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95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
import whisper
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import time
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import datetime
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from flask import Flask, request, jsonify
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import threading
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app = Flask(__name__)
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app.config['JSON_AS_ASCII'] = False # 确保中文正常显示
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# 全局变量存储模型
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model = None
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def load_model():
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global model
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print("开始加载 Whisper 模型...")
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start_time = time.time()
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# 手动指定模型存储路径
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model_path = "./models" # 您可以修改为任意路径
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# 根据实际情况，选择使用CPU还是GPU
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model = whisper.load_model("medium", download_root=model_path, device="cpu")
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load_time = time.time() - start_time
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print(f"模型加载完成，耗时: {str(datetime.timedelta(seconds=load_time))}")
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print(f"模型存储路径: {model_path}")
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# 在应用启动时加载模型
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@app.before_request
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def before_first_request():
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global model
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if model is None:
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print("首次请求，加载模型中...")
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load_model()
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@app.route('/transcribe', methods=['POST'])
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def transcribe():
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if model is None:
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return jsonify({"error": "模型尚未加载完成"}), 503
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if 'audio' not in request.files:
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return jsonify({"error": "未提供音频文件"}), 400
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audio_file = request.files['audio']
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audio_path = f"/{audio_file.filename}"
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audio_file.save(audio_path)
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# 开始转录 - 使用 FP32 避免 NaN
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start_time = time.time()
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result = model.transcribe(audio_path, language="zh", fp16=False) # 主动降低精度，使用 FP32 避免 NaN
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transcription_time = time.time() - start_time
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return jsonify({
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"text": result["text"],
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"processing_time": transcription_time
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})
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@app.route('/transcribe_text', methods=['POST'])
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def transcribe_text():
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"""返回纯文本格式的转录结果，方便命令行查看"""
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if model is None:
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return "模型尚未加载完成", 503
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if 'audio' not in request.files:
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return "未提供音频文件", 400
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audio_file = request.files['audio']
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audio_path = f"/{audio_file.filename}"
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audio_file.save(audio_path)
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# 开始转录 - 使用 FP32 避免 NaN
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start_time = time.time()
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result = model.transcribe(audio_path, language="zh", fp16=False)
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transcription_time = time.time() - start_time
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# 返回纯文本格式
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return f"{result['text']}\r\n处理时间: {transcription_time:.2f}秒"
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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": "ok",
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"model_loaded": model is not None
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})
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if __name__ == '__main__':
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# 在启动应用前预先加载模型
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print("启动服务前预先加载模型...")
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load_model()
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app.run(host='0.0.0.0', port=5000, threaded=True) |