Refactor Project to 3 parts: Models, Control, Data

Need readme

control/cli/encoder_preprocess.py

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+import argparse
+from pathlib import Path
+
+from models.encoder.preprocess import (preprocess_aidatatang_200zh,
+                                preprocess_librispeech, preprocess_voxceleb1,
+                                preprocess_voxceleb2)
+from utils.argutils import print_args
+
+if __name__ == "__main__":
+    class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
+        pass
+    
+    parser = argparse.ArgumentParser(
+        description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
+                    "writes them to the disk. This will allow you to train the encoder. The "
+                    "datasets required are at least one of LibriSpeech, VoxCeleb1, VoxCeleb2, aidatatang_200zh. ",
+        formatter_class=MyFormatter
+    )
+    parser.add_argument("datasets_root", type=Path, help=\
+        "Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.")
+    parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
+        "Path to the output directory that will contain the mel spectrograms. If left out, "
+        "defaults to <datasets_root>/SV2TTS/encoder/")
+    parser.add_argument("-d", "--datasets", type=str, 
+                        default="librispeech_other,voxceleb1,aidatatang_200zh", help=\
+        "Comma-separated list of the name of the datasets you want to preprocess. Only the train "
+        "set of these datasets will be used. Possible names: librispeech_other, voxceleb1, "
+        "voxceleb2.")
+    parser.add_argument("-s", "--skip_existing", action="store_true", help=\
+        "Whether to skip existing output files with the same name. Useful if this script was "
+        "interrupted.")
+    parser.add_argument("--no_trim", action="store_true", help=\
+        "Preprocess audio without trimming silences (not recommended).")
+    args = parser.parse_args()
+
+    # 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
+
+    # Process the arguments
+    args.datasets = args.datasets.split(",")
+    if not hasattr(args, "out_dir"):
+        args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder")
+    assert args.datasets_root.exists()
+    args.out_dir.mkdir(exist_ok=True, parents=True)
+
+    # Preprocess the datasets
+    print_args(args, parser)
+    preprocess_func = {
+        "librispeech_other": preprocess_librispeech,
+        "voxceleb1": preprocess_voxceleb1,
+        "voxceleb2": preprocess_voxceleb2,
+        "aidatatang_200zh": preprocess_aidatatang_200zh,
+    }
+    args = vars(args)
+    for dataset in args.pop("datasets"):
+        print("Preprocessing %s" % dataset)
+        preprocess_func[dataset](**args)