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app.py

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+import streamlit as st
+import whisper
+from docx import Document
+import os
+import ssl
+import shutil
+
+# 设置缓存文件夹路径
+CACHE_DIR = "transcription_cache"
+
+# 确保缓存目录存在
+if not os.path.exists(CACHE_DIR):
+    os.makedirs(CACHE_DIR)
+
+# 禁用SSL证书验证（仅限临时使用，如果模型已本地化可移除）
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# 加载本地模型（确保模型文件路径正确）
+model_path = "small.pt"  # 请确保模型已下载到此路径
+model = whisper.load_model(model_path)
+
+def format_time(seconds):
+    """格式化秒数为分钟:秒钟的形式"""
+    mins, secs = divmod(int(seconds), 60)
+    return f"{mins:02}:{secs:02}"
+
+def transcribe_and_generate_docx(audio_path, cache_subdir):
+    audio = whisper.load_audio(audio_path)
+    result = model.transcribe(audio, language=None)  # 自动检测语言
+    segments = result["segments"]
+
+    # 构建文档
+    doc = Document()
+    doc.add_heading("语音转录结果", level=1)
+
+    transcript_preview = []
+    for i, segment in enumerate(segments):
+        start = segment['start']
+        end = segment['end']
+        text = segment['text'].strip()
+
+        timestamp = f"[{format_time(start)} - {format_time(end)}]"
+        paragraph_text = f"{timestamp} {text}"
+
+        doc.add_paragraph(paragraph_text)
+        transcript_preview.append(paragraph_text)
+
+    output_filename = os.path.join(cache_subdir, f"transcript_{os.path.basename(audio_path)}.docx")
+    doc.save(output_filename)
+
+    return output_filename, '\n\n'.join(transcript_preview)
+
+# Streamlit界面定义
+st.title("语音转录系统")
+st.write("上传音频文件进行语音转录并生成Word文档。支持多种语言自动检测及时间戳。")
+
+uploaded_file = st.file_uploader("选择一个音频文件", type=['mp3', 'wav','mp4', 'aac','m4a'])
+
+if uploaded_file is not None:
+    # 创建一个新的子目录来存储本次任务的文件
+    cache_subdir = os.path.join(CACHE_DIR, str(len(os.listdir(CACHE_DIR)) + 1))
+    os.makedirs(cache_subdir)
+    
+    # 保存上传的音频文件到缓存目录
+    temp_audio_path = os.path.join(cache_subdir, uploaded_file.name)
+    with open(temp_audio_path, "wb") as f:
+        f.write(uploaded_file.getvalue())
+    
+    # 执行转录
+    output_filename, transcript_preview = transcribe_and_generate_docx(temp_audio_path, cache_subdir)
+    
+    # 提供下载链接
+    st.download_button(
+        label="下载DOCX文件",
+        data=open(output_filename, "rb").read(),
+        file_name=output_filename,
+        mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    )
+    
+    # 显示转录预览
+    st.text_area("转录结果预览", value=transcript_preview, height=400)
+    
+    # 展示历史记录
+    st.subheader("历史记录")
+    for idx, task_dir in enumerate(sorted(os.listdir(CACHE_DIR), key=lambda x: int(x))):
+        st.markdown(f"### 任务 {idx + 1}")
+        audio_files = [f for f in os.listdir(os.path.join(CACHE_DIR, task_dir)) if f.endswith('.mp3')]
+        docx_files = [f for f in os.listdir(os.path.join(CACHE_DIR, task_dir)) if f.endswith('.docx')]
+        
+        if audio_files:
+            st.audio(os.path.join(CACHE_DIR, task_dir, audio_files[0]), format='audio/mp3')
+        if docx_files:
+            with open(os.path.join(CACHE_DIR, task_dir, docx_files[0]), "rb") as file_content:
+                st.download_button(label=f"下载任务 {idx + 1} 的 DOCX 文件", data=file_content, file_name=docx_files[0])

requirements.txt

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+openai-whisper
+python-docx
+librosa
+soundfile
+scipy
+numpy