306 lines
9.2 KiB
Python
306 lines
9.2 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Vosk 语音识别服务
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轻量级、易安装、支持离线
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"""
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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import os
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import json
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import wave
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from difflib import SequenceMatcher
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app = Flask(__name__)
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CORS(app)
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# 全局变量
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vosk_model = None
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model_loaded = False
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def init_vosk_model():
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"""初始化Vosk模型"""
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global vosk_model, model_loaded
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try:
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from vosk import Model, KaldiRecognizer
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# 将KaldiRecognizer设为全局变量以便后续使用
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globals()['KaldiRecognizer'] = KaldiRecognizer
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model_path = "./vosk-model-small-cn-0.22"
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if not os.path.exists(model_path):
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print(f"[错误] 模型不存在: {model_path}")
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print("请下载模型：https://alphacephei.com/vosk/models/vosk-model-small-cn-0.22.zip")
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return False
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print(f"正在加载模型: {model_path}")
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vosk_model = Model(model_path)
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model_loaded = True
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print("✓ 模型加载成功！")
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return True
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except Exception as e:
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print(f"✗ 模型加载失败: {str(e)}")
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model_loaded = False
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return False
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def convert_audio_to_wav(input_path, output_path):
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"""转换音频为WAV格式（使用pydub）"""
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try:
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from pydub import AudioSegment
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print(f"[转换] 使用pydub转换音频...")
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# 加载音频（自动检测格式）
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audio = AudioSegment.from_file(input_path)
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# 转换为单声道、16kHz、16位WAV
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audio = audio.set_channels(1) # 单声道
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audio = audio.set_frame_rate(16000) # 16kHz采样率
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audio = audio.set_sample_width(2) # 16位
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# 导出为WAV
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audio.export(output_path, format='wav')
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print(f"[转换] pydub转换成功")
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return True, None
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except Exception as e:
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error_msg = str(e).lower()
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# 检查是否是ffmpeg未安装的错误
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if 'ffmpeg' in error_msg or 'ffprobe' in error_msg or 'filenotfounderror' in error_msg:
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return False, (
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"需要安装 ffmpeg 才能转换音频格式。\n"
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"请下载 ffmpeg: https://www.gyan.dev/ffmpeg/builds/\n"
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"或运行: pip install ffmpeg-python\n"
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"错误详情: " + str(e)
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)
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else:
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return False, f"音频转换失败: {str(e)}"
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def recognize_audio(audio_path):
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"""识别音频文件"""
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converted_path = None
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try:
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# 先尝试直接打开WAV文件
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print(f"[识别] 尝试打开音频文件: {audio_path}")
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try:
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wf = wave.open(audio_path, "rb")
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is_valid_wav = True
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print(f"[识别] 文件是有效的WAV格式")
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except Exception as e:
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is_valid_wav = False
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print(f"[识别] 不是有效的WAV格式: {str(e)}")
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# 如果不是有效的WAV，尝试转换
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if not is_valid_wav:
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print(f"[识别] 检测到非WAV格式，开始转换...")
