Create app.py

app.py

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+import os
+import time
+import json
+import random
+import gradio as gr
+import torch
+import torchaudio
+import numpy as np
+from scipy.io import wavfile
+import scipy.signal as sps
+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"
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="語音質檢麥克風實時錄音"),        
+        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="語音質檢原始音檔"),
+    ],
+    #outputs=gr.outputs.Textbox(label="語音質檢音檔噪音去除結果"),
+    outputs=gr.outputs.Audio(type="filepath", label="Output"),
+    title="語音質檢噪音去除 (語音增強)：Meta Denoiser",
+    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",
+    examples=[
+        ["exampleAudio/15s_2020-03-27_sep1.wav"],
+        ["exampleAudio/13s_2020-03-27_sep2.wav"],
+        ["exampleAudio/30s_2020-04-23_sep1.wav"],
+        ["exampleAudio/15s_2020-04-23_sep2.wav"],        
+    ],    
+)
+
+demo.launch(enable_queue=True)