import os
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import io
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import sys
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import time
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import shutil
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import psutil
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import asyncio
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from funasr import AutoModel
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from config.logger import setup_logging
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from typing import Optional, Tuple, List
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from core.providers.asr.utils import lang_tag_filter
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from core.providers.asr.base import ASRProviderBase
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from core.providers.asr.dto.dto import InterfaceType
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TAG = __name__
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logger = setup_logging()
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MAX_RETRIES = 2
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RETRY_DELAY = 1 # 重试延迟（秒）
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# 捕获标准输出
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class CaptureOutput:
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def __enter__(self):
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self._output = io.StringIO()
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self._original_stdout = sys.stdout
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sys.stdout = self._output
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def __exit__(self, exc_type, exc_value, traceback):
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sys.stdout = self._original_stdout
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self.output = self._output.getvalue()
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self._output.close()
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# 将捕获到的内容通过 logger 输出
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if self.output:
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logger.bind(tag=TAG).info(self.output.strip())
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class ASRProvider(ASRProviderBase):
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def __init__(self, config: dict, delete_audio_file: bool):
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super().__init__()
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# 内存检测，要求大于2G
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min_mem_bytes = 2 * 1024 * 1024 * 1024
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try:
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total_mem = psutil.virtual_memory().total
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except RuntimeError as e:
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logger.bind(tag=TAG).warning(f"获取系统内存信息失败，跳过FunASR内存检测: {e}")
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else:
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if total_mem < min_mem_bytes:
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logger.bind(tag=TAG).error(f"可用内存不足2G，当前仅有 {total_mem / (1024*1024):.2f} MB，可能无法启动FunASR")
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self.interface_type = InterfaceType.LOCAL
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self.model_dir = config.get("model_dir")
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self.output_dir = config.get("output_dir") # 修正配置键名
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self.language = config.get("language", "auto")
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self.delete_audio_file = delete_audio_file
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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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with CaptureOutput():
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self.model = AutoModel(
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model=self.model_dir,
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vad_kwargs={"max_single_segment_time": 30000},
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disable_update=True,
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hub="hf",
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# device="cuda:0", # 启用GPU加速
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)
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async def speech_to_text(
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self, opus_data: List[bytes], session_id: str, artifacts=None
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) -> Tuple[Optional[str], Optional[str]]:
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"""语音转文本主处理逻辑"""
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retry_count = 0
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while retry_count < MAX_RETRIES:
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try:
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if artifacts is None:
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return "", None
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# 语音识别 - 使用线程池避免阻塞事件循环
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start_time = time.time()
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result = await asyncio.to_thread(
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self.model.generate,
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input=artifacts.pcm_bytes,
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cache={},
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language=self.language,
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use_itn=True,
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batch_size_s=60,
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)
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text = lang_tag_filter(result[0]["text"])
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logger.bind(tag=TAG).debug(
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f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text['content']}"
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)
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return text, artifacts.file_path
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except OSError as e:
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retry_count += 1
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if retry_count >= MAX_RETRIES:
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logger.bind(tag=TAG).error(
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f"语音识别失败（已重试{retry_count}次）: {e}", exc_info=True
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)
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return "", None
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logger.bind(tag=TAG).warning(
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f"语音识别失败，正在重试（{retry_count}/{MAX_RETRIES}）: {e}"
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)
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time.sleep(RETRY_DELAY)
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except Exception as e:
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logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
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return "", None
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