def getCriterion(args, downsampling, nSpeakers, nPhones):
dimFeatures = args.hiddenGar if not args.onEncoder else args.hiddenEncoder
if not args.supervised:
if args.cpc_mode == 'none':
cpcCriterion = cr.NoneCriterion()
else:
sizeInputSeq = (args.sizeWindow // downsampling)
cpcCriterion = cr.CPCUnsupersivedCriterion(
args.nPredicts,
args.hiddenGar,
args.hiddenEncoder,
args.negativeSamplingExt,
mode=args.cpc_mode,
rnnMode=args.rnnMode,
dropout=args.dropout,
nSpeakers=nSpeakers,
speakerEmbedding=args.speakerEmbedding,
sizeInputSeq=sizeInputSeq)
elif args.pathPhone is not None:
if not args.CTC:
cpcCriterion = cr.PhoneCriterion(dimFeatures,
nPhones,
args.onEncoder,
nLayers=args.nLevelsPhone)
else:
cpcCriterion = cr.CTCPhoneCriterion(dimFeatures, nPhones,
args.onEncoder)
else:
cpcCriterion = cr.SpeakerCriterion(dimFeatures, nSpeakers)
return cpcCriterion
def getCriterion(args, downsampling, nSpeakers, nPhones):
dimFeatures = args.hiddenGar if not args.onEncoder else args.hiddenEncoder
if not args.supervised:
if args.cpc_mode == 'none':
cpcCriterion = cr.NoneCriterion()
else:
sizeInputSeq = (args.sizeWindow // downsampling)
if args.CPCCTC:
cpcCriterion = sa.CPCUnsupersivedCriterion(args.nPredicts,
args.CPCCTCNumMatched,
args.hiddenGar,
args.hiddenEncoder,
args.negativeSamplingExt,
allowed_skips_beg=args.CPCCTCSkipBeg,
allowed_skips_end=args.CPCCTCSkipEnd,
predict_self_loop=args.CPCCTCSelfLoop,
learn_blank=args.CPCCTCLearnBlank,
normalize_enc=args.CPCCTCNormalizeEncs,
normalize_preds=args.CPCCTCNormalizePreds,
masq_rules=args.CPCCTCMasq,
loss_temp=args.CPCCTCLossTemp,
no_negs_in_match_window=args.CPCCTCNoNegsMatchWin,
limit_negs_in_batch=args.limitNegsInBatch,
mode=args.cpc_mode,
rnnMode=args.rnnMode,
dropout=args.dropout,
nSpeakers=nSpeakers,
speakerEmbedding=args.speakerEmbedding,
sizeInputSeq=sizeInputSeq)
else:
cpcCriterion = cr.CPCUnsupersivedCriterion(args.nPredicts,
args.hiddenGar,
args.hiddenEncoder,
args.negativeSamplingExt,
mode=args.cpc_mode,
rnnMode=args.rnnMode,
dropout=args.dropout,
nSpeakers=nSpeakers,
speakerEmbedding=args.speakerEmbedding,
sizeInputSeq=sizeInputSeq)
elif args.pathPhone is not None:
if not args.CTC:
cpcCriterion = cr.PhoneCriterion(dimFeatures,
nPhones, args.onEncoder,
nLayers=args.nLevelsPhone)
else:
cpcCriterion = cr.CTCPhoneCriterion(dimFeatures,
nPhones, args.onEncoder)
else:
cpcCriterion = cr.SpeakerCriterion(dimFeatures, nSpeakers)
return cpcCriterion
def constructPhoneCriterionAndOptimizer():
if not args.CTCphones:
# print(f"Running phone separability with aligned phones")
phone_criterion = cr.PhoneCriterion(dim_features,
nPhonesInData, args.phone_get_encoded,
nLayers=args.linsep_net_layers)
else:
# print(f"Running phone separability with CTC loss")
phone_criterion = cr.CTCPhoneCriterion(dim_features,
nPhonesInData, args.phone_get_encoded,
nLayers=args.linsep_net_layers)
phone_criterion.cuda()
phone_criterion = torch.nn.DataParallel(phone_criterion, device_ids=range(args.nGPU))
# Optimizer
phone_g_params = list(phone_criterion.parameters())
phone_optimizer = torch.optim.Adam(phone_g_params, lr=args.linsep_lr,
betas=(args.linsep_beta1, args.linsep_beta2),
eps=args.linsep_epsilon)
return phone_criterion, phone_optimizer
def main(argv):
args = parse_args(argv)
logs = {"epoch": [], "iter": [], "saveStep": args.save_step}
load_criterion = False
seqNames, speakers = findAllSeqs(args.pathDB,
extension=args.file_extension,
loadCache=not args.ignore_cache)
if args.model == "cpc":
def loadCPCFeatureMaker(pathCheckpoint, gru_level=-1, get_encoded=False, keep_hidden=True):
"""
Load CPC Feature Maker from CPC checkpoint file.
