refactor(asr): 统一音频预处理逻辑并引入AudioArtifacts

重构所有ASR提供商的speech_to_text方法，将重复的音频解码、合并和文件保存逻辑提取到基类的speech_to_text_wrapper中。引入AudioArtifacts数据类封装PCM帧、字节数据、文件路径和临时路径，简化各提供商实现。移除各提供商中的冗余文件清理代码，由基类统一处理。

新增requires_file()和prefers_temp_file()方法允许提供商声明文件需求，优化内存和磁盘使用。保持接口兼容性的同时提高代码复用性和可维护性。

main/xiaozhi-server/core/providers/asr/aliyun.py

@@ -220,29 +220,18 @@ class ASRProvider(ASRProviderBase):
             logger.warning("Token已过期，正在自动刷新...")
             self._refresh_token()
 
-        file_path = None
         try:
-            # 解码Opus为PCM
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            combined_pcm_data = b"".join(pcm_data)
-
-            # 判断是否保存为WAV文件
-            if self.delete_audio_file:
-                pass
-            else:
-                file_path = self.save_audio_to_file(pcm_data, session_id)
-
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
             # 发送请求并获取文本
-            text = await self._send_request(combined_pcm_data)
+            text = await self._send_request(artifacts.pcm_bytes)
 
             if text:
-                return text, file_path
+                return text, artifacts.file_path
 
-            return "", file_path
+            return "", artifacts.file_path
 
         except Exception as e:
             logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
-            return "", file_path
+            return "", None

main/xiaozhi-server/core/providers/asr/baidu.py

@@ -37,30 +37,20 @@ class ASRProvider(ASRProviderBase):
             logger.bind(tag=TAG).warning("音频数据为空！")
             return None, None
 
-        file_path = None
         try:
             # 检查配置是否已设置
             if not self.app_id or not self.api_key or not self.secret_key:
                 logger.bind(tag=TAG).error("百度语音识别配置未设置，无法进行识别")
-                return None, file_path
+                return None, None
 
-            # 将Opus音频数据解码为PCM
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            combined_pcm_data = b"".join(pcm_data)
-
-            # 判断是否保存为WAV文件
-            if self.delete_audio_file:
-                pass
-            else:
-                self.save_audio_to_file(pcm_data, session_id)
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
 
             start_time = time.time()
             # 识别本地文件
             result = self.client.asr(
-                combined_pcm_data,
+                artifacts.pcm_bytes,
                 "pcm",
                 16000,
                 {
@@ -73,13 +63,13 @@ class ASRProvider(ASRProviderBase):
                     f"百度语音识别耗时: {time.time() - start_time:.3f}s | 结果: {result}"
                 )
                 result = result["result"][0]
-                return result, file_path
+                return result, artifacts.file_path
             else:
                 raise Exception(
                     f"百度语音识别失败，错误码: {result['err_no']}，错误信息: {result['err_msg']}"
                 )
-                return None, file_path
+                return None, artifacts.file_path
 
         except Exception as e:
             logger.bind(tag=TAG).error(f"处理音频时发生错误！{e}", exc_info=True)
-            return None, file_path
+            return None, None

main/xiaozhi-server/core/providers/asr/base.py

@@ -8,14 +8,16 @@ import queue
 import asyncio
 import traceback
 import threading
+import shutil
 import opuslib_next
 from abc import ABC, abstractmethod
 from config.logger import setup_logging
-from typing import Optional, Tuple, List
+from typing import Optional, Tuple, List, NamedTuple
 from core.handle.receiveAudioHandle import startToChat
 from core.handle.reportHandle import enqueue_asr_report
 from core.utils.util import remove_punctuation_and_length
 from core.handle.receiveAudioHandle import handleAudioMessage
+import tempfile
 
