Update app.py

app.py

@@ -7,15 +7,6 @@ import torchaudio
 import numpy as np
 from denoiser.demucs import Demucs
 from pydub import AudioSegment
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-# # 設置 Hugging Face Hub 的 Access Token
-# auth_token = os.getenv("HF_TOKEN")
-
-# # 加載私有模型
-# model_id = "DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser"
-# model = AutoModelForSequenceClassification.from_pretrained(model_id, token=auth_token)
-# tokenizer = AutoTokenizer.from_pretrained(model_id, token=auth_token)
 
 modelpath = './denoiser/master64.th'
 
@@ -29,28 +20,17 @@ def transcribe(file_upload, microphone):
     out = demucs(x[None])[0]
     out = out / max(out.abs().max().item(), 1)
     torchaudio.save('enhanced.wav', out, sr)
-    enhanced = AudioSegment.from_wav('enhanced.wav') #只有去完噪的需要降bitrate再做語音識別
+    enhanced = AudioSegment.from_wav('enhanced.wav') # 只有去完噪的需要降 bitrate 再做語音識別
     enhanced.export('enhanced.wav', format="wav", bitrate="256k")
     return "enhanced.wav"
 
-    # # 假設模型是用於文本分類
-    # inputs = tokenizer("enhanced.wav", return_tensors="pt")
-    # outputs = model(**inputs)
-    # predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-
-    # return "enhanced.wav", predictions
-
 demo = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(source="microphone", type="filepath", optional=True, label="語音質檢麥克風實時錄音"),        
-        gr.Audio(source="upload", type="filepath", optional=True, label="語音質檢原始音檔"),
+        gr.Audio(type="filepath", label="語音質檢麥克風實時錄音"),        
+        gr.Audio(type="filepath", label="語音質檢原始音檔"),
     ],
     outputs=gr.Audio(type="filepath", label="Output"),
-    # outputs=[
-    #     gr.Audio(type="filepath", label="Output"),
-    #     gr.Textbox(label="Model Predictions")
-    # ],
     title="<p style='text-align: center'><a href='https://www.twman.org/AI' target='_blank'>語音質檢噪音去除 (語音增強)：Meta Denoiser</a>",
     description="為了提升語音識別的效果，可以在識別前先進行噪音去除",
     allow_flagging="never",