reading all speakers upfront

config.json

@@ -76,6 +76,6 @@
     "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
     "text_cleaner": "phoneme_cleaners",
-    "num_speakers": 10 // should just be bigger than the actual number of speakers, 0 disables speaker embeddings
+    "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
 }
 

config_tacotron.json

@@ -77,6 +77,6 @@
         "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
         "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
         "text_cleaner": "phoneme_cleaners",
-        "num_speakers": 10 // should just be bigger than the actual number of speakers, 0 disables speaker embeddings
+        "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
     }
     

config_tacotron2.json

@@ -79,6 +79,6 @@
         "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
         "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
         "text_cleaner": "phoneme_cleaners",
-        "num_speakers": 10 // should just be bigger than the actual number of speakers, 0 disables speaker embeddings
+        "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
     }
     

config_tacotron_de.json

@@ -77,6 +77,6 @@
                 "use_phonemes": false,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
                 "phoneme_language": "de",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
                 "text_cleaner": "phoneme_cleaners",
-                "num_speakers": 10 // should just be bigger than the actual number of speakers, 0 disables speaker embeddings
+                "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
             }
             

config_tacotron_gst.json

@@ -77,6 +77,6 @@
         "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
         "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
         "text_cleaner": "phoneme_cleaners",
-        "num_speakers": 10 // should just be bigger than the actual number of speakers, 0 disables speaker embeddings
+        "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
     }
     

datasets/preprocess.py

@@ -1,6 +1,13 @@
 import os
 from glob import glob
 import re
+import sys
+
+
+def get_preprocessor_by_name(name):
+    """Returns the respective preprocessing function."""
+    thismodule = sys.modules[__name__]
+    return getattr(thismodule, name.lower())
 
 
 def tweb(root_path, meta_file):

train.py

@@ -1,8 +1,5 @@
 import argparse
-import importlib
-import json
 import os
-import shutil
 import sys
 import time
 import traceback
@@ -27,10 +24,11 @@ from utils.generic_utils import (NoamLR, check_update, count_parameters,
                                  set_init_dict, copy_config_file, setup_model)
 from utils.logger import Logger
 from utils.speakers import load_speaker_mapping, save_speaker_mapping, \
-    copy_speaker_mapping
+    get_speakers
 from utils.synthesis import synthesis
 from utils.text.symbols import phonemes, symbols
 from utils.visual import plot_alignment, plot_spectrogram
+from datasets.preprocess import get_preprocessor_by_name
 
 torch.backends.cudnn.enabled = True
 torch.backends.cudnn.benchmark = False
@@ -51,7 +49,7 @@ def setup_loader(is_val=False, verbose=False):
             c.meta_file_val if is_val else c.meta_file_train,
             c.r,
             c.text_cleaner,
-            preprocessor=preprocessor,
+            preprocessor=get_preprocessor_by_name(c.dataset),
             ap=ap,
             batch_group_size=0 if is_val else c.batch_group_size * c.batch_size,
             min_seq_len=0 if is_val else c.min_seq_len,
@@ -78,7 +76,7 @@ def setup_loader(is_val=False, verbose=False):
 def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
           ap, epoch):
     data_loader = setup_loader(is_val=False, verbose=(epoch==0))
-    if c.num_speakers > 1:
+    if c.use_speaker_embedding:
         speaker_mapping = load_speaker_mapping(OUT_PATH)
     model.train()
     epoch_time = 0
@@ -102,21 +100,10 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
         avg_text_length = torch.mean(text_lengths.float())
         avg_spec_length = torch.mean(mel_lengths.float())
 
-        if c.num_speakers > 1:
+        if c.use_speaker_embedding:
-            speaker_ids = []
+            speaker_ids = [speaker_mapping[speaker_name]
-            for speaker_name in speaker_names:
+                           for speaker_name in speaker_names]
-                if speaker_name not in speaker_mapping:
-                    speaker_mapping[speaker_name] = len(speaker_mapping)
-                speaker_ids.append(speaker_mapping[speaker_name])
             speaker_ids = torch.LongTensor(speaker_ids)
-
-            if len(speaker_mapping) > c.num_speakers:
-                raise ValueError("It seems there are at least {} speakers in "
-                                 "your dataset, while 'num_speakers' is set to "
-                                 "{}. Found the following speakers: {}".format(
-                                    len(speaker_mapping),
-                                    c.num_speakers,
-                                    list(speaker_mapping)))
         else:
             speaker_ids = None
 
@@ -271,15 +258,12 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
         if c.tb_model_param_stats:
             tb_logger.tb_model_weights(model, current_step)
 