 TAG = __name__
 logger = setup_logging()
@@ -23,7 +25,8 @@ logger = setup_logging()
 
 class ASRProviderBase(ABC):
     def __init__(self):
-        pass
+        self._current_artifacts: Optional[ASRProviderBase.AudioArtifacts] = None
+        """当前正在处理的音频 artifact"""
 
     # 打开音频通道
     async def open_audio_channels(self, conn):
@@ -93,10 +96,14 @@ class ASRProviderBase(ABC):
                 wav_data = self._pcm_to_wav(combined_pcm_data)
 
             # 定义ASR任务
-            asr_task = self.speech_to_text(asr_audio_task, conn.session_id, conn.audio_format)
+            asr_task = self.speech_to_text_wrapper(
+                asr_audio_task, conn.session_id, conn.audio_format
+            )
 
             if conn.voiceprint_provider and wav_data:
-                voiceprint_task = conn.voiceprint_provider.identify_speaker(wav_data, conn.session_id)
+                voiceprint_task = conn.voiceprint_provider.identify_speaker(
+                    wav_data, conn.session_id
+                )
                 # 并发等待两个结果
                 asr_result, voiceprint_result = await asyncio.gather(
                     asr_task, voiceprint_task, return_exceptions=True
@@ -160,19 +167,19 @@ class ASRProviderBase(ABC):
                 # 使用自定义模块进行上报
                 await startToChat(conn, enhanced_text)
                 enqueue_asr_report(conn, enhanced_text, asr_audio_task)
-                
+
         except Exception as e:
             logger.bind(tag=TAG).error(f"处理语音停止失败: {e}")
             import traceback
+
             logger.bind(tag=TAG).debug(f"异常详情: {traceback.format_exc()}")
 
     def _build_enhanced_text(self, text: str, speaker_name: Optional[str]) -> str:
         """构建包含说话人信息的文本（仅用于纯文本ASR）"""
         if speaker_name and speaker_name.strip():
-            return json.dumps({
-                "speaker": speaker_name,
-                "content": text
-            }, ensure_ascii=False)
+            return json.dumps(
+                {"speaker": speaker_name, "content": text}, ensure_ascii=False
+            )
         else:
             return text
 
@@ -181,23 +188,23 @@ class ASRProviderBase(ABC):
         if len(pcm_data) == 0:
             logger.bind(tag=TAG).warning("PCM数据为空，无法转换WAV")
             return b""
-        
+
         # 确保数据长度是偶数（16位音频）
         if len(pcm_data) % 2 != 0:
             pcm_data = pcm_data[:-1]
-        
+
         # 创建WAV文件头
         wav_buffer = io.BytesIO()
         try:
-            with wave.open(wav_buffer, 'wb') as wav_file:
-                wav_file.setnchannels(1)      # 单声道
-                wav_file.setsampwidth(2)      # 16位
+            with wave.open(wav_buffer, "wb") as wav_file:
+                wav_file.setnchannels(1)  # 单声道
+                wav_file.setsampwidth(2)  # 16位
                 wav_file.setframerate(16000)  # 16kHz采样率
                 wav_file.writeframes(pcm_data)
-            
+
             wav_buffer.seek(0)
             wav_data = wav_buffer.read()
-            
+
             return wav_data
         except Exception as e:
             logger.bind(tag=TAG).error(f"WAV转换失败: {e}")
@@ -206,6 +213,37 @@ class ASRProviderBase(ABC):
     def stop_ws_connection(self):
         pass
 