"""
# Set LSTM level
if gru_level is not None and gru_level > 0:
updateConfig = argparse.Namespace(nLevelsGRU=gru_level)
else:
updateConfig = None
# Load CPC model
model, nHiddenGar, nHiddenEncoder = fl.loadModel(pathCheckpoint, updateConfig=updateConfig)
# Keep hidden units at LSTM layers on sequential batches
model.gAR.keepHidden = keep_hidden
# Build CPC Feature Maker from CPC model
#featureMaker = fl.FeatureModule(model, get_encoded=get_encoded)
#return featureMaker
return model, nHiddenGar, nHiddenEncoder
if args.gru_level is not None and args.gru_level > 0:
model, hidden_gar, hidden_encoder = loadCPCFeatureMaker(args.load, gru_level=args.gru_level)
else:
model, hidden_gar, hidden_encoder = fl.loadModel(args.load,
loadStateDict=not args.no_pretraining)
dim_features = hidden_encoder if args.get_encoded else hidden_gar
else:
sys.path.append(os.path.abspath(args.path_fairseq))
from fairseq import checkpoint_utils
def loadCheckpoint(path_checkpoint, path_data):
"""
Load lstm_lm model from checkpoint.
"""
# Set up the args Namespace
model_args = argparse.Namespace(
task="language_modeling",
output_dictionary_size=-1,
data=path_data,
path=path_checkpoint
)
# Load model
models, _model_args = checkpoint_utils.load_model_ensemble([model_args.path])
model = models[0]
return model
model = loadCheckpoint(args.load[0], args.pathDB)
dim_features = 768
dim_inter = args.dim_inter
# Now the criterion
if args.mode == "phonemes_nullspace" or args.mode == "speakers_nullspace":
speakers_factorized = cr.SpeakerDoubleCriterion(dim_features, dim_inter, len(speakers))
speakers_factorized.load_state_dict(torch.load(args.path_speakers_factorized)["cpcCriterion"])
for param in speakers_factorized.parameters():
param.requires_grad = False
def my_nullspace(At, rcond=None):
ut, st, vht = torch.Tensor.svd(At, some=False,compute_uv=True)
vht=vht.T
Mt, Nt = ut.shape[0], vht.shape[1]
if rcond is None:
rcondt = torch.finfo(st.dtype).eps * max(Mt, Nt)
tolt = torch.max(st) * rcondt
numt= torch.sum(st > tolt, dtype=int)
nullspace = vht[numt:,:].T.cpu().conj()
# nullspace.backward(torch.ones_like(nullspace),retain_graph=True)
return nullspace
dim_features = dim_features - dim_inter
nullspace = my_nullspace(speakers_factorized.linearSpeakerClassifier[0].weight)
model = CPCModelNullspace(model, nullspace)
phone_labels = None
if args.pathPhone is not None:
phone_labels, n_phones = parseSeqLabels(args.pathPhone)
label_key = 'phone'
if not args.CTC:
print(f"Running phone separability with aligned phones")
criterion = cr.PhoneCriterion(dim_features,
n_phones, args.get_encoded)
else:
print(f"Running phone separability with CTC loss")
criterion = cr.CTCPhoneCriterion(dim_features,
n_phones, args.get_encoded)
else:
label_key = 'speaker'
print(f"Running speaker separability")
if args.mode == "speakers_factorized":
criterion = cr.SpeakerDoubleCriterion(dim_features, dim_inter, len(speakers))
else:
criterion = cr.SpeakerCriterion(dim_features, len(speakers))
criterion.cuda()
criterion = torch.nn.DataParallel(criterion, device_ids=range(args.nGPU))
model.cuda()
model = torch.nn.DataParallel(model, device_ids=range(args.nGPU))
# Dataset
seq_train = filterSeqs(args.pathTrain, seqNames)
seq_val = filterSeqs(args.pathVal, seqNames)
if args.debug:
seq_train = seq_train[:1000]
seq_val = seq_val[:100]
db_train = AudioBatchData(args.pathDB, args.size_window, seq_train,
phone_labels, len(speakers), nProcessLoader=args.n_process_loader,
MAX_SIZE_LOADED=args.max_size_loaded)
db_val = AudioBatchData(args.pathDB, args.size_window, seq_val,
phone_labels, len(speakers), nProcessLoader=args.n_process_loader)
batch_size = args.batchSizeGPU * args.nGPU
train_loader = db_train.getDataLoader(batch_size, "uniform", True,
numWorkers=0)
val_loader = db_val.getDataLoader(batch_size, 'sequential', False,
numWorkers=0)
