from synthesizer.preprocess import create_embeddings from utils.argutils import print_args from pathlib import Path import argparse from synthesizer.preprocess import preprocess_dataset from 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__": 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 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 /SV2TTS/synthesizer/") parser.add_argument("-n", "--n_processes", type=int, default=1, 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("-d", "--dataset", type=str, default="aidatatang_200zh", help=\ "Name of the dataset to process, allowing values: magicdata, aidatatang_200zh, aishell3.") parser.add_argument("-e", "--encoder_model_fpath", type=Path, default="encoder/saved_models/pretrained.pt", help=\ "Path your trained encoder model.") parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\ "Number of processes in parallel.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() # 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.") encoder_model_fpath = args.encoder_model_fpath del args.no_trim, args.encoder_model_fpath args.hparams = hparams.parse(args.hparams) n_processes_embed = args.n_processes_embed del args.n_processes_embed preprocess_dataset(**vars(args)) create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath)