Update app.py
Browse files
app.py
CHANGED
@@ -10,14 +10,15 @@ from werkzeug.utils import secure_filename
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import tempfile
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from moviepy.editor import VideoFileClip
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import logging
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import torchaudio
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#
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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app = Flask(__name__)
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#
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MAX_CONCURRENT_REQUESTS = 2
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MAX_FILE_DURATION = 60 * 30
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TEMPORARY_FOLDER = tempfile.gettempdir()
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@@ -25,15 +26,15 @@ ALLOWED_AUDIO_EXTENSIONS = {'mp3', 'wav', 'ogg', 'm4a', 'flac', 'aac', 'wma', 'o
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ALLOWED_VIDEO_EXTENSIONS = {'mp4', 'avi', 'mov', 'mkv', 'webm', 'flv', 'wmv', 'mpeg', 'mpg', '3gp'}
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ALLOWED_EXTENSIONS = ALLOWED_AUDIO_EXTENSIONS.union(ALLOWED_VIDEO_EXTENSIONS)
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API_KEY = os.environ.get("API_KEY")
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MODEL_NAME = os.environ.get("WHISPER_MODEL", "guillaumekln/faster-whisper-large-v2")
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#
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device = "cuda" if torch.cuda.is_available() else "cpu"
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compute_type = "float16" if device == "cuda" else "int8"
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logging.info(f"
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# Faster Whisper
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beamsize = 2
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try:
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wmodel = WhisperModel(
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@@ -42,12 +43,12 @@ try:
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compute_type=compute_type,
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download_root="./model_cache"
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)
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logging.info(f"
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except Exception as e:
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logging.error(f"
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wmodel = None
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#
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request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS)
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active_requests = 0
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@@ -70,29 +71,35 @@ def cleanup_temp_files(*file_paths):
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try:
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if file_path and os.path.exists(file_path):
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os.remove(file_path)
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logging.info(f"
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except Exception as e:
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logging.error(f"
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def extract_audio_from_video(video_path, output_audio_path):
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try:
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if video.duration > MAX_FILE_DURATION:
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video.close()
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raise ValueError(f"
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video.audio.write_audiofile(output_audio_path, codec='pcm_s16le') # Specify codec
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video.close()
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return output_audio_path
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except Exception as e:
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logging.exception("
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raise Exception(f"
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@app.route("/health", methods=["GET"])
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def health_check():
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return jsonify({
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'status': 'API
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'timestamp': datetime.datetime.now().isoformat(),
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'device': device,
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'compute_type': compute_type,
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@@ -118,10 +125,10 @@ def transcribe():
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global active_requests
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if not validate_api_key(request):
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return jsonify({'error': '
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if not request_semaphore.acquire(blocking=False):
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return jsonify({'error': '
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active_requests += 1
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start_time = time.time()
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@@ -130,20 +137,20 @@ def transcribe():
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try:
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if wmodel is None:
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return jsonify({'error': '
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if 'file' not in request.files:
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return jsonify({'error': '
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file = request.files['file']
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if not (file and allowed_file(file.filename)):
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return jsonify({'error': f'
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#
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temp_file_path = os.path.join(TEMPORARY_FOLDER, secure_filename(file.filename))
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file.save(temp_file_path)
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#
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file_extension = file.filename.rsplit('.', 1)[1].lower()
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is_video = file_extension in ALLOWED_VIDEO_EXTENSIONS
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@@ -153,19 +160,32 @@ def transcribe():
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transcription_file = temp_audio_path
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else:
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transcription_file = temp_file_path
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try:
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if duration > MAX_FILE_DURATION:
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raise ValueError(f"
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except Exception as
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logging.exception(f"
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segments, _ = wmodel.transcribe(
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transcription_file,
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beam_size=beamsize,
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@@ -182,21 +202,22 @@ def transcribe():
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}), 200
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except Exception as e:
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logging.exception("
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return jsonify({'error': str(e)}), 500
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finally:
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cleanup_temp_files(temp_file_path, temp_audio_path)
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active_requests -= 1
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request_semaphore.release()
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print(f"
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if __name__ == "__main__":
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#
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if not os.path.exists(TEMPORARY_FOLDER):
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os.makedirs(TEMPORARY_FOLDER)
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logging.info(f"
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app.run(host="0.0.0.0", port=7860, threaded=True)
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import tempfile
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from moviepy.editor import VideoFileClip
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import logging
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import torchaudio
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import ffmpeg # 导入 ffmpeg-python
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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app = Flask(__name__)
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# 配置
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MAX_CONCURRENT_REQUESTS = 2
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MAX_FILE_DURATION = 60 * 30
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TEMPORARY_FOLDER = tempfile.gettempdir()
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ALLOWED_VIDEO_EXTENSIONS = {'mp4', 'avi', 'mov', 'mkv', 'webm', 'flv', 'wmv', 'mpeg', 'mpg', '3gp'}
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ALLOWED_EXTENSIONS = ALLOWED_AUDIO_EXTENSIONS.union(ALLOWED_VIDEO_EXTENSIONS)
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API_KEY = os.environ.get("API_KEY")
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MODEL_NAME = os.environ.get("WHISPER_MODEL", "guillaumekln/faster-whisper-large-v2")
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# 设备检查
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device = "cuda" if torch.cuda.is_available() else "cpu"
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compute_type = "float16" if device == "cuda" else "int8"
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logging.info(f"使用设备: {device},计算类型: {compute_type}")
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# Faster Whisper 设置
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beamsize = 2
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try:
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wmodel = WhisperModel(
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compute_type=compute_type,
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download_root="./model_cache"
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)
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logging.info(f"模型 {MODEL_NAME} 加载成功.")
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except Exception as e:
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logging.error(f"加载模型 {MODEL_NAME} 失败: {e}")
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wmodel = None
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# 并发控制
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request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS)
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active_requests = 0
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try:
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if file_path and os.path.exists(file_path):
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os.remove(file_path)
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logging.info(f"删除临时文件: {file_path}")
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except Exception as e:
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logging.error(f"删除临时文件 {file_path} 出错: {str(e)}")
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def extract_audio_from_video(video_path, output_audio_path):
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try:
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# 使用 ffmpeg-python 调用 FFmpeg
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ffmpeg.input(video_path).output(output_audio_path, acodec='pcm_s16le').run(capture_stdout=True, capture_stderr=True)
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# or use with more options:
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# ffmpeg.input(video_path).output(output_audio_path, acodec='pcm_s16le', ar=44100, ac=2).run(capture_stdout=True, capture_stderr=True)
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# 检查视频时长
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video = VideoFileClip(video_path) # moviepy
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if video.duration > MAX_FILE_DURATION:
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video.close()
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raise ValueError(f"视频时长超过 {MAX_FILE_DURATION} 秒")
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video.close()
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return output_audio_path
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except Exception as e:
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logging.exception("提取视频中的音频出错")
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raise Exception(f"提取视频中的音频出错: {str(e)}")
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@app.route("/health", methods=["GET"])
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def health_check():
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return jsonify({
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'status': 'API 正在运行',
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'timestamp': datetime.datetime.now().isoformat(),
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'device': device,
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'compute_type': compute_type,
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global active_requests
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if not validate_api_key(request):
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return jsonify({'error': '无效的 API 密钥'}), 401
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if not request_semaphore.acquire(blocking=False):
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return jsonify({'error': '服务器繁忙'}), 503
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active_requests += 1
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start_time = time.time()
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try:
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if wmodel is None:
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return jsonify({'error': '模型加载失败。请检查服务器日志。'}), 500
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if 'file' not in request.files:
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return jsonify({'error': '未提供文件'}), 400
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file = request.files['file']
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if not (file and allowed_file(file.filename)):
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return jsonify({'error': f'无效的文件格式。支持:{", ".join(ALLOWED_EXTENSIONS)}'}), 400
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# 保存上传的文件到临时位置
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temp_file_path = os.path.join(TEMPORARY_FOLDER, secure_filename(file.filename))
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file.save(temp_file_path)
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# 检查是否是视频文件,如果是,则提取音频
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file_extension = file.filename.rsplit('.', 1)[1].lower()
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is_video = file_extension in ALLOWED_VIDEO_EXTENSIONS
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transcription_file = temp_audio_path
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else:
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transcription_file = temp_file_path
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# 检查音频文件时长
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try:
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# 使用 torchaudio.load 加载音频,并指定格式
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waveform, sample_rate = torchaudio.load(transcription_file, format=file_extension)
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duration = waveform.size(1) / sample_rate
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if duration > MAX_FILE_DURATION:
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raise ValueError(f"音频时长超过 {MAX_FILE_DURATION} 秒")
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except Exception as load_err:
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logging.exception(f"使用 torchaudio.load 加载音频文件出错: {transcription_file}")
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try:
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# 尝试使用 soundfile 后端加载 (禁用 sox_io)
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torchaudio.set_audio_backend("soundfile") # 强制使用 soundfile 后端
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waveform, sample_rate = torchaudio.load(transcription_file) # 不要指定文件扩展名
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duration = waveform.size(1) / sample_rate
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if duration > MAX_FILE_DURATION:
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raise ValueError(f"音频时长超过 {MAX_FILE_DURATION} 秒")
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except Exception as soundfile_err:
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logging.exception(f"使用 soundfile 后端加载音频文件出错: {transcription_file}")
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return jsonify({'error': f'使用两个后端加载音频文件都出错: {str(soundfile_err)}'}), 400
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finally:
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torchaudio.set_audio_backend("default") # 恢复默认音频后端
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# 转录音频文件
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segments, _ = wmodel.transcribe(
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transcription_file,
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beam_size=beamsize,
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}), 200
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except Exception as e:
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logging.exception("转录过程中发生异常")
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return jsonify({'error': str(e)}), 500
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finally:
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cleanup_temp_files(temp_file_path, temp_audio_path)
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active_requests -= 1
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request_semaphore.release()
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print(f"处理时间:{time.time() - start_time:.2f}s (活动请求:{active_requests})")
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if __name__ == "__main__":
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# 创建临时文件夹(如果不存在)
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if not os.path.exists(TEMPORARY_FOLDER):
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os.makedirs(TEMPORARY_FOLDER)
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logging.info(f"创建临时文件夹: {TEMPORARY_FOLDER}")
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app.run(host="0.0.0.0", port=7860, threaded=True)
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