Update app.py

app.py

@@ -1,29 +1,3 @@
-# import os
-# import time
-# import json
-# import gradio as gr
-# import torch
-# import torchaudio
-# import numpy as np
-# from denoiser.demucs import Demucs
-# from pydub import AudioSegment
-
-# modelpath = './denoiser/master64.th'
-
-# def transcribe(file_upload, microphone):
-#     file = microphone if microphone is not None else file_upload
-#     model = Demucs(hidden=64)
-#     state_dict = torch.load(modelpath, map_location='cpu')
-#     model.load_state_dict(state_dict)
-#     demucs = model
-#     x, sr = torchaudio.load(file)
-#     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.export('enhanced.wav', format="wav", bitrate="256k")
-#     return "enhanced.wav"
-
 import os
 import time
 import json
@@ -33,67 +7,93 @@ import torchaudio
 import numpy as np
 from denoiser.demucs import Demucs
 from pydub import AudioSegment
-import soundfile as sf
-import librosa
 
 modelpath = './denoiser/master64.th'
 
 def transcribe(file_upload, microphone):
     file = microphone if microphone is not None else file_upload
-
-    # 新增音訊預處理 → 統一格式
-    def preprocess_audio(path):
-        data, sr = sf.read(path)
-        
-        # 如果是雙聲道 → 轉單聲道
-        if len(data.shape) > 1:
-            data = data.mean(axis=1)
-
-        # 如果不是 16kHz → 重採樣
-        if sr != 16000:
-            data = librosa.resample(data, orig_sr=sr, target_sr=16000)
-            sr = 16000
-
-        # 儲存為 WAV 供模型使用
-        sf.write("enhanced.wav", data, sr)
-        return "enhanced.wav"
-
-    # 如果是 MP3，先轉成 WAV 再處理
-    if file.lower().endswith(".mp3"):
-        audio = AudioSegment.from_file(file)
-        audio = audio.set_frame_rate(16000).set_channels(1)  # 轉單聲道 + 16kHz
-        audio.export("enhanced.wav", format="wav")
-        file = "enhanced.wav"
-    else:
-        file = preprocess_audio(file)
-
     model = Demucs(hidden=64)
     state_dict = torch.load(modelpath, map_location='cpu')
     model.load_state_dict(state_dict)
-    demucs = model.eval()
-
+    demucs = model
     x, sr = torchaudio.load(file)
-    x = x[0:1]  # 強制取第一個聲道（確保是單聲道）
+    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.export('enhanced.wav', format="wav", bitrate="256k")
+    return "enhanced.wav"
 
-    with torch.no_grad():
-        out = demucs(x[None])[0]
+# import os
+# import time
+# import json
+# import gradio as gr
+# import torch
+# import torchaudio
+# import numpy as np
+# from denoiser.demucs import Demucs
+# from pydub import AudioSegment
+# import soundfile as sf
+# import librosa
 
-    out = out / max(out.abs().max().item(), 1)
-    torchaudio.save('enhanced_final.wav', out, sr)
+# modelpath = './denoiser/master64.th'
+
+# def transcribe(file_upload, microphone):
+#     file = microphone if microphone is not None else file_upload
+
+#     # 新增音訊預處理 → 統一格式
+#     def preprocess_audio(path):
+#         data, sr = sf.read(path)
+        
+#         # 如果是雙聲道 → 轉單聲道
+#         if len(data.shape) > 1:
+#             data = data.mean(axis=1)
+
+#         # 如果不是 16kHz → 重採樣
+#         if sr != 16000:
+#             data = librosa.resample(data, orig_sr=sr, target_sr=16000)
+#             sr = 16000
+
+#         # 儲存為 WAV 供模型使用
+#         sf.write("enhanced.wav", data, sr)
+#         return "enhanced.wav"
+
+#     # 如果是 MP3，先轉成 WAV 再處理
+#     if file.lower().endswith(".mp3"):
+#         audio = AudioSegment.from_file(file)
+#         audio = audio.set_frame_rate(16000).set_channels(1)  # 轉單聲道 + 16kHz
+#         audio.export("enhanced.wav", format="wav")
+#         file = "enhanced.wav"
+#     else:
+#         file = preprocess_audio(file)
+
+#     model = Demucs(hidden=64)
+#     state_dict = torch.load(modelpath, map_location='cpu')
+#     model.load_state_dict(state_dict)
+#     demucs = model.eval()
+
+#     x, sr = torchaudio.load(file)
+#     x = x[0:1]  # 強制取第一個聲道（確保是單聲道）
+
+#     with torch.no_grad():
+#         out = demucs(x[None])[0]
+
+#     out = out / max(out.abs().max().item(), 1)
+#     torchaudio.save('enhanced_final.wav', out, sr)
 
-    # 輸出 WAV 格式給前端播放
-    enhanced = AudioSegment.from_wav('enhanced_final.wav')
-    enhanced.export('enhanced_final.mp3', format="mp3", bitrate="256k")
+#     # 輸出 WAV 格式給前端播放
+#     enhanced = AudioSegment.from_wav('enhanced_final.wav')
+#     enhanced.export('enhanced_final.mp3', format="mp3", bitrate="256k")
 
-    return "enhanced_final.mp3"  # 回傳 MP3 更省空間
+#     return "enhanced_final.mp3"  # 回傳 MP3 更省空間
 
 
-# 👇 加上這一行，解決 Gradio schema 推導錯誤
-transcribe.__annotations__ = {
-    "file_upload": str,
-    "microphone": str,
-    "return": str
-}
+# # 👇 加上這一行，解決 Gradio schema 推導錯誤
+# transcribe.__annotations__ = {
+#     "file_upload": str,
+#     "microphone": str,
+#     "return": str
+# }
 
 demo = gr.Interface(
     fn=transcribe,