mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-21 22:53:56 +08:00
242 lines
8.7 KiB
Python
242 lines
8.7 KiB
Python
import os
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import io
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import wave
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import uuid
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import json
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import time
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import queue
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import asyncio
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import traceback
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import threading
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import opuslib_next
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from abc import ABC, abstractmethod
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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.handle.receiveAudioHandle import startToChat
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from core.handle.reportHandle import enqueue_asr_report
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from core.utils.util import remove_punctuation_and_length
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from core.handle.receiveAudioHandle import handleAudioMessage
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TAG = __name__
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logger = setup_logging()
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class ASRProviderBase(ABC):
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def __init__(self):
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pass
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# 打开音频通道
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async def open_audio_channels(self, conn):
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conn.asr_priority_thread = threading.Thread(
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target=self.asr_text_priority_thread, args=(conn,), daemon=True
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)
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conn.asr_priority_thread.start()
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# 有序处理ASR音频
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def asr_text_priority_thread(self, conn):
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while not conn.stop_event.is_set():
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try:
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message = conn.asr_audio_queue.get(timeout=1)
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future = asyncio.run_coroutine_threadsafe(
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handleAudioMessage(conn, message),
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conn.loop,
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)
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future.result()
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except queue.Empty:
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continue
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except Exception as e:
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logger.bind(tag=TAG).error(
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f"处理ASR文本失败: {str(e)}, 类型: {type(e).__name__}, 堆栈: {traceback.format_exc()}"
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)
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continue
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# 接收音频
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async def receive_audio(self, conn, audio, audio_have_voice):
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if conn.client_listen_mode == "manual":
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# 手动模式：缓存音频用于ASR识别
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conn.asr_audio.append(audio)
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else:
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# 自动/实时模式：使用VAD检测
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have_voice = audio_have_voice
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conn.asr_audio.append(audio)
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if not have_voice and not conn.client_have_voice:
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conn.asr_audio = conn.asr_audio[-10:]
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return
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# 自动模式下通过VAD检测到语音停止时触发识别
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if conn.client_voice_stop:
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asr_audio_task = conn.asr_audio.copy()
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conn.asr_audio.clear()
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conn.reset_vad_states()
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if len(asr_audio_task) > 15:
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await self.handle_voice_stop(conn, asr_audio_task)
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# 处理语音停止
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async def handle_voice_stop(self, conn, asr_audio_task: List[bytes]):
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"""并行处理ASR和声纹识别"""
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try:
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total_start_time = time.monotonic()
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# 准备音频数据
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if conn.audio_format == "pcm":
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pcm_data = asr_audio_task
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else:
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pcm_data = self.decode_opus(asr_audio_task)
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combined_pcm_data = b"".join(pcm_data)
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# 预先准备WAV数据
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wav_data = None
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if conn.voiceprint_provider and combined_pcm_data:
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wav_data = self._pcm_to_wav(combined_pcm_data)
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# 定义ASR任务
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asr_task = self.speech_to_text(asr_audio_task, conn.session_id, conn.audio_format)
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if conn.voiceprint_provider and wav_data:
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voiceprint_task = conn.voiceprint_provider.identify_speaker(wav_data, conn.session_id)
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# 并发等待两个结果
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asr_result, voiceprint_result = await asyncio.gather(
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asr_task, voiceprint_task, return_exceptions=True
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)
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else:
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asr_result = await asr_task
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voiceprint_result = None
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# 记录识别结果 - 检查是否为异常
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if isinstance(asr_result, Exception):
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logger.bind(tag=TAG).error(f"ASR识别失败: {asr_result}")
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raw_text = ""
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else:
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raw_text, _ = asr_result
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if isinstance(voiceprint_result, Exception):
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logger.bind(tag=TAG).error(f"声纹识别失败: {voiceprint_result}")
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speaker_name = ""
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else:
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speaker_name = voiceprint_result
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if raw_text:
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logger.bind(tag=TAG).info(f"识别文本: {raw_text}")
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if speaker_name:
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logger.bind(tag=TAG).info(f"识别说话人: {speaker_name}")
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# 性能监控
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total_time = time.monotonic() - total_start_time
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logger.bind(tag=TAG).debug(f"总处理耗时: {total_time:.3f}s")
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# 检查文本长度
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text_len, _ = remove_punctuation_and_length(raw_text)
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self.stop_ws_connection()
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if text_len > 0:
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# 构建包含说话人信息的JSON字符串
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enhanced_text = self._build_enhanced_text(raw_text, speaker_name)
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# 使用自定义模块进行上报
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await startToChat(conn, enhanced_text)
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enqueue_asr_report(conn, enhanced_text, asr_audio_task)
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except Exception as e:
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logger.bind(tag=TAG).error(f"处理语音停止失败: {e}")
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import traceback
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logger.bind(tag=TAG).debug(f"异常详情: {traceback.format_exc()}")
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def _build_enhanced_text(self, text: str, speaker_name: Optional[str]) -> str:
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"""构建包含说话人信息的文本"""
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if speaker_name and speaker_name.strip():
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return json.dumps({
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"speaker": speaker_name,
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"content": text
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}, ensure_ascii=False)
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else:
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return text
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def _pcm_to_wav(self, pcm_data: bytes) -> bytes:
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"""将PCM数据转换为WAV格式"""
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if len(pcm_data) == 0:
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logger.bind(tag=TAG).warning("PCM数据为空，无法转换WAV")
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return b""
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# 确保数据长度是偶数（16位音频）
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if len(pcm_data) % 2 != 0:
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pcm_data = pcm_data[:-1]
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# 创建WAV文件头
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wav_buffer = io.BytesIO()
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try:
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with wave.open(wav_buffer, 'wb') as wav_file:
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wav_file.setnchannels(1) # 单声道
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wav_file.setsampwidth(2) # 16位
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wav_file.setframerate(16000) # 16kHz采样率
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wav_file.writeframes(pcm_data)
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wav_buffer.seek(0)
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wav_data = wav_buffer.read()
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return wav_data
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except Exception as e:
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logger.bind(tag=TAG).error(f"WAV转换失败: {e}")
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return b""
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def stop_ws_connection(self):
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pass
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def save_audio_to_file(self, pcm_data: List[bytes], session_id: str) -> str:
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"""PCM数据保存为WAV文件"""
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module_name = __name__.split(".")[-1]
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file_name = f"asr_{module_name}_{session_id}_{uuid.uuid4()}.wav"
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file_path = os.path.join(self.output_dir, file_name)
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with wave.open(file_path, "wb") as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2) # 2 bytes = 16-bit
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wf.setframerate(16000)
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wf.writeframes(b"".join(pcm_data))
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return file_path
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@abstractmethod
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async def speech_to_text(
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self, opus_data: List[bytes], session_id: str, audio_format="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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pass
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@staticmethod
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def decode_opus(opus_data: List[bytes]) -> List[bytes]:
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"""将Opus音频数据解码为PCM数据"""
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decoder = None
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try:
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decoder = opuslib_next.Decoder(16000, 1)
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pcm_data = []
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buffer_size = 960 # 每次处理960个采样点 (60ms at 16kHz)
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for i, opus_packet in enumerate(opus_data):
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try:
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if not opus_packet or len(opus_packet) == 0:
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continue
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pcm_frame = decoder.decode(opus_packet, buffer_size)
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if pcm_frame and len(pcm_frame) > 0:
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pcm_data.append(pcm_frame)
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except opuslib_next.OpusError as e:
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logger.bind(tag=TAG).warning(f"Opus解码错误，跳过数据包 {i}: {e}")
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except Exception as e:
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logger.bind(tag=TAG).error(f"音频处理错误，数据包 {i}: {e}")
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return pcm_data
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except Exception as e:
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logger.bind(tag=TAG).error(f"音频解码过程发生错误: {e}")
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return []
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finally:
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if decoder is not None:
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try:
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del decoder
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except Exception as e:
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logger.bind(tag=TAG).debug(f"释放decoder资源时出错: {e}")
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