+    class AudioArtifacts(NamedTuple):
+        pcm_frames: List[bytes]
+        pcm_bytes: bytes
+        file_path: Optional[str]
+        temp_path: Optional[str]
+
+    def get_current_artifacts(self) -> Optional["ASRProviderBase.AudioArtifacts"]:
+        return self._current_artifacts
+
+    def requires_file(self) -> bool:
+        """是否需要文件输入"""
+        return False
+
+    def prefers_temp_file(self) -> bool:
+        """是否优先使用临时文件"""
+        return False
+
+    def build_temp_file(self, pcm_bytes: bytes) -> Optional[str]:
+        try:
+            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+                temp_path = temp_file.name
+            with wave.open(temp_path, "wb") as wav_file:
+                wav_file.setnchannels(1)
+                wav_file.setsampwidth(2)
+                wav_file.setframerate(16000)
+                wav_file.writeframes(pcm_bytes)
+            return temp_path
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"临时音频文件生成失败: {e}")
+            return None
+
     def save_audio_to_file(self, pcm_data: List[bytes], session_id: str) -> str:
         """PCM数据保存为WAV文件"""
         module_name = __name__.split(".")[-1]
@@ -220,6 +258,59 @@ class ASRProviderBase(ABC):
 
         return file_path
 
+    async def speech_to_text_wrapper(
+        self, opus_data: List[bytes], session_id: str, audio_format="opus"
+    ) -> Tuple[Optional[str], Optional[str]]:
+        file_path = None
+        temp_path = None
+        try:
+            if audio_format == "pcm":
+                pcm_data = opus_data
+            else:
+                pcm_data = self.decode_opus(opus_data)
+            combined_pcm_data = b"".join(pcm_data)
+
+            free_space = shutil.disk_usage(self.output_dir).free
+            if free_space < len(combined_pcm_data) * 2:
+                raise OSError("磁盘空间不足")
+
+            if self.requires_file() and self.prefers_temp_file():
+                temp_path = self.build_temp_file(combined_pcm_data)
+
+            if (hasattr(self, "delete_audio_file") and not self.delete_audio_file) or (
+                self.requires_file() and not self.prefers_temp_file()
+            ):
+                file_path = self.save_audio_to_file(pcm_data, session_id)
+
+            self._current_artifacts = ASRProviderBase.AudioArtifacts(
+                pcm_frames=pcm_data,
+                pcm_bytes=combined_pcm_data,
+                file_path=file_path,
+                temp_path=temp_path,
+            )
+
+            text, _ = await self.speech_to_text(opus_data, session_id, audio_format)
+            return text, file_path
+        except OSError as e:
+            logger.bind(tag=TAG).error(f"文件操作错误: {e}")
+            return None, None
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"语音识别失败: {e}")
+            return None, None
+        finally:
+            try:
+                if temp_path and os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                if (
+                    hasattr(self, "delete_audio_file")
+                    and self.delete_audio_file
+                    and file_path
+                    and os.path.exists(file_path)
+                ):
+                    os.remove(file_path)
+            except Exception as e:
+                logger.bind(tag=TAG).error(f"文件清理失败: {e}")
+
     @abstractmethod
     async def speech_to_text(
         self, opus_data: List[bytes], session_id: str, audio_format="opus"
@@ -235,23 +326,23 @@ class ASRProviderBase(ABC):
             decoder = opuslib_next.Decoder(16000, 1)
             pcm_data = []
             buffer_size = 960  # 每次处理960个采样点 (60ms at 16kHz)
-            
+
             for i, opus_packet in enumerate(opus_data):
                 try:
                     if not opus_packet or len(opus_packet) == 0:
                         continue
-                    
+
                     pcm_frame = decoder.decode(opus_packet, buffer_size)
                     if pcm_frame and len(pcm_frame) > 0:
                         pcm_data.append(pcm_frame)
-                        
+
                 except opuslib_next.OpusError as e:
                     logger.bind(tag=TAG).warning(f"Opus解码错误，跳过数据包 {i}: {e}")
                 except Exception as e:
                     logger.bind(tag=TAG).error(f"音频处理错误，数据包 {i}: {e}")
-            
+
             return pcm_data
-            
+
         except Exception as e:
             logger.bind(tag=TAG).error(f"音频解码过程发生错误: {e}")
             return []

main/xiaozhi-server/core/providers/asr/doubao.py

@@ -236,20 +236,10 @@ class ASRProvider(ASRProviderBase):
     ) -> Tuple[Optional[str], Optional[str]]:
         """将语音数据转换为文本"""
 
