Rename to piper

src/python/piper_train/infer.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import logging
+import sys
+import time
+from pathlib import Path
+
+import torch
+
+from .vits.lightning import VitsModel
+from .vits.utils import audio_float_to_int16
+from .vits.wavfile import write as write_wav
+
+_LOGGER = logging.getLogger("piper_train.infer")
+
+
+def main():
+    """Main entry point"""
+    logging.basicConfig(level=logging.DEBUG)
+    parser = argparse.ArgumentParser(prog="piper_train.infer")
+    parser.add_argument(
+        "--checkpoint", required=True, help="Path to model checkpoint (.ckpt)"
+    )
+    parser.add_argument("--output-dir", required=True, help="Path to write WAV files")
+    parser.add_argument("--sample-rate", type=int, default=22050)
+    args = parser.parse_args()
+
+    args.output_dir = Path(args.output_dir)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    model = VitsModel.load_from_checkpoint(args.checkpoint, dataset=None)
+
+    # Inference only
+    model.eval()
+
+    with torch.no_grad():
+        model.model_g.dec.remove_weight_norm()
+
+    for i, line in enumerate(sys.stdin):
+        line = line.strip()
+        if not line:
+            continue
+
+        utt = json.loads(line)
+        utt_id = str(i)
+        phoneme_ids = utt["phoneme_ids"]
+        speaker_id = utt.get("speaker_id")
+
+        text = torch.LongTensor(phoneme_ids).unsqueeze(0)
+        text_lengths = torch.LongTensor([len(phoneme_ids)])
+        scales = [0.667, 1.0, 0.8]
+        sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None
+
+        start_time = time.perf_counter()
+        audio = model(text, text_lengths, scales, sid=sid).detach().numpy()
+        audio = audio_float_to_int16(audio)
+        end_time = time.perf_counter()
+
+        audio_duration_sec = audio.shape[-1] / args.sample_rate
+        infer_sec = end_time - start_time
+        real_time_factor = (
+            infer_sec / audio_duration_sec if audio_duration_sec > 0 else 0.0
+        )
+
+        _LOGGER.debug(
+            "Real-time factor for %s: %0.2f (infer=%0.2f sec, audio=%0.2f sec)",
+            i + 1,
+            real_time_factor,
+            infer_sec,
+            audio_duration_sec,
+        )
+
+        output_path = args.output_dir / f"{utt_id}.wav"
+        write_wav(str(output_path), args.sample_rate, audio)
+
+
+if __name__ == "__main__":
+    main()