Update app.py

app.py

@@ -1,38 +1,35 @@
 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
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-# 設定 Hugging Face Hub 的 Access Token
-auth_token = os.getenv("HF_HOME") 
+# 设置 Hugging Face Hub 的 Access Token
+auth_token = os.getenv("HF_TOKEN")
 
-# 加載私有模型
+# 加载私有模型
 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)
+model = AutoModelForSequenceClassification.from_pretrained(model_id, use_auth_token=auth_token)
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=auth_token)
 
 def transcribe(file_upload, microphone):
     file = microphone if microphone is not None else file_upload
     demucs_model = Demucs(hidden=64)
-    state_dict = torch.load(modelpath, map_location='cpu')
+    state_dict = torch.load("path_to_model_checkpoint", map_location='cpu')  # 请确保提供正确的模型文件路径
     demucs_model.load_state_dict(state_dict)
     x, sr = torchaudio.load(file)
     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 = AudioSegment.from_wav('enhanced.wav')  # 只有去完噪的需要降bitrate再做语音识别
     enhanced.export('enhanced.wav', format="wav", bitrate="256k")
 
-    # 假設模型是用於文本分類
+    # 假设模型是用于文本分类
     inputs = tokenizer(enhanced, return_tensors="pt")
     outputs = model(**inputs)
     predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
@@ -42,17 +39,15 @@ def transcribe(file_upload, microphone):
 demo = gr.Interface(
     fn=transcribe,
     inputs=[
-        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"),
+        gr.Audio(type="filepath", label="语音质检麦克风实时录音"),
+        gr.Audio(type="filepath", label="语音质检原始音档"),
     ],
     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"
-    ),
+    title="<p style='text-align: center'><a href='https://www.twman.org/AI' target='_blank'>语音质检噪音去除 (语音增强)：Meta Denoiser</a>",
+    description="为了提升语音识别的效果，可以在识别前先进行噪音去除",
     allow_flagging="never",
     examples=[
         ["exampleAudio/15s_2020-03-27_sep1.wav"],
@@ -62,4 +57,4 @@ demo = gr.Interface(
     ],
 )
 
-demo.launch(debug=True)
+demo.launch(debug=True)