mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-26 00:53:54 +08:00
115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
import os
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import json
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import time
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from typing import Optional, Tuple, List
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from .base import ASRProviderBase
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from config.logger import setup_logging
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from core.providers.asr.dto.dto import InterfaceType
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import vosk
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TAG = __name__
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logger = setup_logging()
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class ASRProvider(ASRProviderBase):
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def __init__(self, config: dict, delete_audio_file: bool = True):
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super().__init__()
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self.interface_type = InterfaceType.LOCAL
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self.model_path = config.get("model_path")
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self.output_dir = config.get("output_dir", "tmp/")
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self.delete_audio_file = delete_audio_file
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# 初始化VOSK模型
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self.model = None
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self.recognizer = None
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self._load_model()
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# 确保输出目录存在
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os.makedirs(self.output_dir, exist_ok=True)
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def _load_model(self):
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"""加载VOSK模型"""
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try:
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if not os.path.exists(self.model_path):
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raise FileNotFoundError(f"VOSK模型路径不存在: {self.model_path}")
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logger.bind(tag=TAG).info(f"正在加载VOSK模型: {self.model_path}")
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self.model = vosk.Model(self.model_path)
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# 初始化VOSK识别器（采样率必须为16kHz）
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self.recognizer = vosk.KaldiRecognizer(self.model, 16000)
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logger.bind(tag=TAG).info("VOSK模型加载成功")
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except Exception as e:
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logger.bind(tag=TAG).error(f"加载VOSK模型失败: {e}")
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raise
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async def speech_to_text(
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self, audio_data: List[bytes], session_id: str, audio_format: str = "opus"
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) -> Tuple[Optional[str], Optional[str]]:
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"""将语音数据转换为文本"""
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file_path = None
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try:
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# 检查模型是否加载成功
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if not self.model:
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logger.bind(tag=TAG).error("VOSK模型未加载，无法进行识别")
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return "", None
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# 解码音频（如果原始格式是Opus）
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if audio_format == "pcm":
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pcm_data = audio_data
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else:
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pcm_data = self.decode_opus(audio_data)
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if not pcm_data:
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logger.bind(tag=TAG).warning("解码后的PCM数据为空，无法进行识别")
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return "", None
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# 合并PCM数据
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combined_pcm_data = b"".join(pcm_data)
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if len(combined_pcm_data) == 0:
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logger.bind(tag=TAG).warning("合并后的PCM数据为空")
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return "", None
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# 判断是否保存为WAV文件
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if not self.delete_audio_file:
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file_path = self.save_audio_to_file(pcm_data, session_id)
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start_time = time.time()
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# 进行识别（VOSK推荐每次送入2000字节的数据）
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chunk_size = 2000
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text_result = ""
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for i in range(0, len(combined_pcm_data), chunk_size):
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chunk = combined_pcm_data[i:i+chunk_size]
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if self.recognizer.AcceptWaveform(chunk):
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result = json.loads(self.recognizer.Result())
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text = result.get('text', '')
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if text:
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text_result += text + " "
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# 获取最终结果
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final_result = json.loads(self.recognizer.FinalResult())
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final_text = final_result.get('text', '')
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if final_text:
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text_result += final_text
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logger.bind(tag=TAG).debug(
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f"VOSK语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text_result.strip()}"
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)
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return text_result.strip(), file_path
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except Exception as e:
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logger.bind(tag=TAG).error(f"VOSK语音识别失败: {e}")
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return "", None
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finally:
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# 文件清理逻辑
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if self.delete_audio_file and file_path and os.path.exists(file_path):
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try:
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os.remove(file_path)
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logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
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except Exception as e:
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logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")
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