feat:新增vosk本地语音识别服务

.gitignore

@@ -155,4 +155,5 @@ main/manager-web/node_modules
 # model files
 main/xiaozhi-server/models/SenseVoiceSmall/model.pt
 main/xiaozhi-server/models/sherpa-onnx*
+main/xiaozhi-server/models/vosk-model*
 my_wakeup_words.mp3

main/xiaozhi-server/config.yaml

@@ -162,6 +162,10 @@ ASR:
     type: fun_local
     model_dir: models/SenseVoiceSmall
     output_dir: tmp/
+  VoskASR:
+    type: vosk_local
+    model_dir: models/vosk-model
+    output_dir: tmp/
   SherpaASR:
     type: sherpa_onnx_local
     model_dir: models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17

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

@@ -0,0 +1,133 @@
+import time
+import wave
+import os
+import sys
+import io
+import json
+from config.logger import setup_logging
+from typing import Optional, Tuple, List
+import uuid
+import opuslib_next
+from core.providers.asr.base import ASRProviderBase
+
+from vosk import Model, KaldiRecognizer, SetLogLevel
+
+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 ASRProvider(ASRProviderBase):
+    def __init__(self, config: dict, delete_audio_file: bool):
+        self.model_dir = config.get("model_dir")
+        self.output_dir = config.get("output_dir")
+        self.delete_audio_file = delete_audio_file
+        self.sample_rate = 16000
+
+        # 确保输出目录存在
+        os.makedirs(self.output_dir, exist_ok=True)
+        
+        # 初始化VOSK模型
+        with CaptureOutput():
+            SetLogLevel(-1)
+            logger.bind(tag=TAG).info(f"正在加载VOSK模型: {self.model_dir}")
+            self.model = Model(self.model_dir)
+            logger.bind(tag=TAG).info("VOSK模型加载完成")
+
+    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(self.sample_rate, 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(self.sample_rate)
+            wf.writeframes(b"".join(pcm_data))
+
+        return file_path
+
+    async 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()
+            
+            # 创建识别器
+            recognizer = KaldiRecognizer(self.model, self.sample_rate)
+            recognizer.SetWords(True)  # 启用词级时间戳
+            
+            # 读取WAV文件并进行识别
+            with wave.open(file_path, "rb") as wf:
+                # 确保音频格式正确
+                if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getframerate() != self.sample_rate:
+                    logger.bind(tag=TAG).error(f"音频格式不支持: 需要16kHz, 16-bit, 单声道")
+                    return "", file_path
+                
+                # 分块处理音频数据
+                result_text = ""
+                while True:
+                    data = wf.readframes(4000)  # 读取音频块
+                    if len(data) == 0:
+                        break
+                    
+                    if recognizer.AcceptWaveform(data):
+                        result_json = json.loads(recognizer.Result())
+                        if "text" in result_json and result_json["text"].strip():
+                            result_text += result_json["text"] + " "
+                
+                # 获取最终结果
+                final_result = json.loads(recognizer.FinalResult())
+                if "text" in final_result and final_result["text"].strip():
+                    result_text += final_result["text"]
+                
+                result_text = result_text.strip()
+                
+            logger.bind(tag=TAG).debug(f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {result_text}")
+
+            return result_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
+
+        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/requirements.txt

@@ -22,4 +22,5 @@ mem0ai==0.1.62
 bs4==0.0.2
 modelscope==1.23.2
 sherpa_onnx==1.11.0
-cnlunar==0.2.0
+cnlunar==0.2.0
+vosk==0.3.45