-        file_path = None
         try:
-            # 合并所有opus数据包
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            combined_pcm_data = b"".join(pcm_data)
-
-            # 判断是否保存为WAV文件
-            if self.delete_audio_file:
-                pass
-            else:
-                file_path = self.save_audio_to_file(pcm_data, session_id)
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
 
             # 直接使用PCM数据
             # 计算分段大小 (单声道, 16bit, 16kHz采样率)
@@ -258,14 +248,14 @@ class ASRProvider(ASRProviderBase):
 
             # 语音识别
             start_time = time.time()
-            text = await self._send_request(combined_pcm_data, segment_size)
+            text = await self._send_request(artifacts.pcm_bytes, segment_size)
             if text:
                 logger.bind(tag=TAG).debug(
                     f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}"
                 )
-                return text, file_path
-            return "", file_path
+                return text, artifacts.file_path
+            return "", artifacts.file_path
 
         except Exception as e:
             logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
-            return "", file_path
+            return "", None

main/xiaozhi-server/core/providers/asr/fun_local.py

@@ -67,36 +67,19 @@ class ASRProvider(ASRProviderBase):
         self, opus_data: List[bytes], session_id: str, audio_format="opus"
     ) -> Tuple[Optional[str], Optional[str]]:
         """语音转文本主处理逻辑"""
-        file_path = None
         retry_count = 0
-
+        
         while retry_count < MAX_RETRIES:
             try:
-                # 合并所有opus数据包
-                if audio_format == "pcm":
-                    pcm_data = opus_data
-                else:
-                    pcm_data = self.decode_opus(opus_data)
-
-                combined_pcm_data = b"".join(pcm_data)
-
-                # 检查磁盘空间
-                if not self.delete_audio_file:
-                    free_space = shutil.disk_usage(self.output_dir).free
-                    if free_space < len(combined_pcm_data) * 2:  # 预留2倍空间
-                        raise OSError("磁盘空间不足")
-
-                # 判断是否保存为WAV文件
-                if self.delete_audio_file:
-                    pass
-                else:
-                    file_path = self.save_audio_to_file(pcm_data, session_id)
+                artifacts = self.get_current_artifacts()
+                if artifacts is None:
+                    return "", None
 
                 # 语音识别 - 使用线程池避免阻塞事件循环
                 start_time = time.time()
                 result = await asyncio.to_thread(
                     self.model.generate,
-                    input=combined_pcm_data,
+                    input=artifacts.pcm_bytes,
                     cache={},
                     language="auto",
                     use_itn=True,
@@ -107,7 +90,7 @@ class ASRProvider(ASRProviderBase):
                     f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text['content']}"
                 )
 
-                return text, file_path
+                return text, artifacts.file_path
 
             except OSError as e:
                 retry_count += 1
@@ -115,7 +98,7 @@ class ASRProvider(ASRProviderBase):
                     logger.bind(tag=TAG).error(
                         f"语音识别失败（已重试{retry_count}次）: {e}", exc_info=True
                     )
-                    return "", file_path
+                    return "", None
                 logger.bind(tag=TAG).warning(
                     f"语音识别失败，正在重试（{retry_count}/{MAX_RETRIES}）: {e}"
                 )
@@ -123,15 +106,4 @@ class ASRProvider(ASRProviderBase):
 
             except Exception as e:
                 logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
-                return "", file_path
-
-            finally:
-                # 文件清理逻辑
-                if self.delete_audio_file and file_path and os.path.exists(file_path):
-                    try:
-                        os.remove(file_path)
-                        logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
-                    except Exception as e:
-                        logger.bind(tag=TAG).error(
-                            f"文件删除失败: {file_path} | 错误: {e}"
-                        )
+                return "", None

