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>
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@ -9,7 +9,7 @@ log:
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log_level: INFO
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prompt: |
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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。
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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。
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你说话语速适中，用词通俗易懂，像一位值得信赖的老朋友。
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[核心能力]
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- 健康咨询：解答日常健康问题，提供科学、实用的建议
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@ -27,7 +27,7 @@ prompt: |
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- 给出确定性诊断
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- 推荐具体药品品牌
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system_error_response: "抱歉，阿福现在有点忙，咱们稍后再聊。"
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system_error_response: "抱歉，小虎现在有点忙，咱们稍后再聊。"
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end_prompt:
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enable: true
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@ -37,8 +37,8 @@ end_prompt:
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wakeup_words:
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- "你好小智"
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- "你好小志"
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- "阿福阿福"
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- "你好阿福"
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- "小虎小虎"
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- "你好小虎"
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selected_module:
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LLM: Qwen3Local
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@ -0,0 +1,103 @@
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"""
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Qwen3-ASR Local GPU Provider for xiaozhi-server
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Based on fun_local.py structure.
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"""
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import os
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import time
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import torch
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import asyncio
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import numpy as np
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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.providers.asr.base import ASRProviderBase
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from core.providers.asr.dto.dto import InterfaceType
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TAG = __name__
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logger = setup_logging()
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MAX_RETRIES = 2
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RETRY_DELAY = 1
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class ASRProvider(ASRProviderBase):
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def __init__(self, config: dict, delete_audio_file: bool):
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super().__init__()
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self.interface_type = InterfaceType.LOCAL
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self.output_dir = config.get("output_dir", "tmp/")
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self.delete_audio_file = delete_audio_file
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model_path = config.get("model_path", "Qwen/Qwen3-ASR-1.7B")
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device = config.get("device", "cuda:1")
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dtype_str = config.get("dtype", "bfloat16")
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dtype = getattr(torch, dtype_str, torch.bfloat16)
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os.makedirs(self.output_dir, exist_ok=True)
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logger.bind(tag=TAG).info(
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f"Qwen3ASR loading: model={model_path} device={device} dtype={dtype_str}"
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)
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t0 = time.time()
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from qwen_asr import Qwen3ASRModel
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self.model = Qwen3ASRModel.from_pretrained(
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model_path,
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dtype=dtype,
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device_map=device,
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max_new_tokens=256,
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)
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logger.bind(tag=TAG).info(f"Qwen3ASR loaded in {time.time()-t0:.1f}s")
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async def speech_to_text(
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self, opus_data: List[bytes], session_id: str, audio_format="opus", artifacts=None
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) -> Tuple[Optional[str], Optional[str]]:
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"""语音转文本 - 使用本地 Qwen3-ASR 模型"""
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retry_count = 0
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while retry_count < MAX_RETRIES:
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try:
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if artifacts is None:
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return "", None
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pcm_bytes = artifacts.pcm_bytes
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if not pcm_bytes or len(pcm_bytes) == 0:
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return "", artifacts.file_path
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# PCM bytes -> numpy float32 (16kHz, 16-bit, mono)
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audio_np = np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
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# 使用线程池避免阻塞事件循环
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start_time = time.time()
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results = await asyncio.to_thread(
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self.model.transcribe,
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audio=(audio_np, 16000),
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language=None, # auto-detect
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)
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if results and len(results) > 0:
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text = results[0].text
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lang = getattr(results[0], 'language', 'unknown')
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elapsed = time.time() - start_time
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logger.bind(tag=TAG).info(
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f"语音识别耗时: {elapsed:.3f}s | 语言: {lang} | 结果: {text}"
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)
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return text, artifacts.file_path
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else:
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return "", artifacts.file_path
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except OSError as e:
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retry_count += 1
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if retry_count >= MAX_RETRIES:
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logger.bind(tag=TAG).error(
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f"语音识别失败（已重试{retry_count}次）: {e}", exc_info=True
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)
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return "", None
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logger.bind(tag=TAG).warning(
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f"语音识别失败，正在重试（{retry_count}/{MAX_RETRIES}）: {e}"
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)
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time.sleep(RETRY_DELAY)
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except Exception as e:
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logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
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return "", None
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@ -46,20 +46,27 @@ class TTSProvider(TTSProviderBase):
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)
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def _generate_wav(self, text):
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"""同步合成，在线程池中调用"""
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"""同步合成"""
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import time
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from scipy.signal import resample_poly
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from math import gcd
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logger.bind(tag=TAG).info(f"TTS 收到文字: [{text}]")
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t0 = time.time()
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audio = self.tts.generate(text, sid=self.sid, speed=self.speed)
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samples = np.array(audio.samples, dtype=np.float32)
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t1 = time.time()
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# 重采样到目标采样率（设备要求 24000Hz，模型输出 44100Hz）
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target_sr = 24000
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if self.sample_rate != target_sr:
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g = gcd(self.sample_rate, target_sr)
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samples = resample_poly(samples, target_sr // g, self.sample_rate // g)
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t2 = time.time()
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pcm = (samples * 32767).astype(np.int16)
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audio_duration = len(pcm) / target_sr
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wav_io = io.BytesIO()
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with wave.open(wav_io, "wb") as wf:
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@ -67,7 +74,12 @@ class TTSProvider(TTSProviderBase):
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wf.setsampwidth(2)
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wf.setframerate(target_sr)
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wf.writeframes(pcm.tobytes())
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return wav_io.getvalue()
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wav_data = wav_io.getvalue()
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logger.bind(tag=TAG).info(
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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]}]"
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)
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return wav_data
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async def text_to_speak(self, text, output_file):
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wav_data = self._generate_wav(text)
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