Update app.py

app.py

@@ -10,30 +10,45 @@ from scipy.io import wavfile
 import scipy.signal as sps
 from denoiser.demucs import Demucs
 from pydub import AudioSegment
-    
-modelpath = './denoiser/master64.th'
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# 設定 Hugging Face Hub 的 Access Token
+auth_token = os.getenv("HF_HOME") 
+
+# 加載私有模型
+model_id = "DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser"
+model = AutoModelForSequenceClassification.from_pretrained(model_id, use_auth_token=os.environ["HF_TOKEN"])
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=os.environ["HF_TOKEN"])
 
 def transcribe(file_upload, microphone):
     file = microphone if microphone is not None else file_upload
-    model = Demucs(hidden=64)
+    demucs_model = Demucs(hidden=64)
     state_dict = torch.load(modelpath, map_location='cpu')
-    model.load_state_dict(state_dict)
-    demucs = model
+    demucs_model.load_state_dict(state_dict)
     x, sr = torchaudio.load(file)
-    out = demucs(x[None])[0]
+    out = demucs_model(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.export('enhanced.wav', format="wav", bitrate="256k")
-    return "enhanced.wav"
+
+    # 假設模型是用於文本分類
+    inputs = tokenizer(enhanced, 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"),
+        gr.Textbox(label="Model Predictions")
     ],
-    outputs=gr.Audio(type="filepath", label="Output"),
     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"
@@ -47,4 +62,4 @@ demo = gr.Interface(
     ],
 )
 
-demo.launch(enable_queue=True)
+demo.launch(debug=True)