# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
from paddle import DataParallel
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.nn.clip import ClipGradByGlobalNorm
from paddle.optimizer import Adam
from paddlespeech.t2s.training import default_argument_parser
from paddlespeech.t2s.training import ExperimentBase
from paddlespeech.vector.exps.ge2e.config import get_cfg_defaults
from paddlespeech.vector.exps.ge2e.speaker_verification_dataset import Collate
from paddlespeech.vector.exps.ge2e.speaker_verification_dataset import MultiSpeakerMelDataset
from paddlespeech.vector.exps.ge2e.speaker_verification_dataset import MultiSpeakerSampler
from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
[docs]class Ge2eExperiment(ExperimentBase):
[docs] def setup_model(self):
config = self.config
model = LSTMSpeakerEncoder(config.data.n_mels, config.model.num_layers,
config.model.hidden_size,
config.model.embedding_size)
optimizer = Adam(
config.training.learning_rate_init,
parameters=model.parameters(),
grad_clip=ClipGradByGlobalNorm(3))
self.model = DataParallel(model) if self.parallel else model
self.model_core = model
self.optimizer = optimizer
[docs] def setup_dataloader(self):
config = self.config
train_dataset = MultiSpeakerMelDataset(self.args.data)
sampler = MultiSpeakerSampler(train_dataset,
config.training.speakers_per_batch,
config.training.utterances_per_speaker)
train_loader = DataLoader(
train_dataset,
batch_sampler=sampler,
collate_fn=Collate(config.data.partial_n_frames),
num_workers=16)
self.train_dataset = train_dataset
self.train_loader = train_loader
[docs] def train_batch(self):
start = time.time()
batch = self.read_batch()
data_loader_time = time.time() - start
self.optimizer.clear_grad()
self.model.train()
specs = batch
loss, eer = self.model(specs, self.config.training.speakers_per_batch)
loss.backward()
self.model_core.do_gradient_ops()
self.optimizer.step()
iteration_time = time.time() - start
# logging
loss_value = float(loss)
msg = "Rank: {}, ".format(dist.get_rank())
msg += "step: {}, ".format(self.iteration)
msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
iteration_time)
msg += 'loss: {:>.6f} err: {:>.6f}'.format(loss_value, eer)
self.logger.info(msg)
if dist.get_rank() == 0:
self.visualizer.add_scalar("train/loss", loss_value, self.iteration)
self.visualizer.add_scalar("train/eer", eer, self.iteration)
self.visualizer.add_scalar("param/w",
float(self.model_core.similarity_weight),
self.iteration)
self.visualizer.add_scalar("param/b",
float(self.model_core.similarity_bias),
self.iteration)
[docs]def main_sp(config, args):
exp = Ge2eExperiment(config, args)
exp.setup()
exp.resume_or_load()
exp.run()
[docs]def main(config, args):
if args.ngpu > 1:
dist.spawn(main_sp, args=(config, args), nprocs=args.ngpu)
else:
main_sp(config, args)
if __name__ == "__main__":
config = get_cfg_defaults()
parser = default_argument_parser()
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
print(args)
main(config, args)