_5, x_server_6