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96269dd
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This Space is synced from the GitHub repo: https://github.com/SWivid/F5-TTS. Please submit contributions to the Space there

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'])
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
@@ -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)
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:
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")
 
85
  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")