move bash script based tests to python with coqpit

TTS/bin/train_speedy_speech.py

@@ -398,7 +398,6 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
                 "Prior to November 22, 1963.",
             ]
 
-
         # test sentences
         test_audios = {}
         test_figures = {}

TTS/utils/arguments.py

@@ -2,12 +2,13 @@
 # -*- coding: utf-8 -*-
 """Argument parser for training scripts."""
 
-import torch
 import argparse
 import glob
 import os
 import re
 
+import torch
+
 from TTS.tts.utils.text.symbols import parse_symbols
 from TTS.utils.console_logger import ConsoleLogger
 from TTS.utils.generic_utils import create_experiment_folder, get_git_branch

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.has('reinit_layers') and 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

tests/tts_tests/test_speedy_speech_train.py

@@ -0,0 +1,48 @@
+import glob
+import os
+
+from tests import get_tests_output_path, run_cli
+from TTS.config import BaseDatasetConfig
+from TTS.tts.configs import SpeedySpeechConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+
+config = SpeedySpeechConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    print_step=1,
+    print_eval=True,
+)
+config.audio.do_trim_silence = True
+config.audio.trim_db = 60
+config.save_json(config_path)
+
+# train the model for one epoch
+command_train = (
+    f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_speedy_speech.py --config_path {config_path} "
+    f"--coqpit.output_path {output_path} "
+    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.meta_file_train metadata.csv "
+    "--coqpit.datasets.0.meta_file_val metadata.csv "
+    "--coqpit.datasets.0.path tests/data/ljspeech "
+    "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt"
+)
+run_cli(command_train)
+
+# Find latest folder
+continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
+
+# restore the model and continue training for one more epoch
+command_train = f"CUDA_VISIBLE_DEVICES='' python TTS/bin/train_speedy_speech.py --continue_path {continue_path} "
+run_cli(command_train)