Update `TTS.bin` scripts for the new API

TTS/bin/compute_attention_masks.py

@@ -75,7 +75,7 @@ Example run:
     # load the model
     num_chars = len(phonemes) if C.use_phonemes else len(symbols)
     # TODO: handle multi-speaker
-    model = setup_model(num_chars, num_speakers=0, c=C)
+    model = setup_model(C)
     model, _ = load_checkpoint(model, args.model_path, None, args.use_cuda)
     model.eval()
 

TTS/bin/compute_statistics.py

@@ -77,7 +77,7 @@ def main():
     print(f" > Avg mel spec mean: {mel_mean.mean()}")
     print(f" > Avg mel spec scale: {mel_scale.mean()}")
     print(f" > Avg linear spec mean: {linear_mean.mean()}")
-    print(f" > Avg lienar spec scale: {linear_scale.mean()}")
+    print(f" > Avg linear spec scale: {linear_scale.mean()}")
 
     # set default config values for mean-var scaling
     CONFIG.audio.stats_path = output_file_path

TTS/bin/convert_tacotron2_torch_to_tf.py

@@ -31,18 +31,18 @@ c = load_config(config_path)
 num_speakers = 0
 
 # init torch model
-num_chars = len(phonemes) if c.use_phonemes else len(symbols)
+model = setup_model(c)
-model = setup_model(num_chars, num_speakers, c)
 checkpoint = torch.load(args.torch_model_path, map_location=torch.device("cpu"))
 state_dict = checkpoint["model"]
 model.load_state_dict(state_dict)
 
 # init tf model
+num_chars = len(phonemes) if c.use_phonemes else len(symbols)
 model_tf = Tacotron2(
     num_chars=num_chars,
     num_speakers=num_speakers,
     r=model.decoder.r,
-    postnet_output_dim=c.audio["num_mels"],
+    out_channels=c.audio["num_mels"],
     decoder_output_dim=c.audio["num_mels"],
     attn_type=c.attention_type,
     attn_win=c.windowing,

TTS/bin/distribute.py

@@ -1,36 +1,24 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-import argparse
 import os
 import pathlib
 import subprocess
-import sys
 import time
 
 import torch
 
+from TTS.trainer import TrainingArgs
+
 
 def main():
     """
     Call train.py as a new process and pass command arguments
     """
-    parser = argparse.ArgumentParser()
+    parser = TrainingArgs().init_argparse(arg_prefix="")
     parser.add_argument("--script", type=str, help="Target training script to distibute.")
-    parser.add_argument(
-        "--continue_path",
-        type=str,
-        help='Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
-        default="",
-        required="--config_path" not in sys.argv,
-    )
-    parser.add_argument(
-        "--restore_path", type=str, help="Model file to be restored. Use to finetune a model.", default=""
-    )
-    parser.add_argument(
-        "--config_path", type=str, help="Path to config file for training.", required="--continue_path" not in sys.argv
-    )
     args, unargs = parser.parse_known_args()
+    breakpoint()
 
     num_gpus = torch.cuda.device_count()
     group_id = time.strftime("%Y_%m_%d-%H%M%S")
@@ -51,6 +39,7 @@ def main():
         my_env = os.environ.copy()
         my_env["PYTHON_EGG_CACHE"] = "/tmp/tmp{}".format(i)
         command[-1] = "--rank={}".format(i)
+        # prevent stdout for processes with rank != 0
         stdout = None if i == 0 else open(os.devnull, "w")
         p = subprocess.Popen(["python3"] + command, stdout=stdout, env=my_env)  # pylint: disable=consider-using-with
         processes.append(p)

TTS/bin/extract_tts_spectrograms.py

@@ -14,7 +14,6 @@ from TTS.tts.datasets import load_meta_data
 from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.models import setup_model
 from TTS.tts.utils.speakers import get_speaker_manager
-from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import count_parameters
 
@@ -40,9 +39,7 @@ def setup_loader(ap, r, verbose=False):
         use_noise_augment=False,
         verbose=verbose,
         speaker_id_mapping=speaker_manager.speaker_ids,
-        d_vector_mapping=speaker_manager.d_vectors
+        d_vector_mapping=speaker_manager.d_vectors if c.use_speaker_embedding and c.use_d_vector_file else None,
-        if c.use_speaker_embedding and c.use_external_speaker_embedding_file
-        else None,
     )
 
     if c.use_phonemes and c.compute_input_seq_cache:
@@ -224,16 +221,10 @@ def extract_spectrograms(
 
 def main(args):  # pylint: disable=redefined-outer-name
     # pylint: disable=global-variable-undefined
-    global meta_data, symbols, phonemes, model_characters, speaker_manager
+    global meta_data, speaker_manager
 
     # Audio processor
     ap = AudioProcessor(**c.audio)
-    if "characters" in c.keys() and c["characters"]:
-        symbols, phonemes = make_symbols(**c.characters)
-
-    # set model characters
-    model_characters = phonemes if c.use_phonemes else symbols
-    num_chars = len(model_characters)
 
     # load data instances
     meta_data_train, meta_data_eval = load_meta_data(c.datasets)
@@ -245,7 +236,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     speaker_manager = get_speaker_manager(c, args, meta_data_train)
 
     # setup model
-    model = setup_model(num_chars, speaker_manager.num_speakers, c, d_vector_dim=speaker_manager.d_vector_dim)
+    model = setup_model(c)
 
     # restore model
     checkpoint = torch.load(args.checkpoint_path, map_location="cpu")
@@ -283,5 +274,5 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     c = load_config(args.config_path)
-    c.audio["do_trim_silence"] = False  # IMPORTANT!!!!!!!!!!!!!!! disable to align mel
+    c.audio.trim_silence = False
     main(args)