main/xiaozhi-server/core/providers/asr/fun_server.py

@@ -109,18 +109,10 @@ class ASRProvider(ASRProviderBase):
         :param session_id: Unique session identifier.
         :return: Tuple containing recognized text and optional timestamp.
         """
-        file_path = None
-        if audio_format == "pcm":
-            pcm_data = opus_data
-        else:
-            pcm_data = self.decode_opus(opus_data)
-        combined_pcm_data = b"".join(pcm_data)
-
-        # 判断是否保存为WAV文件
-        if self.delete_audio_file:
-            pass
-        else:
-            file_path = self.save_audio_to_file(pcm_data, session_id)
+        artifacts = self.get_current_artifacts()
+        
+        if artifacts is None:
+            return "", None
         auth_header = {"Authorization": "Bearer; {}".format(self.api_key)}
         async with websockets.connect(
             self.uri,
@@ -132,7 +124,7 @@ class ASRProvider(ASRProviderBase):
             try:
                 # Use asyncio to handle WebSocket communication
                 send_task = asyncio.create_task(
-                    self._send_data(ws, combined_pcm_data, session_id)
+                    self._send_data(ws, artifacts.pcm_bytes, session_id)
                 )
                 receive_task = asyncio.create_task(self._receive_responses(ws))
 
@@ -161,14 +153,14 @@ class ASRProvider(ASRProviderBase):
                 result = lang_tag_filter(result)
                 return (
                     result,
-                    file_path,
+                    artifacts.file_path,
                 )  # Return the recognized text and timestamp (if any)
 
             except websockets.exceptions.ConnectionClosed as e:
                 logger.bind(tag=TAG).error(f"WebSocket connection closed: {e}")
-                return "", file_path
+                return "", artifacts.file_path
             except Exception as e:
                 logger.bind(tag=TAG).error(
                     f"Error during speech-to-text conversion: {e}", exc_info=True
                 )
-                return "", file_path
+                return "", artifacts.file_path

main/xiaozhi-server/core/providers/asr/openai.py

@@ -21,20 +21,17 @@ class ASRProvider(ASRProviderBase):
 
         os.makedirs(self.output_dir, exist_ok=True)
 
+    def requires_file(self) -> bool:
+        return True
+
     async def speech_to_text(self, opus_data: List[bytes], session_id: str, audio_format="opus") -> Tuple[Optional[str], Optional[str]]:
         file_path = None
         try:
-            start_time = time.time()
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            file_path = self.save_audio_to_file(pcm_data, session_id)
-
-            logger.bind(tag=TAG).debug(
-                f"音频文件保存耗时: {time.time() - start_time:.3f}s | 路径: {file_path}"
-            )
-
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
+            file_path = artifacts.file_path
+                
             logger.bind(tag=TAG).info(f"file path: {file_path}")
             headers = {
                 "Authorization": f"Bearer {self.api_key}",
@@ -71,12 +68,4 @@ class ASRProvider(ASRProviderBase):
         except Exception as e:
             logger.bind(tag=TAG).error(f"语音识别失败: {e}")
             return "", None
-        finally:
-            # 文件清理逻辑
-            if self.delete_audio_file and file_path and os.path.exists(file_path):
-                try:
-                    os.remove(file_path)
-                    logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
-                except Exception as e:
-                    logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")
         

main/xiaozhi-server/core/providers/asr/qwen3_asr_flash.py

@@ -1,5 +1,4 @@
 import os
-import tempfile
 from typing import Optional, Tuple, List
 import dashscope
 from config.logger import setup_logging
@@ -35,27 +34,11 @@ class ASRProvider(ASRProviderBase):
         # 确保输出目录存在
         os.makedirs(self.output_dir, exist_ok=True)
 
