fix some trainer

examples/vctk/vc3/conf/default.yaml

@@ -41,7 +41,7 @@ discriminator_params:
     dim_in: 64           # same as dim_in in generator_params
     num_domains: 20      # same as num_domains in mapping_network_params
     max_conv_dim: 512    # same as max_conv_dim in generator_params
-    n_repeat: 4
+    repeat_num: 4
 asr_params:
     input_dim: 80
     hidden_dim: 256
@@ -77,6 +77,7 @@ loss_params:
 ###########################################################
 batch_size: 5               # Batch size.
 num_workers: 2              # Number of workers in DataLoader.
+max_mel_length: 192
 
 ###########################################################
 #             OPTIMIZER & SCHEDULER SETTING               #
@@ -84,47 +85,47 @@ num_workers: 2              # Number of workers in DataLoader.
 generator_optimizer_params:
     beta1: 0.0
     beta2: 0.99
-    weight_decay: 1e-4                   
+    weight_decay: 1.0e-4                   
-    epsilon: 1e-9
+    epsilon: 1.0e-9
 generator_scheduler_params:
-    max_learning_rate: 2e-4
+    max_learning_rate: 2.0e-4
     phase_pct: 0.0
     divide_factor: 1
     total_steps: 200000                # train_max_steps
-    end_learning_rate: 2e-4
+    end_learning_rate: 2.0e-4
 style_encoder_optimizer_params:
     beta1: 0.0
     beta2: 0.99
-    weight_decay: 1e-4                 
+    weight_decay: 1.0e-4                 
-    epsilon: 1e-9
+    epsilon: 1.0e-9
 style_encoder_scheduler_params:
-    max_learning_rate: 2e-4
+    max_learning_rate: 2.0e-4
     phase_pct: 0.0
     divide_factor: 1
     total_steps: 200000                # train_max_steps
-    end_learning_rate: 2e-4
+    end_learning_rate: 2.0e-4
 mapping_network_optimizer_params:
     beta1: 0.0
     beta2: 0.99
-    weight_decay: 1e-4                 
+    weight_decay: 1.0e-4                 
-    epsilon: 1e-9
+    epsilon: 1.0e-9
 mapping_network_scheduler_params:
-    max_learning_rate: 2e-6
+    max_learning_rate: 2.0e-6
     phase_pct: 0.0
     divide_factor: 1
     total_steps: 200000                # train_max_steps
-    end_learning_rate: 2e-6
+    end_learning_rate: 2.0e-6
 discriminator_optimizer_params:
     beta1: 0.0
     beta2: 0.99
-    weight_decay: 1e-4                 
+    weight_decay: 1.0e-4                 
-    epsilon: 1e-9
+    epsilon: 1.0e-9
 discriminator_scheduler_params:
-    max_learning_rate: 2e-4
+    max_learning_rate: 2.0e-4
     phase_pct: 0.0
     divide_factor: 1
     total_steps: 200000                # train_max_steps
-    end_learning_rate: 2e-4        
+    end_learning_rate: 2.0e-4        
 
 ###########################################################
 #                    TRAINING SETTING                     #

examples/vctk/vc3/local/train.sh

@@ -8,6 +8,4 @@ python3 ${BIN_DIR}/train.py \
     --dev-metadata=dump/dev/norm/metadata.jsonl \
     --config=${config_path} \
     --output-dir=${train_output_path} \
-    --ngpu=1 \
+    --ngpu=1
-    --phones-dict=dump/phone_id_map.txt \
-    --speaker-dict=dump/speaker_id_map.txt

paddlespeech/t2s/datasets/am_batch_fn.py

@@ -852,20 +852,21 @@ class StarGANv2VCCollateFn:
         # (B,)
         label = paddle.to_tensor(label)
         ref_label = paddle.to_tensor(ref_label)
-        # [B, 80, T] -> [B, 1, 80, T]
+        # [B, T, 80] -> [B, 1, 80, T]
-        mel = paddle.to_tensor(mel)
+        mel = paddle.to_tensor(mel).transpose([0, 2, 1]).unsqueeze(1)
-        ref_mel = paddle.to_tensor(ref_mel)
+        ref_mel = paddle.to_tensor(ref_mel).transpose([0, 2, 1]).unsqueeze(1)
-        ref_mel_2 = paddle.to_tensor(ref_mel_2)
+        ref_mel_2 = paddle.to_tensor(ref_mel_2).transpose(
+            [0, 2, 1]).unsqueeze(1)
 
