rename num_speakers

examples/csmsc/voc1/run.sh

@@ -3,7 +3,7 @@
 set -e
 source path.sh
 
-gpus=4,5
+gpus=0,1
 stage=0
 stop_stage=100
 

paddlespeech/t2s/exps/fastspeech2/multi_spk_synthesize_e2e.py

@@ -46,14 +46,14 @@ def evaluate(args, fastspeech2_config, pwg_config):
     print("vocab_size:", vocab_size)
     with open(args.speaker_dict, 'rt') as f:
         spk_id = [line.strip().split() for line in f.readlines()]
-    num_speakers = len(spk_id)
+    spk_num = len(spk_id)
-    print("num_speakers:", num_speakers)
+    print("spk_num:", spk_num)
 
     odim = fastspeech2_config.n_mels
     model = FastSpeech2(
         idim=vocab_size,
         odim=odim,
-        num_speakers=num_speakers,
+        spk_num=spk_num,
         **fastspeech2_config["model"])
 
     model.set_state_dict(

paddlespeech/t2s/exps/fastspeech2/multi_spk_synthesize_e2e_en.py

@@ -51,14 +51,14 @@ def evaluate(args, fastspeech2_config, pwg_config):
     print("vocab_size:", vocab_size)
     with open(args.speaker_dict, 'rt') as f:
         spk_id = [line.strip().split() for line in f.readlines()]
-    num_speakers = len(spk_id)
+    spk_num = len(spk_id)
-    print("num_speakers:", num_speakers)
+    print("spk_num:", spk_num)
 
     odim = fastspeech2_config.n_mels
     model = FastSpeech2(
         idim=vocab_size,
         odim=odim,
-        num_speakers=num_speakers,
+        spk_num=spk_num,
         **fastspeech2_config["model"])
 
     model.set_state_dict(

paddlespeech/t2s/exps/fastspeech2/synthesize.py

@@ -40,19 +40,19 @@ def evaluate(args, fastspeech2_config, pwg_config):
 
     fields = ["utt_id", "text"]
 
-    num_speakers = None
+    spk_num = None
     if args.speaker_dict is not None:
         print("multiple speaker fastspeech2!")
         with open(args.speaker_dict, 'rt') as f:
             spk_id = [line.strip().split() for line in f.readlines()]
-        num_speakers = len(spk_id)
+        spk_num = len(spk_id)
         fields += ["spk_id"]
     elif args.voice_cloning:
         print("voice cloning!")
         fields += ["spk_emb"]
     else:
         print("single speaker fastspeech2!")
-    print("num_speakers:", num_speakers)
+    print("spk_num:", spk_num)
 
     test_dataset = DataTable(data=test_metadata, fields=fields)
 
@@ -65,7 +65,7 @@ def evaluate(args, fastspeech2_config, pwg_config):
     model = FastSpeech2(
         idim=vocab_size,
         odim=odim,
-        num_speakers=num_speakers,
+        spk_num=spk_num,
         **fastspeech2_config["model"])
 
     model.set_state_dict(

paddlespeech/t2s/exps/fastspeech2/train.py

@@ -62,13 +62,13 @@ def train_sp(args, config):
         "pitch", "energy"
     ]
     converters = {"speech": np.load, "pitch": np.load, "energy": np.load}
-    num_speakers = None
+    spk_num = None
     if args.speaker_dict is not None:
         print("multiple speaker fastspeech2!")
         collate_fn = fastspeech2_multi_spk_batch_fn
         with open(args.speaker_dict, 'rt') as f:
             spk_id = [line.strip().split() for line in f.readlines()]
-        num_speakers = len(spk_id)
+        spk_num = len(spk_id)
         fields += ["spk_id"]
     elif args.voice_cloning:
         print("Training voice cloning!")
@@ -78,7 +78,7 @@ def train_sp(args, config):
     else:
         print("single speaker fastspeech2!")
         collate_fn = fastspeech2_single_spk_batch_fn
-    print("num_speakers:", num_speakers)
+    print("spk_num:", spk_num)
 
     # dataloader has been too verbose
     logging.getLogger("DataLoader").disabled = True
@@ -129,10 +129,7 @@ def train_sp(args, config):
 
     odim = config.n_mels
     model = FastSpeech2(
-        idim=vocab_size,
+        idim=vocab_size, odim=odim, spk_num=spk_num, **config["model"])
-        odim=odim,
-        num_speakers=num_speakers,
-        **config["model"])
     if world_size > 1:
         model = DataParallel(model)
     print("model done!")

paddlespeech/t2s/models/fastspeech2/fastspeech2.py

@@ -96,7 +96,7 @@ class FastSpeech2(nn.Layer):
             pitch_embed_dropout: float=0.5,
             stop_gradient_from_pitch_predictor: bool=False,
             # spk emb
-            num_speakers: int=None,
+            spk_num: int=None,
             spk_embed_dim: int=None,
             spk_embed_integration_type: str="add",
             #  tone emb
@@ -146,9 +146,9 @@ class FastSpeech2(nn.Layer):
         # initialize parameters
         initialize(self, init_type)
 
-        if self.spk_embed_dim and num_speakers:
+        if spk_num and self.spk_embed_dim:
             self.spk_embedding_table = nn.Embedding(
-                num_embeddings=num_speakers,
+                num_embeddings=spk_num,
                 embedding_dim=self.spk_embed_dim,
                 padding_idx=self.padding_idx)