def main(
metadata_path,
source_dir,
target_dir,
output_dir,
root,
batch_size,
reload,
reload_dir,
):
"""Main function"""
# import Inferencer module
inferencer = Inferencer(root)
device = inferencer.device
sample_rate = inferencer.sample_rate
print(f"[INFO]: Inferencer is loaded from {root}.")
metadata = json.load(open(metadata_path))
print(f"[INFO]: Metadata list is loaded from {metadata_path}.")
output_dir = Path(output_dir) / Path(root).stem / \
f"{metadata['source_corpus']}2{metadata['target_corpus']}"
output_dir.mkdir(parents=True, exist_ok=True)
if reload:
metadata, conv_mels = reload_from_numpy(device, metadata, reload_dir)
else:
metadata, conv_mels = conversion(inferencer, device, root, metadata,
source_dir, target_dir, output_dir)
waveforms = []
max_memory_use = conv_mels[0].size(0) * batch_size
with torch.no_grad():
pbar = tqdm(total=metadata["n_samples"])
left = 0
while (left < metadata["n_samples"]):
batch_size = max_memory_use // conv_mels[left].size(0) - 1
right = left + min(batch_size, metadata["n_samples"] - left)
waveforms.extend(
inferencer.spectrogram2waveform(conv_mels[left:right]))
pbar.update(batch_size)
left += batch_size
pbar.close()
for pair, waveform in tqdm(zip(metadata["pairs"], waveforms)):
waveform = waveform.detach().cpu().numpy()
prefix = Path(pair["src_utt"]).stem
postfix = Path(pair["tgt_utts"][0]).stem
file_path = output_dir / f"{prefix}_to_{postfix}.wav"
pair["converted"] = f"{prefix}_to_{postfix}.wav"
if Path(root).stem == "BLOW":
wavfile.write(file_path, sample_rate, waveform)
else:
sf.write(file_path, waveform, sample_rate)
metadata_output_path = output_dir / "metadata.json"
json.dump(metadata, metadata_output_path.open("w"), indent=2)
