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Runtime error
Musa
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27ccf6a
1
Parent(s):
d12d70f
Update app.py
Browse files
app.py
CHANGED
@@ -19,6 +19,10 @@ config = Config()
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logging.getLogger("numba").setLevel(logging.WARNING)
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limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
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def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
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def vc_fn(
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input_audio,
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@@ -74,20 +78,6 @@ def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
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return info, (None, None)
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return vc_fn
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def load_hubert():
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global hubert_model
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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def change_to_tts_mode(tts_mode):
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if tts_mode:
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return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
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@@ -97,8 +87,12 @@ def change_to_tts_mode(tts_mode):
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if __name__ == '__main__':
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load_hubert()
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models = []
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tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
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voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
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if limitation:
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with open("weights/model_info.json", "r", encoding="utf-8") as f:
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models_info = json.load(f)
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logging.getLogger("numba").setLevel(logging.WARNING)
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limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
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# Load the whisper_large_v2 interface
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whisper_interface = gr.Interface.load("models/openai/whisper-large-v2")
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def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
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def vc_fn(
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input_audio,
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return info, (None, None)
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return vc_fn
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def change_to_tts_mode(tts_mode):
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if tts_mode:
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return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
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if __name__ == '__main__':
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load_hubert()
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models = []
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tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
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voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
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# Add the whisper_large_v2 model to the models list
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models.append(("Whisper v2", "Whisper v2", "OpenAI", None, whisper_interface))
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if limitation:
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with open("weights/model_info.json", "r", encoding="utf-8") as f:
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models_info = json.load(f)
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