config: rename 阿福 to 小虎, wake word 小虎小虎

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

config/.config.yaml

@@ -9,7 +9,7 @@ log:
   log_level: INFO
 
 prompt: |
-  你是阿福，一位经验丰富、亲切温暖的家庭医生。你服务的对象主要是中老年人。
+  你是小虎，一位经验丰富、亲切温暖的家庭医生。你服务的对象主要是中老年人。
   你说话语速适中，用词通俗易懂，像一位值得信赖的老朋友。
   [核心能力]
   - 健康咨询：解答日常健康问题，提供科学、实用的建议
@@ -27,7 +27,7 @@ prompt: |
   - 给出确定性诊断
   - 推荐具体药品品牌
 
-system_error_response: "抱歉，阿福现在有点忙，咱们稍后再聊。"
+system_error_response: "抱歉，小虎现在有点忙，咱们稍后再聊。"
 
 end_prompt:
   enable: true
@@ -37,8 +37,8 @@ end_prompt:
 wakeup_words:
   - "你好小智"
   - "你好小志"
-  - "阿福阿福"
+  - "小虎小虎"
-  - "你好阿福"
+  - "你好小虎"
 
 selected_module:
   LLM: Qwen3Local

modules/asr/qwen3_asr_local.py

@@ -0,0 +1,103 @@
+"""
+Qwen3-ASR Local GPU Provider for xiaozhi-server
+Based on fun_local.py structure.
+"""
+
+import os
+import time
+import torch
+import asyncio
+import numpy as np
+
+from config.logger import setup_logging
+from typing import Optional, Tuple, List
+from core.providers.asr.base import ASRProviderBase
+from core.providers.asr.dto.dto import InterfaceType
+
+TAG = __name__
+logger = setup_logging()
+
+MAX_RETRIES = 2
+RETRY_DELAY = 1
+
+
+class ASRProvider(ASRProviderBase):
+    def __init__(self, config: dict, delete_audio_file: bool):
+        super().__init__()
+        self.interface_type = InterfaceType.LOCAL
+        self.output_dir = config.get("output_dir", "tmp/")
+        self.delete_audio_file = delete_audio_file
+
+        model_path = config.get("model_path", "Qwen/Qwen3-ASR-1.7B")
+        device = config.get("device", "cuda:1")
+        dtype_str = config.get("dtype", "bfloat16")
+        dtype = getattr(torch, dtype_str, torch.bfloat16)
+
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        logger.bind(tag=TAG).info(
+            f"Qwen3ASR loading: model={model_path} device={device} dtype={dtype_str}"
+        )
+        t0 = time.time()
+
+        from qwen_asr import Qwen3ASRModel
+        self.model = Qwen3ASRModel.from_pretrained(
+            model_path,
+            dtype=dtype,
+            device_map=device,
+            max_new_tokens=256,
+        )
+        logger.bind(tag=TAG).info(f"Qwen3ASR loaded in {time.time()-t0:.1f}s")
+
+    async def speech_to_text(
+        self, opus_data: List[bytes], session_id: str, audio_format="opus", artifacts=None
+    ) -> Tuple[Optional[str], Optional[str]]:
+        """语音转文本 - 使用本地 Qwen3-ASR 模型"""
+        retry_count = 0
+
+        while retry_count < MAX_RETRIES:
+            try:
+                if artifacts is None:
+                    return "", None
+
+                pcm_bytes = artifacts.pcm_bytes
+                if not pcm_bytes or len(pcm_bytes) == 0:
+                    return "", artifacts.file_path
+
+                # PCM bytes -> numpy float32 (16kHz, 16-bit, mono)
+                audio_np = np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+                # 使用线程池避免阻塞事件循环
+                start_time = time.time()
+                results = await asyncio.to_thread(
+                    self.model.transcribe,
+                    audio=(audio_np, 16000),
+                    language=None,  # auto-detect
+                )
+
+                if results and len(results) > 0:
+                    text = results[0].text
+                    lang = getattr(results[0], 'language', 'unknown')
+                    elapsed = time.time() - start_time
+                    logger.bind(tag=TAG).info(
+                        f"语音识别耗时: {elapsed:.3f}s | 语言: {lang} | 结果: {text}"
+                    )
+                    return text, artifacts.file_path
+                else:
+                    return "", artifacts.file_path
+
+            except OSError as e:
+                retry_count += 1
+                if retry_count >= MAX_RETRIES:
+                    logger.bind(tag=TAG).error(
+                        f"语音识别失败（已重试{retry_count}次）: {e}", exc_info=True
+                    )
+                    return "", None
+                logger.bind(tag=TAG).warning(
+                    f"语音识别失败，正在重试（{retry_count}/{MAX_RETRIES}）: {e}"
+                )
+                time.sleep(RETRY_DELAY)
+
+            except Exception as e:
+                logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
+                return "", None

modules/tts/sherpa_tts.py

@@ -46,20 +46,27 @@ class TTSProvider(TTSProviderBase):
         )
 
     def _generate_wav(self, text):
-        """同步合成，在线程池中调用"""
+        """同步合成"""
+        import time
         from scipy.signal import resample_poly
         from math import gcd
 
+        logger.bind(tag=TAG).info(f"TTS 收到文字: [{text}]")
+        t0 = time.time()
+
         audio = self.tts.generate(text, sid=self.sid, speed=self.speed)
         samples = np.array(audio.samples, dtype=np.float32)
+        t1 = time.time()
 
         # 重采样到目标采样率（设备要求 24000Hz，模型输出 44100Hz）
         target_sr = 24000
         if self.sample_rate != target_sr:
             g = gcd(self.sample_rate, target_sr)
             samples = resample_poly(samples, target_sr // g, self.sample_rate // g)
+        t2 = time.time()
 
         pcm = (samples * 32767).astype(np.int16)
+        audio_duration = len(pcm) / target_sr
 
         wav_io = io.BytesIO()
         with wave.open(wav_io, "wb") as wf:
@@ -67,7 +74,12 @@ class TTSProvider(TTSProviderBase):
             wf.setsampwidth(2)
             wf.setframerate(target_sr)
             wf.writeframes(pcm.tobytes())
-        return wav_io.getvalue()
+        wav_data = wav_io.getvalue()
+
+        logger.bind(tag=TAG).info(
+            f"TTS 完成: 合成={t1-t0:.2f}s 重采样={t2-t1:.2f}s 音频时长={audio_duration:.1f}s 大小={len(wav_data)}B [{text[:20]}]"
+        )
+        return wav_data
 
     async def text_to_speak(self, text, output_file):
         wav_data = self._generate_wav(text)