optimize(train): move discriminators into rvc

infer/modules/train/train.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import logging
+from typing import Tuple
 
 logger = logging.getLogger(__name__)
 logging.getLogger("numba").setLevel(logging.WARNING)
@@ -55,8 +56,9 @@ from infer.lib.train.data_utils import (
     TextAudioLoaderMultiNSFsid,
 )
 
+from rvc.discriminators import MultiPeriodDiscriminator
+
 if hps.version == "v1":
-    from infer.lib.infer_pack.models import MultiPeriodDiscriminator
     from infer.lib.infer_pack.models import SynthesizerTrnMs256NSFsid as RVC_Model_f0
     from infer.lib.infer_pack.models import (
         SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
@@ -65,7 +67,6 @@ else:
     from infer.lib.infer_pack.models import (
         SynthesizerTrnMs768NSFsid as RVC_Model_f0,
         SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
     )
 
 from infer.lib.train.losses import (
@@ -180,7 +181,12 @@ def run(rank, n_gpus, hps, logger: logging.Logger):
         )
     if torch.cuda.is_available():
         net_g = net_g.cuda(rank)
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
+    has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
+    net_d = MultiPeriodDiscriminator(
+        hps.version,
+        use_spectral_norm=hps.model.use_spectral_norm,
+        has_xpu=has_xpu,
+    )
     if torch.cuda.is_available():
         net_d = net_d.cuda(rank)
     optim_g = torch.optim.AdamW(
@@ -298,7 +304,7 @@ def run(rank, n_gpus, hps, logger: logging.Logger):
 
 
 def train_and_evaluate(
-    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
+    rank, epoch, hps, nets: Tuple[RVC_Model_f0, MultiPeriodDiscriminator], optims, schedulers, scaler, loaders, logger, writers, cache
 ):
     net_g, net_d = nets
     optim_g, optim_d = optims