# Optimizer
g_params = list(criterion.parameters())
model.optimize = False
model.eval()
if args.unfrozen:
print("Working in full fine-tune mode")
g_params += list(model.parameters())
model.optimize = True
else:
print("Working with frozen features")
for g in model.parameters():
g.requires_grad = False
optimizer = torch.optim.Adam(g_params, lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.epsilon)
# Checkpoint directory
args.pathCheckpoint = Path(args.pathCheckpoint)
args.pathCheckpoint.mkdir(exist_ok=True)
args.pathCheckpoint = str(args.pathCheckpoint / "checkpoint")
with open(f"{args.pathCheckpoint}_args.json", 'w') as file:
json.dump(vars(args), file, indent=2)
if args.centerpushFile:
clustersFileExt = args.centerpushFile.split('.')[-1]
assert clustersFileExt in ('pt', 'npy', 'txt')
if clustersFileExt == 'npy':
centers = np.load(args.centerpushFile)
elif clustersFileExt == 'txt':
centers = np.genfromtxt(args.centerpushFile)
elif clustersFileExt == 'pt': # assuming it's a checkpoint
centers = torch.load(args.centerpushFile, map_location=torch.device('cpu'))['state_dict']['Ck']
centers = torch.reshape(centers, centers.shape[1:]).numpy()
centers = torch.tensor(centers).cuda()
centerpushSettings = (centers, args.centerpushDeg)
else:
centerpushSettings = None
run(model, criterion, train_loader, val_loader, optimizer, logs,
args.n_epoch, args.pathCheckpoint, label_key=label_key, centerpushSettings=centerpushSettings)
def main(argv):
args = parse_args(argv)
logs = {"epoch": [], "iter": [], "saveStep": args.save_step}
load_criterion = False
seqNames, speakers = findAllSeqs(args.pathDB,
extension=args.file_extension,
loadCache=not args.ignore_cache)
model, hidden_gar, hidden_encoder = fl.loadModel(
args.load, loadStateDict=not args.no_pretraining)
model.cuda()
model = torch.nn.DataParallel(model, device_ids=range(args.nGPU))
dim_features = hidden_encoder if args.get_encoded else hidden_gar
# Now the criterion
phone_labels = None
if args.pathPhone is not None:
phone_labels, n_phones = parseSeqLabels(args.pathPhone)
if not args.CTC:
print(f"Running phone separability with aligned phones")
criterion = cr.PhoneCriterion(dim_features, n_phones,
args.get_encoded)
else:
print(f"Running phone separability with CTC loss")
criterion = cr.CTCPhoneCriterion(dim_features, n_phones,
args.get_encoded)
else:
print(f"Running speaker separability")
criterion = cr.SpeakerCriterion(dim_features, len(speakers))
criterion.cuda()
criterion = torch.nn.DataParallel(criterion, device_ids=range(args.nGPU))
# Dataset
seq_train = filterSeqs(args.pathTrain, seqNames)
seq_val = filterSeqs(args.pathVal, seqNames)
if args.debug:
seq_train = seq_train[:1000]
seq_val = seq_val[:100]
db_train = AudioBatchData(args.pathDB, args.size_window, seq_train,
phone_labels, len(speakers))
db_val = AudioBatchData(args.pathDB, args.size_window, seq_val,
phone_labels, len(speakers))
batch_size = args.batchSizeGPU * args.nGPU
train_loader = db_train.getDataLoader(batch_size,
"uniform",
True,
numWorkers=0)
val_loader = db_val.getDataLoader(batch_size,
'sequential',
False,
numWorkers=0)
# Optimizer
g_params = list(criterion.parameters())
model.optimize = False
model.eval()
if args.unfrozen:
print("Working in full fine-tune mode")
g_params += list(model.parameters())
model.optimize = True
else:
print("Working with frozen features")
for g in model.parameters():
g.requires_grad = False
optimizer = torch.optim.Adam(g_params,
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.epsilon)
# Checkpoint directory
args.pathCheckpoint = Path(args.pathCheckpoint)
args.pathCheckpoint.mkdir(exist_ok=True)
args.pathCheckpoint = str(args.pathCheckpoint / "checkpoint")
with open(f"{args.pathCheckpoint}_args.json", 'w') as file:
json.dump(vars(args), file, indent=2)
run(model, criterion, train_loader, val_loader, optimizer, logs,
args.n_epoch, args.pathCheckpoint)
