diff --git a/src/python/larynx_train/export_generator.py b/src/python/larynx_train/export_generator.py
new file mode 100644
index 0000000..7421bdd
--- /dev/null
+++ b/src/python/larynx_train/export_generator.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+import argparse
+import logging
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+from .vits.lightning import VitsModel
+
+_LOGGER = logging.getLogger("larynx_train.export_generator")
+
+
+def main():
+    """Main entry point"""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint", help="Path to model checkpoint (.ckpt)")
+    parser.add_argument("output", help="Path to output model (.pt)")
+
+    parser.add_argument(
+        "--debug", action="store_true", help="Print DEBUG messages to the console"
+    )
+    args = parser.parse_args()
+
+    if args.debug:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
+
+    _LOGGER.debug(args)
+
+    # -------------------------------------------------------------------------
+
+    args.checkpoint = Path(args.checkpoint)
+    args.output = Path(args.output)
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    model = VitsModel.load_from_checkpoint(args.checkpoint)
+    model_g = model.model_g
+
+    # Inference only
+    model_g.eval()
+
+    with torch.no_grad():
+        model_g.dec.remove_weight_norm()
+
+    model_g.forward = model_g.infer
+
+    torch.save(model_g, args.output)
+
+    _LOGGER.info("Exported model to %s", args.output)
+
+
+# -----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    main()
diff --git a/src/python/larynx_train/infer_generator.py b/src/python/larynx_train/infer_generator.py
new file mode 100644
index 0000000..e58f608
--- /dev/null
+++ b/src/python/larynx_train/infer_generator.py
@@ -0,0 +1,86 @@
+#!/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("larynx_train.infer_generator")
+
+
+def main():
+    """Main entry point"""
+    logging.basicConfig(level=logging.DEBUG)
+    parser = argparse.ArgumentParser(prog="larynx_train.infer_generator")
+    parser.add_argument(
+        "--model", required=True, help="Path to generator (.pt)"
+    )
+    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 = torch.load(args.model)
+
+    # Inference only
+    model.eval()
+
+    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)])
+        sid = torch.LongTensor([speaker_id]) if speaker_id is not None else None
+
+        start_time = time.perf_counter()
+        audio = (
+            model(
+                text,
+                text_lengths,
+                sid,
+                # torch.FloatTensor([0.667]),
+                # torch.FloatTensor([1.0]),
+                # torch.FloatTensor([0.8]),
+            )[0]
+            .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()
diff --git a/src/python/larynx_train/infer_torchscript.py b/src/python/larynx_train/infer_torchscript.py
index 84d5afb..b0223ac 100755
--- a/src/python/larynx_train/infer_torchscript.py
+++ b/src/python/larynx_train/infer_torchscript.py
@@ -32,10 +32,7 @@ def main():
     model = torch.jit.load(args.model)
 
     # Inference only
-    # model.eval()
-
-    # with torch.no_grad():
-    #     model.model_g.dec.remove_weight_norm()
+    model.eval()
 
     for i, line in enumerate(sys.stdin):
         line = line.strip()
@@ -49,7 +46,6 @@ def main():
 
         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()