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converted_path = audio_path + '.converted.wav'
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success, error = convert_audio_to_wav(audio_path, converted_path)
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if not success:
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print(f"[识别] 转换失败: {error}")
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return None, f"音频格式转换失败: {error}"
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print(f"[识别] 转换成功: {converted_path}")
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# 使用转换后的文件
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audio_path = converted_path
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wf = wave.open(audio_path, "rb")
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print(f"[识别] 转换后的文件已打开")
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# 检查音频参数
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if wf.getnchannels() != 1:
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wf.close()
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return None, "音频必须是单声道"
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if wf.getsampwidth() != 2:
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wf.close()
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return None, "音频必须是16位"
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if wf.getframerate() not in [8000, 16000, 32000, 48000]:
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wf.close()
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return None, f"不支持的采样率: {wf.getframerate()}"
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# 创建识别器
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rec = KaldiRecognizer(vosk_model, wf.getframerate())
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rec.SetWords(True)
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result_text = ""
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# 读取并识别
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while True:
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data = wf.readframes(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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result = json.loads(rec.Result())
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text = result.get('text', '')
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if text:
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result_text += text + " "
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# 获取最终结果
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final_result = json.loads(rec.FinalResult())
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final_text = final_result.get('text', '')
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if final_text:
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result_text += final_text
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wf.close()
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# 清理转换后的临时文件
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if converted_path and os.path.exists(converted_path):
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try:
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os.remove(converted_path)
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print(f"[识别] 已清理转换文件: {converted_path}")
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except Exception as e:
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print(f"[识别] 清理转换文件失败: {e}")
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result_text = result_text.strip()
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return result_text, None
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except Exception as e:
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# 发生错误时也清理转换文件
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if converted_path and os.path.exists(converted_path):
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try:
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os.remove(converted_path)
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except:
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pass
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return None, str(e)
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def calculate_similarity(text1, text2):
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"""计算文本相似度（0-100分）"""
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if not text1 or not text2:
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return 0
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# 去除空格
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text1 = text1.replace(" ", "")
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text2 = text2.replace(" ", "")
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if not text1 or not text2:
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return 0
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# 计算相似度
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similarity = SequenceMatcher(None, text1, text2).ratio()
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return round(similarity * 100, 2)
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@app.route('/api/speech/recognize', methods=['POST'])
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def recognize():
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"""语音识别接口"""
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try:
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# 检查模型是否加载
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if not model_loaded:
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return jsonify({
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'code': 500,
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'msg': '模型未加载，请检查服务器日志'
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}), 500
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# 检查文件
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if 'audio' not in request.files:
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return jsonify({
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'code': 400,
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'msg': '未上传音频文件'
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}), 400
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audio_file = request.files['audio']
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reference_text = request.form.get('referenceText', '')
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# 保存临时文件
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temp_dir = './temp_audio'
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if not os.path.exists(temp_dir):
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os.makedirs(temp_dir)
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import time
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timestamp = str(int(time.time() * 1000))
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temp_path = os.path.join(temp_dir, f'audio_{timestamp}.wav')
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audio_file.save(temp_path)
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print(f"收到音频: {temp_path}")
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print(f"参考文本: {reference_text}")
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# 识别音频
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recognized_text, error = recognize_audio(temp_path)
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# 删除临时文件
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try:
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os.remove(temp_path)
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except:
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pass
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if error:
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return jsonify({
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'code': 500,
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'msg': f'识别失败: {error}'
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}), 500
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if not recognized_text:
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return jsonify({
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'code': 500,
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'msg': '未识别到有效语音'
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}), 500
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# 计算评分
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score = calculate_similarity(recognized_text, reference_text)
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pronunciation_score = max(0, score - 5)
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fluency_score = max(0, score - 3)
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print(f"识别结果: {recognized_text}")
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print(f"相似度: {score}分")
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return jsonify({
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'code': 200,
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'msg': '成功',
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'data': {
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'recognizedText': recognized_text,
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'score': score,
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'pronunciationScore': pronunciation_score,
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'fluencyScore': fluency_score,
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'status': 'completed'
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}
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})
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except Exception as e:
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print(f"处理错误: {str(e)}")
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return jsonify({
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'code': 500,
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'msg': f'处理失败: {str(e)}'
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}), 500
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@app.route('/api/speech/health', methods=['GET'])
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def health():
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"""健康检查"""
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return jsonify({
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'code': 200,
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'msg': '服务正常',
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'data': {
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'model_loaded': model_loaded,
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'engine': 'vosk'
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}
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})
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if __name__ == '__main__':
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print("=" * 50)
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print("Vosk 语音识别服务")
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print("=" * 50)
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print("")
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# 初始化模型
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if init_vosk_model():
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print("")
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print("=" * 50)
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print("服务启动成功！")
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print("访问地址: http://localhost:5000")
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print("健康检查: http://localhost:5000/api/speech/health")
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print("=" * 50)
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print("")
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# 启动服务
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app.run(host='0.0.0.0', port=5000, debug=False)
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else:
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print("")
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print("=" * 50)
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print("服务启动失败！请检查模型文件")
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print("=" * 50)
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input("按回车键退出...")
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