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extract_feature_print.py

@@ -3,7 +3,7 @@ import os, sys, traceback
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
 
-# device=sys.argv[1]
+device=sys.argv[1]
 n_part = int(sys.argv[2])
 i_part = int(sys.argv[3])
 if len(sys.argv) == 6:
@@ -18,13 +18,22 @@ import torch
 import torch.nn.functional as F
 import soundfile as sf
 import numpy as np
-from fairseq import checkpoint_utils
+import fairseq
 
-device = "cpu"
-if torch.cuda.is_available():
-    device = "cuda"
-elif torch.backends.mps.is_available():
-    device = "mps"
+if("privateuseone"not in device):
+    device = "cpu"
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif torch.backends.mps.is_available():
+        device = "mps"
+else:
+    import torch_directml
+    device = torch_directml.device(torch_directml.default_device())
+    def forward_dml(ctx, x, scale):
+        ctx.scale = scale
+        res = x.clone().detach()
+        return res
+    fairseq.modules.grad_multiply.GradMultiply.forward=forward_dml
 
 f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
 
@@ -70,7 +79,7 @@ if os.access(model_path, os.F_OK) == False:
         % model_path
     )
     exit(0)
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [model_path],
     suffix="",
 )