coqpit refactoring

TTS/tts/utils/synthesis.py

@@ -23,8 +23,8 @@ def text_to_seqvec(text, CONFIG):
                 text_cleaner,
                 CONFIG.phoneme_language,
                 CONFIG.enable_eos_bos_chars,
-                tp=CONFIG.characters if "characters" in CONFIG.keys() else None,
-                add_blank=CONFIG["add_blank"] if "add_blank" in CONFIG.keys() else False,
+                tp=CONFIG.characters,
+                add_blank=CONFIG.add_blank,
             ),
             dtype=np.int32,
         )
@@ -33,8 +33,8 @@ def text_to_seqvec(text, CONFIG):
             text_to_sequence(
                 text,
                 text_cleaner,
-                tp=CONFIG.characters if "characters" in CONFIG.keys() else None,
-                add_blank=CONFIG["add_blank"] if "add_blank" in CONFIG.keys() else False,
+                tp=CONFIG.characters,
+                add_blank=CONFIG.add_blank,
             ),
             dtype=np.int32,
         )

TTS/utils/arguments.py

@@ -2,12 +2,11 @@
 # -*- coding: utf-8 -*-
 """Argument parser for training scripts."""
 
+import torch
 import argparse
 import glob
-import json
 import os
 import re
-import sys
 
 from TTS.tts.utils.text.symbols import parse_symbols
 from TTS.utils.console_logger import ConsoleLogger
@@ -146,10 +145,9 @@ def process_args(args):
     config.parse_args(coqpit_overrides)
     if config.mixed_precision:
         print("   >  Mixed precision mode is ON")
-    if not os.path.exists(config.output_path):
+    experiment_path = args.continue_path
+    if not experiment_path:
         experiment_path = create_experiment_folder(config.output_path, config.run_name, args.debug)
-    else:
-        experiment_path = config.output_path
     audio_path = os.path.join(experiment_path, "test_audios")
     # setup rank 0 process in distributed training
     if args.rank == 0:
@@ -164,12 +162,12 @@ def process_args(args):
         if config.has("characters_config"):
             used_characters = parse_symbols()
             new_fields["characters"] = used_characters
-        copy_model_files(config, args.config_path, experiment_path, new_fields)
+        copy_model_files(config, experiment_path, new_fields)
         os.chmod(audio_path, 0o775)
         os.chmod(experiment_path, 0o775)
         tb_logger = TensorboardLogger(experiment_path, model_name=config.model)
         # write model desc to tensorboard
-        tb_logger.tb_add_text("model-description", config["run_description"], 0)
+        tb_logger.tb_add_text("model-config", f"<pre>{config.to_json()}</pre>", 0)
     c_logger = ConsoleLogger()
     return config, experiment_path, audio_path, c_logger, tb_logger
 

TTS/utils/audio.py

@@ -21,6 +21,7 @@ class AudioProcessor(object):
         sample_rate (int, optional): target audio sampling rate. Defaults to None.
         resample (bool, optional): enable/disable resampling of the audio clips when the target sampling rate does not match the original sampling rate. Defaults to False.
         num_mels (int, optional): number of melspectrogram dimensions. Defaults to None.
+        log_func (int, optional): log exponent used for converting spectrogram aplitude to DB.
         min_level_db (int, optional): minimum db threshold for the computed melspectrograms. Defaults to None.
         frame_shift_ms (int, optional): milliseconds of frames between STFT columns. Defaults to None.
         frame_length_ms (int, optional): milliseconds of STFT window length. Defaults to None.

TTS/utils/generic_utils.py

@@ -111,7 +111,7 @@ def set_init_dict(model_dict, checkpoint_state, c):
     # 2. filter out different size layers
     pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
     # 3. skip reinit layers
-    if c.reinit_layers is not None:
+    if c.has('reinit_layers') and c.reinit_layers is not None:
         for reinit_layer_name in c.reinit_layers:
             pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
     # 4. overwrite entries in the existing state dict

TTS/utils/io.py

@@ -58,35 +58,25 @@ def load_config(config_path: str) -> None:
     return config
 
 
-def copy_model_files(c, config_file, out_path, new_fields):
+def copy_model_files(config, out_path, new_fields):
     """Copy config.json and other model files to training folder and add
     new fields.
 
     Args:
-        c (dict): model config from config.json.
-        config_file (str): path to config file.
+        config (Coqpit): Coqpit config defining the training run.
         out_path (str): output path to copy the file.
         new_fields (dict): new fileds to be added or edited
             in the config file.
     """
-    # copy config.json
     copy_config_path = os.path.join(out_path, "config.json")
-    config_lines = open(config_file, "r", encoding="utf-8").readlines()
     # add extra information fields
-    for key, value in new_fields.items():
-        if isinstance(value, str):
-            new_line = '"{}":"{}",\n'.format(key, value)
-        else:
-            new_line = '"{}":{},\n'.format(key, json.dumps(value, ensure_ascii=False))
-        config_lines.insert(1, new_line)
-    config_out_file = open(copy_config_path, "w", encoding="utf-8")
-    config_out_file.writelines(config_lines)
-    config_out_file.close()
+    config.update(new_fields, allow_new=True)
+    config.save_json(copy_config_path)
     # copy model stats file if available
-    if c.audio["stats_path"] is not None:
+    if config.audio.stats_path is not None:
         copy_stats_path = os.path.join(out_path, "scale_stats.npy")
         if not os.path.exists(copy_stats_path):
             copyfile(
-                c.audio["stats_path"],
+                config.audio.stats_path,
                 copy_stats_path,
             )