w/o cropping mode

1 files changed, 66 insertions, 0 deletions

diff --git a/scripts/voice_conversion_test.py b/scripts/voice_conversion_test.py
new file mode 100644
index 0000000..e0ed8c4
--- /dev/null
+++ b/scripts/voice_conversion_test.py
@@ -0,0 +1,66 @@
+import argparse
+import glob
+import multiprocessing
+import re
+from functools import partial
+from pathlib import Path
+
+import librosa
+import numpy
+
+from become_yukarin import VoiceChanger
+from become_yukarin.config import create_from_json as create_config
+
+parser = argparse.ArgumentParser()
+parser.add_argument('model_names', nargs='+')
+parser.add_argument('-md', '--model_directory', type=Path, default=Path('/mnt/dwango/hiroshiba/become-yukarin/'))
+parser.add_argument('-iwd', '--input_wave_directory', type=Path,
+                    default=Path('/mnt/dwango/hiroshiba/become-yukarin/dataset/hiho-wave/hiho-pause-atr503-subset/'))
+args = parser.parse_args()
+
+model_directory = args.model_directory  # type: Path
+input_wave_directory = args.input_wave_directory  # type: Path
+
+paths_test = list(Path('./test_data/').glob('*.wav'))
+
+
+def extract_number(f):
+    s = re.findall("\d+", str(f))
+    return int(s[-1]) if s else -1
+
+
+def process(p: Path, voice_changer: VoiceChanger):
+    try:
+        if p.suffix in ['.npy', '.npz']:
+            p = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
+            p = Path(p)
+        wave = voice_changer(p)
+        librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
+    except:
+        import traceback
+        print('error!', str(p))
+        traceback.format_exc()
+
+
+for model_name in args.model_names:
+    base_model = model_directory / model_name
+    config = create_config(base_model / 'config.json')
+
+    input_paths = list(sorted([Path(p) for p in glob.glob(str(config.dataset.input_glob))]))
+    numpy.random.RandomState(config.dataset.seed).shuffle(input_paths)
+    path_train = input_paths[0]
+    path_test = input_paths[-1]
+
+    model_paths = base_model.glob('predictor*.npz')
+    model_path = list(sorted(model_paths, key=extract_number))[-1]
+    print(model_path)
+    voice_changer = VoiceChanger(config, model_path)
+
+    output = Path('./output').absolute() / base_model.name
+    output.mkdir(exist_ok=True)
+
+    paths = [path_train, path_test] + paths_test
+
+    process_partial = partial(process, voice_changer=voice_changer)
+    pool = multiprocessing.Pool()
+    pool.map(process_partial, paths)