-    def _prepare_audio_file(self, pcm_data: bytes) -> str:
-        """将PCM数据转换为WAV文件并返回文件路径"""
-        try:
-            import wave
-            
-            # 创建临时WAV文件
-            with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file:
-                temp_path = temp_file.name
-                
-            # 写入WAV格式
-            with wave.open(temp_path, 'wb') as wav_file:
-                wav_file.setnchannels(1)      # 单声道
-                wav_file.setsampwidth(2)      # 16位
-                wav_file.setframerate(16000)  # 16kHz采样率
-                wav_file.writeframes(pcm_data)
-                
-            return temp_path
-            
-        except Exception as e:
-            logger.bind(tag=tag).error(f"音频文件准备失败: {e}")
-            return None
+    def prefers_temp_file(self) -> bool:
+        return True
+
+    def requires_file(self) -> bool:
+        return True
 
     async def speech_to_text(
         self, opus_data: List[bytes], session_id: str, audio_format="opus"
@@ -63,28 +46,14 @@ class ASRProvider(ASRProviderBase):
         """将语音数据转换为文本"""
         temp_file_path = None
         file_path = None
-        
         try:
-            # 解码音频数据
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            
-            combined_pcm_data = b"".join(pcm_data)
-            if len(combined_pcm_data) == 0:
-                logger.bind(tag=tag).warning("音频数据为空")
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
                 return "", None
-            
-            # 准备音频文件
-            temp_file_path = self._prepare_audio_file(combined_pcm_data)
+            temp_file_path = artifacts.temp_path
+            file_path = artifacts.file_path
             if not temp_file_path:
-                return "", None
-            
-            # 保存音频文件（如果需要）
-            if not self.delete_audio_file:
-                file_path = self.save_audio_to_file(pcm_data, session_id)
-            
+                return "", file_path
             # 构造请求消息
             messages = [
                 {
@@ -141,11 +110,3 @@ class ASRProvider(ASRProviderBase):
         except Exception as e:
             logger.bind(tag=tag).error(f"语音识别失败: {e}")
             return "", file_path
-            
-        finally:
-            # 清理临时文件
-            if temp_file_path and os.path.exists(temp_file_path):
-                try:
-                    os.unlink(temp_file_path)
-                except Exception as e:
-                    logger.bind(tag=tag).warning(f"清理临时文件失败: {e}")

main/xiaozhi-server/core/providers/asr/sherpa_onnx_local.py

@@ -120,24 +120,20 @@ class ASRProvider(ASRProviderBase):
             samples_float32 = samples_float32 / 32768
             return samples_float32, f.getframerate()
 
+    def requires_file(self) -> bool:
+        return True
+
     async def speech_to_text(
         self, opus_data: List[bytes], session_id: str, audio_format="opus"
     ) -> Tuple[Optional[str], Optional[str]]:
         """语音转文本主处理逻辑"""
         file_path = None
         try:
-            # 保存音频文件
-            start_time = time.time()
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            file_path = self.save_audio_to_file(pcm_data, session_id)
-            logger.bind(tag=TAG).debug(
-                f"音频文件保存耗时: {time.time() - start_time:.3f}s | 路径: {file_path}"
-            )
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
+            file_path = artifacts.file_path
 
-            # 语音识别
             start_time = time.time()
             s = self.model.create_stream()
             samples, sample_rate = self.read_wave(file_path)
@@ -153,11 +149,3 @@ class ASRProvider(ASRProviderBase):
         except Exception as e:
             logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
             return "", file_path
-        finally:
-            # 文件清理逻辑
-            if self.delete_audio_file and file_path and os.path.exists(file_path):
-                try:
-                    os.remove(file_path)
-                    logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
-                except Exception as e:
-                    logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")

main/xiaozhi-server/core/providers/asr/tencent.py

@@ -39,28 +39,18 @@ class ASRProvider(ASRProviderBase):
             logger.bind(tag=TAG).warning("音频数据为空！")
             return None, None
 
-        file_path = None
         try:
             # 检查配置是否已设置
             if not self.secret_id or not self.secret_key:
                 logger.bind(tag=TAG).error("腾讯云语音识别配置未设置，无法进行识别")
-                return None, file_path
+                return None, None
 
