Add models for voice conversion

voice_conversion/softvc/hifigan/resample.py

@@ -0,0 +1,56 @@
+import argparse
+from pathlib import Path
+from concurrent.futures import ProcessPoolExecutor
+from multiprocessing import cpu_count
+
+import torchaudio
+from torchaudio.functional import resample
+
+from tqdm import tqdm
+
+
+def process_wav(in_path, out_path, sample_rate):
+    wav, sr = torchaudio.load(in_path)
+    wav = resample(wav, sr, sample_rate)
+    torchaudio.save(out_path, wav, sample_rate)
+    return out_path, wav.size(-1) / sample_rate
+
+
+def preprocess_dataset(args):
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+
+    futures = []
+    executor = ProcessPoolExecutor(max_workers=cpu_count())
+    print(f"Resampling audio in {args.in_dir}")
+    for in_path in args.in_dir.rglob("*.wav"):
+        relative_path = in_path.relative_to(args.in_dir)
+        out_path = args.out_dir / relative_path
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        futures.append(
+            executor.submit(process_wav, in_path, out_path, args.sample_rate)
+        )
+
+    results = [future.result() for future in tqdm(futures)]
+
+    lengths = {path.stem: length for path, length in results}
+    seconds = sum(lengths.values())
+    hours = seconds / 3600
+    print(f"Wrote {len(lengths)} utterances ({hours:.2f} hours)")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Resample an audio dataset.")
+    parser.add_argument(
+        "in_dir", metavar="in-dir", help="path to the dataset directory.", type=Path
+    )
+    parser.add_argument(
+        "out_dir", metavar="out-dir", help="path to the output directory.", type=Path
+    )
+    parser.add_argument(
+        "--sample-rate",
+        help="target sample rate (default 16kHz)",
+        type=int,
+        default=16000,
+    )
+    args = parser.parse_args()
+    preprocess_dataset(args)