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@ -16,6 +16,7 @@ logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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import pdb
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import torch
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if os.path.exists("./gweight.txt"):
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if os.path.exists("./gweight.txt"):
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with open("./gweight.txt", 'r', encoding="utf-8") as file:
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with open("./gweight.txt", 'r', encoding="utf-8") as file:
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@ -48,11 +49,11 @@ is_share = os.environ.get("is_share", "False")
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is_share = eval(is_share)
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is_share = eval(is_share)
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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is_half = eval(os.environ.get("is_half", "True"))
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is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available()
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import gradio as gr
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import gradio as gr
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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import numpy as np
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import librosa, torch
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import librosa
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from feature_extractor import cnhubert
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from feature_extractor import cnhubert
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cnhubert.cnhubert_base_path = cnhubert_base_path
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cnhubert.cnhubert_base_path = cnhubert_base_path
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