remove used files

control/cli/synthesizer_preprocess_audio.py

@@ -1,65 +0,0 @@
-from models.synthesizer.preprocess import preprocess_dataset
-from models.synthesizer.hparams import hparams
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-
-recognized_datasets = [
-    "aidatatang_200zh",
-    "magicdata",
-    "aishell3"
-]
-
-if __name__ == "__main__":
-    print("This method is deprecaded and will not be longer supported, please use 'pre.py'")
-    parser = argparse.ArgumentParser(
-        description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
-                    "and writes them to  the disk. Audio files are also saved, to be used by the "
-                    "vocoder for training.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument("datasets_root", type=Path, help=\
-        "Path to the directory containing your LibriSpeech/TTS datasets.")
-    parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
-        "Path to the output directory that will contain the mel spectrograms, the audios and the "
-        "embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
-    parser.add_argument("-n", "--n_processes", type=int, default=None, help=\
-        "Number of processes in parallel.")
-    parser.add_argument("-s", "--skip_existing", action="store_true", help=\
-        "Whether to overwrite existing files with the same name. Useful if the preprocessing was "
-        "interrupted.")
-    parser.add_argument("--hparams", type=str, default="", help=\
-        "Hyperparameter overrides as a comma-separated list of name-value pairs")
-    parser.add_argument("--no_trim", action="store_true", help=\
-        "Preprocess audio without trimming silences (not recommended).")
-    parser.add_argument("--no_alignments", action="store_true", help=\
-        "Use this option when dataset does not include alignments\
-        (these are used to split long audio files into sub-utterances.)")
-    parser.add_argument("--dataset", type=str, default="aidatatang_200zh", help=\
-        "Name of the dataset to process, allowing values: magicdata, aidatatang_200zh.")
-    args = parser.parse_args()
-
-    # Process the arguments
-    if not hasattr(args, "out_dir"):
-        args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
-    assert args.dataset in recognized_datasets, 'is not supported, please vote for it in https://github.com/babysor/MockingBird/issues/10'
-    # Create directories
-    assert args.datasets_root.exists()
-    args.out_dir.mkdir(exist_ok=True, parents=True)
-
-    # Verify webrtcvad is available
-    if not args.no_trim:
-        try:
-            import webrtcvad
-        except:
-            raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
-                "noise removal and is recommended. Please install and try again. If installation fails, "
-                "use --no_trim to disable this error message.")
-    del args.no_trim
-
-    # Preprocess the dataset
-    print_args(args, parser)
-    args.hparams = hparams.parse(args.hparams)
-
-    preprocess_dataset(**vars(args))

control/cli/synthesizer_preprocess_embeds.py

@@ -1,26 +0,0 @@
-from models.synthesizer.preprocess import create_embeddings
-from utils.argutils import print_args
-from pathlib import Path
-import argparse
-
-
-if __name__ == "__main__":
-    print("This method is deprecaded and will not be longer supported, please use 'pre.py'")
-    parser = argparse.ArgumentParser(
-        description="Creates embeddings for the synthesizer from the LibriSpeech utterances.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument("synthesizer_root", type=Path, help=\
-        "Path to the synthesizer training data that contains the audios and the train.txt file. "
-        "If you let everything as default, it should be <datasets_root>/SV2TTS/synthesizer/.")
-    parser.add_argument("-e", "--encoder_model_fpath", type=Path, 
-                        default="data/ckpt/encoder/pretrained.pt", help=\
-        "Path your trained encoder model.")
-    parser.add_argument("-n", "--n_processes", type=int, default=4, help= \
-        "Number of parallel processes. An encoder is created for each, so you may need to lower "
-        "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy.")
-    args = parser.parse_args()
-    
-    # Preprocess the dataset
-    print_args(args, parser)
-    create_embeddings(**vars(args))