Doubao asr (#117)

* 添加豆包在线 ASR (#82)
---------
Co-authored-by: 胡垚 <qsct9501@163.com>

core/utils/asr.py

@@ -1,132 +1,24 @@
-import time
-import wave
+import importlib
+import logging
 import os
 import sys
-import io
-from abc import ABC, abstractmethod
-from config.logger import setup_logging
-from typing import Optional, Tuple, List
+import time
+import wave
 import uuid
-
-import opuslib_next
-from funasr import AutoModel
-from funasr.utils.postprocess_utils import rich_transcription_postprocess
+from abc import ABC, abstractmethod
+from typing import Optional, Tuple, List
+from core.providers.asr.base import ASRProviderBase
+from config.logger import setup_logging
 
 TAG = __name__
 logger = setup_logging()
 
-# 捕获标准输出
-class CaptureOutput:
-    def __enter__(self):
-        self._output = io.StringIO()
-        self._original_stdout = sys.stdout
-        sys.stdout = self._output
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout = self._original_stdout
-        self.output = self._output.getvalue()
-        self._output.close()
-
-        # 将捕获到的内容通过 logger 输出
-        if self.output:
-            logger.bind(tag=TAG).info(self.output.strip())
-
-class ASR(ABC):
-    @abstractmethod
-    def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
-        """解码Opus数据并保存为WAV文件"""
-        pass
-
-    @abstractmethod
-    def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
-        """将语音数据转换为文本"""
-        pass
-
-
-class FunASR(ASR):
-    def __init__(self, config: dict, delete_audio_file: bool):
-        self.model_dir = config.get("model_dir")
-        self.output_dir = config.get("output_dir")  # 修正配置键名
-        self.delete_audio_file = delete_audio_file
-
-        # 确保输出目录存在
-        os.makedirs(self.output_dir, exist_ok=True)
-        with CaptureOutput():
-            self.model = AutoModel(
-                model=self.model_dir,
-                vad_kwargs={"max_single_segment_time": 30000},
-                disable_update=True,
-                hub="hf"
-                # device="cuda:0",  # 启用GPU加速
-            )
-
-    def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
-        """将Opus音频数据解码并保存为WAV文件"""
-        file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
-        file_path = os.path.join(self.output_dir, file_name)
-
-        decoder = opuslib_next.Decoder(16000, 1)  # 16kHz, 单声道
-        pcm_data = []
-
-        for opus_packet in opus_data:
-            try:
-                pcm_frame = decoder.decode(opus_packet, 960)  # 960 samples = 60ms
-                pcm_data.append(pcm_frame)
-            except opuslib_next.OpusError as e:
-                logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
-
-        with wave.open(file_path, "wb") as wf:
-            wf.setnchannels(1)
-            wf.setsampwidth(2)  # 2 bytes = 16-bit
-            wf.setframerate(16000)
-            wf.writeframes(b"".join(pcm_data))
-
-        return file_path
-
-    def speech_to_text(self, opus_data: List[bytes], session_id: str) -> 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()
-            file_path = self.save_audio_to_file(opus_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}")
-
-            # 语音识别
-            start_time = time.time()
-            result = self.model.generate(
-                input=file_path,
-                cache={},
-                language="auto",
-                use_itn=True,
-                batch_size_s=60,
-            )
-            text = rich_transcription_postprocess(result[0]["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
-
-        except Exception as e:
-            logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
-            return None, 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}")
-
-
-def create_instance(class_name: str, *args, **kwargs) -> ASR:
+def create_instance(class_name: str, *args, **kwargs) -> ASRProviderBase:
     """工厂方法创建ASR实例"""
-    cls_map = {
-        "FunASR": FunASR,
-        # 可扩展其他ASR实现
-    }
+    if os.path.exists(os.path.join('core', 'providers', 'asr', f'{class_name}.py')):
+        lib_name = f'core.providers.asr.{class_name}'
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
+        return sys.modules[lib_name].ASRProvider(*args, **kwargs)
 
-    if cls := cls_map.get(class_name):
-        return cls(*args, **kwargs)
-    raise ValueError(f"不支持的ASR类型: {class_name}")
+    raise ValueError(f"不支持的ASR类型: {class_name}，请检查该配置的type是否设置正确")