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mrfakename
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- model/trainer.py +1 -1
- model/utils.py +2 -2
- scripts/eval_infer_batch.py +1 -1
- speech_edit.py +2 -1
model/trainer.py
CHANGED
@@ -140,7 +140,7 @@ class Trainer:
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else:
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latest_checkpoint = sorted([f for f in os.listdir(self.checkpoint_path) if f.endswith('.pt')], key=lambda x: int(''.join(filter(str.isdigit, x))))[-1]
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# checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device) # rather use accelerator.load_state ಥ_ಥ
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-
checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu")
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if self.is_main:
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self.ema_model.load_state_dict(checkpoint['ema_model_state_dict'])
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else:
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latest_checkpoint = sorted([f for f in os.listdir(self.checkpoint_path) if f.endswith('.pt')], key=lambda x: int(''.join(filter(str.isdigit, x))))[-1]
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# checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device) # rather use accelerator.load_state ಥ_ಥ
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+
checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu")
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if self.is_main:
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self.ema_model.load_state_dict(checkpoint['ema_model_state_dict'])
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model/utils.py
CHANGED
@@ -509,7 +509,7 @@ def run_sim(args):
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device = f"cuda:{rank}"
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model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type='wavlm_large', config_path=None)
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-
state_dict = torch.load(ckpt_dir, map_location=lambda storage, loc: storage)
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model.load_state_dict(state_dict['model'], strict=False)
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use_gpu=True if torch.cuda.is_available() else False
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@@ -565,7 +565,7 @@ def load_checkpoint(model, ckpt_path, device, use_ema = True):
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from safetensors.torch import load_file
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checkpoint = load_file(ckpt_path, device=device)
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else:
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-
checkpoint = torch.load(ckpt_path, map_location=device)
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if use_ema == True:
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ema_model = EMA(model, include_online_model = False).to(device)
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device = f"cuda:{rank}"
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model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type='wavlm_large', config_path=None)
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state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
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model.load_state_dict(state_dict['model'], strict=False)
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use_gpu=True if torch.cuda.is_available() else False
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from safetensors.torch import load_file
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checkpoint = load_file(ckpt_path, device=device)
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else:
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checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
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if use_ema == True:
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ema_model = EMA(model, include_online_model = False).to(device)
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scripts/eval_infer_batch.py
CHANGED
@@ -127,7 +127,7 @@ local = False
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if local:
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vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
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vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
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-
state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device)
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vocos.load_state_dict(state_dict)
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vocos.eval()
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else:
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if local:
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vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
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vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
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state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device)
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vocos.load_state_dict(state_dict)
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vocos.eval()
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else:
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speech_edit.py
CHANGED
@@ -85,8 +85,9 @@ local = False
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if local:
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vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
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vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
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-
state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device)
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vocos.load_state_dict(state_dict)
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vocos.eval()
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else:
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vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
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if local:
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vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
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vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
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+
state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device)
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vocos.load_state_dict(state_dict)
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+
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vocos.eval()
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else:
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vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
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