-    # save speaker mapping
-    if c.num_speakers > 1:
-        save_speaker_mapping(OUT_PATH, speaker_mapping)
     return avg_postnet_loss, current_step
 
 
 def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
     data_loader = setup_loader(is_val=True)
-    if c.num_speakers > 1:
+    if c.use_speaker_embedding:
         speaker_mapping = load_speaker_mapping(OUT_PATH)
     model.eval()
     epoch_time = 0
@@ -311,7 +295,7 @@ def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
                 mel_lengths = data[5]
                 stop_targets = data[6]
 
-                if c.num_speakers > 1:
+                if c.use_speaker_embedding:
                     speaker_ids = [speaker_mapping[speaker_name]
                                    for speaker_name in speaker_names]
                     speaker_ids = torch.LongTensor(speaker_ids)
@@ -443,7 +427,27 @@ def main(args):
         init_distributed(args.rank, num_gpus, args.group_id,
                          c.distributed["backend"], c.distributed["url"])
     num_chars = len(phonemes) if c.use_phonemes else len(symbols)
-    model = setup_model(num_chars, c)
+
+    if c.use_speaker_embedding:
+        speakers = get_speakers(c.data_path, c.meta_file_train, c.dataset)
+        if args.restore_path:
+            prev_out_path = os.path.dirname(args.restore_path)
+            speaker_mapping = load_speaker_mapping(prev_out_path)
+            assert all([speaker in speaker_mapping
+                        for speaker in speakers]), "As of now you, you cannot " \
+                                                   "introduce new speakers to " \
+                                                   "a previously trained model."
+        else:
+            speaker_mapping = {name: i
+                               for i, name in enumerate(speakers)}
+        save_speaker_mapping(OUT_PATH, speaker_mapping)
+        num_speakers = len(speaker_mapping)
+        print("Training with {} speakers: {}".format(num_speakers,
+                                                     ", ".join(speakers)))
+    else:
+        num_speakers = 0
+
+    model = setup_model(num_chars, num_speakers, c)
 
     print(" | > Num output units : {}".format(ap.num_freq), flush=True)
 
@@ -482,10 +486,6 @@ def main(args):
             " > Model restored from step %d" % checkpoint['step'], flush=True)
         start_epoch = checkpoint['epoch']
         args.restore_step = checkpoint['step']
-        # copying speakers.json
-        prev_out_path = os.path.dirname(args.restore_path)
-        if c.num_speakers > 1:
-            copy_speaker_mapping(prev_out_path, OUT_PATH)
     else:
         args.restore_step = 0
 
@@ -607,10 +607,6 @@ if __name__ == '__main__':
         LOG_DIR = OUT_PATH
         tb_logger = Logger(LOG_DIR)
 
-    # Conditional imports
-    preprocessor = importlib.import_module('datasets.preprocess')
-    preprocessor = getattr(preprocessor, c.dataset.lower())
-
     # Audio processor
     ap = AudioProcessor(**c.audio)
 

utils/generic_utils.py

@@ -251,14 +251,14 @@ def set_init_dict(model_dict, checkpoint, c):
     return model_dict
 
 
-def setup_model(num_chars, c):
+def setup_model(num_chars, num_speakers, c):
     print(" > Using model: {}".format(c.model))
     MyModel = importlib.import_module('models.' + c.model.lower())
     MyModel = getattr(MyModel, c.model)
     if c.model.lower() in ["tacotron", "tacotrongst"]:
         model = MyModel(
             num_chars=num_chars,
-            num_speakers=c.num_speakers,
+            num_speakers=num_speakers,
             r=c.r,
             linear_dim=1025,
             mel_dim=80,

utils/speakers.py

@@ -1,6 +1,8 @@
 import os
 import json
 
+from datasets.preprocess import get_preprocessor_by_name
+
 
 def make_speakers_json_path(out_path):
     """Returns conventional speakers.json location."""
@@ -23,8 +25,9 @@ def save_speaker_mapping(out_path, speaker_mapping):
         json.dump(speaker_mapping, f, indent=4)
 
 
-def copy_speaker_mapping(out_path_a, out_path_b):
+def get_speakers(data_root, meta_file, dataset_type):
-    """Copies a speaker mapping when restoring a model from a previous path."""
+    """Returns a sorted, unique list of speakers in a given dataset."""
-    speaker_mapping = load_speaker_mapping(out_path_a)
+    preprocessor = get_preprocessor_by_name(dataset_type)
-    if speaker_mapping is not None:
+    items = preprocessor(data_root, meta_file)
-        save_speaker_mapping(out_path_b, speaker_mapping)
+    speakers = {e[2] for e in items}
+    return sorted(speakers)