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https://github.com/RVC-Boss/GPT-SoVITS.git
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check for models
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d7595cc783
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@ -4,7 +4,7 @@ import librosa
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import torch
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import torch.nn.functional as F
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import soundfile as sf
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import logging
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import logging, os
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logging.getLogger("numba").setLevel(logging.WARNING)
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@ -22,9 +22,11 @@ cnhubert_base_path = None
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class CNHubert(nn.Module):
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def __init__(self):
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super().__init__()
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self.model = HubertModel.from_pretrained(cnhubert_base_path)
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if os.path.exists(cnhubert_base_path):...
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else:raise FileNotFoundError(cnhubert_base_path)
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self.model = HubertModel.from_pretrained(cnhubert_base_path, local_files_only=True)
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self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
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cnhubert_base_path
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cnhubert_base_path, local_files_only=True
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)
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def forward(self, x):
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@ -54,6 +54,8 @@ if os.path.exists(txt_path) == False:
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# device = "mps"
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else:
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device = "cpu"
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if os.path.exists(bert_pretrained_dir):...
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else:raise FileNotFoundError(bert_pretrained_dir)
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tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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@ -34,6 +34,8 @@ logging.getLogger("numba").setLevel(logging.WARNING)
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# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[5]
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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if os.path.exists(pretrained_s2G):...
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else:raise FileNotFoundError(pretrained_s2G)
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hubert_dir = "%s/4-cnhubert" % (opt_dir)
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semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
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