-            # 将Opus音频数据解码为PCM
-            if audio_format == "pcm":
-                pcm_data = opus_data
-            else:
-                pcm_data = self.decode_opus(opus_data)
-            combined_pcm_data = b"".join(pcm_data)
-
-            # 判断是否保存为WAV文件
-            if self.delete_audio_file:
-                pass
-            else:
-                self.save_audio_to_file(pcm_data, session_id)
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
+                return "", None
 
             # 将音频数据转换为Base64编码
-            base64_audio = base64.b64encode(combined_pcm_data).decode("utf-8")
+            base64_audio = base64.b64encode(artifacts.pcm_bytes).decode("utf-8")
 
             # 构建请求体
             request_body = self._build_request_body(base64_audio)
@@ -77,11 +67,11 @@ class ASRProvider(ASRProviderBase):
                     f"腾讯云语音识别耗时: {time.time() - start_time:.3f}s | 结果: {result}"
                 )
 
-            return result, file_path
+            return result, artifacts.file_path
 
         except Exception as e:
             logger.bind(tag=TAG).error(f"处理音频时发生错误！{e}", exc_info=True)
-            return None, file_path
+            return None, None
 
     def _build_request_body(self, base64_audio: str) -> str:
         """构建请求体"""

main/xiaozhi-server/core/providers/asr/vosk.py

@@ -44,36 +44,22 @@ class ASRProvider(ASRProviderBase):
             raise
 
     async def speech_to_text(
-        self, audio_data: List[bytes], session_id: str, audio_format: str = "opus"
+        self, opus_data: List[bytes], session_id: str, audio_format="opus"
     ) -> Tuple[Optional[str], Optional[str]]:
         """将语音数据转换为文本"""
-        file_path = None
         try:
             # 检查模型是否加载成功
             if not self.model:
                 logger.bind(tag=TAG).error("VOSK模型未加载，无法进行识别")
                 return "", None
-
-            # 解码音频（如果原始格式是Opus）
-            if audio_format == "pcm":
-                pcm_data = audio_data
-            else:
-                pcm_data = self.decode_opus(audio_data)
-                
-            if not pcm_data:
-                logger.bind(tag=TAG).warning("解码后的PCM数据为空，无法进行识别")
+            
+            artifacts = self.get_current_artifacts()
+            if artifacts is None:
                 return "", None
-                
-            # 合并PCM数据
-            combined_pcm_data = b"".join(pcm_data)
-            if len(combined_pcm_data) == 0:
+            if not artifacts.pcm_bytes:
                 logger.bind(tag=TAG).warning("合并后的PCM数据为空")
                 return "", None
 
-            # 判断是否保存为WAV文件
-            if not self.delete_audio_file:
-                file_path = self.save_audio_to_file(pcm_data, session_id)
-
             start_time = time.time()
             
             
@@ -81,8 +67,8 @@ class ASRProvider(ASRProviderBase):
             chunk_size = 2000
             text_result = ""
             
-            for i in range(0, len(combined_pcm_data), chunk_size):
-                chunk = combined_pcm_data[i:i+chunk_size]
+            for i in range(0, len(artifacts.pcm_bytes), chunk_size):
+                chunk = artifacts.pcm_bytes[i:i+chunk_size]
                 if self.recognizer.AcceptWaveform(chunk):
                     result = json.loads(self.recognizer.Result())
                     text = result.get('text', '')
@@ -99,16 +85,8 @@ class ASRProvider(ASRProviderBase):
                 f"VOSK语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text_result.strip()}"
             )
             
-            return text_result.strip(), file_path
+            return text_result.strip(), artifacts.file_path
             
         except Exception as e:
             logger.bind(tag=TAG).error(f"VOSK语音识别失败: {e}")
             return "", None
-        finally:
-            # 文件清理逻辑
-            if self.delete_audio_file and file_path and os.path.exists(file_path):
-                try:
-                    os.remove(file_path)
-                    logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
-                except Exception as e:
-                    logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")