add new tests for vocoder trainings using coqpit

tests/vocoder_tests/test_fullband_melgan_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import FullbandMelganConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = FullbandMelganConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_gan.py --config_path {config_path} "
+)
+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_vocoder_gan.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/vocoder_tests/test_melgan_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import MelganConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = MelganConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_gan.py --config_path {config_path} "
+)
+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_vocoder_gan.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/vocoder_tests/test_multiband_melgan_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import MultibandMelganConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = MultibandMelganConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_gan.py --config_path {config_path} "
+)
+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_vocoder_gan.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/vocoder_tests/test_parallel_wavegan_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import ParallelWaveganConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = ParallelWaveganConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_gan.py --config_path {config_path} "
+)
+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_vocoder_gan.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/vocoder_tests/test_wavegrad_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import WavegradConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = WavegradConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_wavegrad.py --config_path {config_path} "
+)
+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_vocoder_wavegrad.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)

tests/vocoder_tests/test_wavernn_train.py

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+import glob
+import os
+import shutil
+
+from tests import get_tests_output_path, run_cli
+from TTS.vocoder.configs import WavernnConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+config = WavernnConfig(
+    batch_size=8,
+    eval_batch_size=8,
+    num_loader_workers=0,
+    num_val_loader_workers=0,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    seq_len=8192,
+    eval_split_size=1,
+    print_step=1,
+    print_eval=True,
+    data_path="tests/data/ljspeech",
+    output_path=output_path
+)
+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_vocoder_wavernn.py --config_path {config_path} "
+)
+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_vocoder_wavernn.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)