add starganv2 vc trainer

examples/vctk/vc3/conf/default.yaml

@@ -1,22 +1,119 @@
-  generator_params:
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+# 其实没用上，其实用的是 16000
+sr: 24000
+n_fft: 2048
+win_length: 1200
+hop_length: 300
+n_mels: 80
+###########################################################
+#                       MODEL SETTING                     #
+###########################################################
+generator_params:
     dim_in: 64
     style_dim: 64
     max_conv_dim: 512
     w_hpf: 0
     F0_channel: 256
-  mapping_network_params:
+mapping_network_params:
     num_domains: 20      # num of speakers in StarGANv2
     latent_dim: 16
     style_dim: 64        # same as style_dim in generator_params
     hidden_dim: 512      # same as max_conv_dim in generator_params
-  style_encoder_params:
+style_encoder_params:
     dim_in: 64           # same as dim_in in generator_params
     style_dim: 64        # same as style_dim in generator_params
     num_domains: 20      # same as num_domains in generator_params
     max_conv_dim: 512    # same as max_conv_dim in generator_params
-  discriminator_params:
+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
-
+asr_params:
+    input_dim: 80
+    hidden_dim: 256
+    n_token: 80
+    token_embedding_dim: 256
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+loss_params:
+    g_loss:
+        lambda_sty: 1.
+        lambda_cyc: 5.
+        lambda_ds: 1.
+        lambda_norm: 1.
+        lambda_asr: 10.
+        lambda_f0: 5.
+        lambda_f0_sty: 0.1
+        lambda_adv: 2.
+        lambda_adv_cls: 0.5
+        norm_bias: 0.5
+    d_loss:
+        lambda_reg: 1.
+        lambda_adv_cls: 0.1
+        lambda_con_reg: 10.
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+batch_size: 5               # Batch size.
+num_workers: 2              # Number of workers in DataLoader.
+
+###########################################################
+#             OPTIMIZER & SCHEDULER SETTING               #
+###########################################################
+generator_optimizer_params:
+    beta1: 0.0
+    beta2: 0.99
+    weight_decay: 1e-4                   
+    epsilon: 1e-9
+generator_scheduler_params:
+    max_learning_rate: 2e-4
+    phase_pct: 0.0
+    divide_factor: 1
+    total_steps: 200000                # train_max_steps
+    end_learning_rate: 2e-4
+style_encoder_optimizer_params:
+    beta1: 0.0
+    beta2: 0.99
+    weight_decay: 1e-4                 
+    epsilon: 1e-9
+style_encoder_scheduler_params:
+    max_learning_rate: 2e-4
+    phase_pct: 0.0
+    divide_factor: 1
+    total_steps: 200000                # train_max_steps
+    end_learning_rate: 2e-4
+mapping_network_optimizer_params:
+    beta1: 0.0
+    beta2: 0.99
+    weight_decay: 1e-4                 
+    epsilon: 1e-9
+mapping_network_scheduler_params:
+    max_learning_rate: 2e-6
+    phase_pct: 0.0
+    divide_factor: 1
+    total_steps: 200000                # train_max_steps
+    end_learning_rate: 2e-6
+discriminator_optimizer_params:
+    beta1: 0.0
+    beta2: 0.99
+    weight_decay: 1e-4                 
+    epsilon: 1e-9
+discriminator_scheduler_params:
+    max_learning_rate: 2e-4
+    phase_pct: 0.0
+    divide_factor: 1
+    total_steps: 200000                # train_max_steps
+    end_learning_rate: 2e-4        
+
+###########################################################
+#                    TRAINING SETTING                     #
+###########################################################
+max_epoch: 150
+num_snapshots: 5
+seed: 1

paddlespeech/t2s/datasets/am_batch_fn.py

@@ -418,7 +418,9 @@ def diffsinger_single_spk_batch_fn(examples):
     # fields = ["text", "note", "note_dur", "is_slur", "text_lengths", "speech", "speech_lengths", "durations", "pitch", "energy"]
     text = [np.array(item["text"], dtype=np.int64) for item in examples]
     note = [np.array(item["note"], dtype=np.int64) for item in examples]
-    note_dur = [np.array(item["note_dur"], dtype=np.float32) for item in examples]
+    note_dur = [
+        np.array(item["note_dur"], dtype=np.float32) for item in examples
+    ]
     is_slur = [np.array(item["is_slur"], dtype=np.int64) for item in examples]
     speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
     pitch = [np.array(item["pitch"], dtype=np.float32) for item in examples]
@@ -474,7 +476,9 @@ def diffsinger_multi_spk_batch_fn(examples):
     # fields = ["text", "note", "note_dur", "is_slur", "text_lengths", "speech", "speech_lengths", "durations", "pitch", "energy", "spk_id"/"spk_emb"]
     text = [np.array(item["text"], dtype=np.int64) for item in examples]
     note = [np.array(item["note"], dtype=np.int64) for item in examples]
-    note_dur = [np.array(item["note_dur"], dtype=np.float32) for item in examples]
+    note_dur = [
+        np.array(item["note_dur"], dtype=np.float32) for item in examples
+    ]
     is_slur = [np.array(item["is_slur"], dtype=np.int64) for item in examples]
     speech = [np.array(item["speech"], dtype=np.float32) for item in examples]
     pitch = [np.array(item["pitch"], dtype=np.float32) for item in examples]
@@ -663,6 +667,20 @@ def vits_multi_spk_batch_fn(examples):
     return batch
 
 
+# 未完成
+def starganv2_vc_batch_fn(examples):
+    batch = {
+        "x_real": None,
+        "y_org": None,
+        "x_ref": None,
+        "x_ref2": None,
+        "y_trg": None,
+        "z_trg": None,
+        "z_trg2": None,
+    }
+    return batch
+
+
 # for PaddleSlim
 def fastspeech2_single_spk_batch_fn_static(examples):
     text = [np.array(item["text"], dtype=np.int64) for item in examples]