-        z_trg = paddle.randn(batch_size, self.latent_dim)
+        z_trg = paddle.randn([batch_size, self.latent_dim])
-        z_trg2 = paddle.randn(batch_size, self.latent_dim)
+        z_trg2 = paddle.randn([batch_size, self.latent_dim])
 
         batch = {
-            "x_real": mels,
+            "x_real": mel,
-            "y_org": labels,
+            "y_org": label,
-            "x_ref": ref_mels,
+            "x_ref": ref_mel,
-            "x_ref2": ref_mels_2,
+            "x_ref2": ref_mel_2,
-            "y_trg": ref_labels,
+            "y_trg": ref_label,
             "z_trg": z_trg,
             "z_trg2": z_trg2
         }

paddlespeech/t2s/exps/starganv2_vc/train.py

@@ -29,9 +29,12 @@ from paddle.optimizer import AdamW
 from paddle.optimizer.lr import OneCycleLR
 from yacs.config import CfgNode
 
-from paddlespeech.t2s.datasets.am_batch_fn import starganv2_vc_batch_fn
+from paddlespeech.cli.utils import download_and_decompress
-from paddlespeech.t2s.datasets.data_table import DataTable
+from paddlespeech.resource.pretrained_models import StarGANv2VC_source
+from paddlespeech.t2s.datasets.am_batch_fn import build_starganv2_vc_collate_fn
+from paddlespeech.t2s.datasets.data_table import StarGANv2VCDataTable
 from paddlespeech.t2s.models.starganv2_vc import ASRCNN
+from paddlespeech.t2s.models.starganv2_vc import Discriminator
 from paddlespeech.t2s.models.starganv2_vc import Generator
 from paddlespeech.t2s.models.starganv2_vc import JDCNet
 from paddlespeech.t2s.models.starganv2_vc import MappingNetwork
@@ -66,7 +69,9 @@ def train_sp(args, config):
     fields = ["speech", "speech_lengths"]
     converters = {"speech": np.load}
 
-    collate_fn = starganv2_vc_batch_fn
+    collate_fn = build_starganv2_vc_collate_fn(
+        latent_dim=config['mapping_network_params']['latent_dim'],
+        max_mel_length=config['max_mel_length'])
 
     # dataloader has been too verbose
     logging.getLogger("DataLoader").disabled = True
@@ -74,16 +79,10 @@ def train_sp(args, config):
     # construct dataset for training and validation
     with jsonlines.open(args.train_metadata, 'r') as reader:
         train_metadata = list(reader)
-    train_dataset = DataTable(
+    train_dataset = StarGANv2VCDataTable(data=train_metadata)
-        data=train_metadata,
-        fields=fields,
-        converters=converters, )
     with jsonlines.open(args.dev_metadata, 'r') as reader:
         dev_metadata = list(reader)
-    dev_dataset = DataTable(
+    dev_dataset = StarGANv2VCDataTable(data=dev_metadata)
-        data=dev_metadata,
-        fields=fields,
-        converters=converters, )
 
     # collate function and dataloader
     train_sampler = DistributedBatchSampler(
@@ -118,6 +117,7 @@ def train_sp(args, config):
     generator = Generator(**config['generator_params'])
     mapping_network = MappingNetwork(**config['mapping_network_params'])
     style_encoder = StyleEncoder(**config['style_encoder_params'])
+    discriminator = Discriminator(**config['discriminator_params'])
 
     # load pretrained model
     jdc_model_dir = os.path.join(uncompress_path, 'jdcnet.pdz')

paddlespeech/t2s/models/starganv2_vc/starganv2_vc_updater.py

@@ -21,10 +21,13 @@ from paddle.nn import Layer
 from paddle.optimizer import Optimizer
 from paddle.optimizer.lr import LRScheduler
 
+from paddlespeech.t2s.models.starganv2_vc.losses import compute_d_loss
+from paddlespeech.t2s.models.starganv2_vc.losses import compute_g_loss
 from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
 from paddlespeech.t2s.training.reporter import report
 from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater
 from paddlespeech.t2s.training.updaters.standard_updater import UpdaterState
+
 logging.basicConfig(
     format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
     datefmt='[%Y-%m-%d %H:%M:%S]')
@@ -62,10 +65,10 @@ class StarGANv2VCUpdater(StandardUpdater):
         self.models = models
 
         self.optimizers = optimizers
-        self.optimizer_g = optimizers['optimizer_g']
+        self.optimizer_g = optimizers['generator']
-        self.optimizer_s = optimizers['optimizer_s']
+        self.optimizer_s = optimizers['style_encoder']
-        self.optimizer_m = optimizers['optimizer_m']
+        self.optimizer_m = optimizers['mapping_network']
-        self.optimizer_d = optimizers['optimizer_d']
+        self.optimizer_d = optimizers['discriminator']
 
         self.schedulers = schedulers
         self.scheduler_g = schedulers['generator']