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https://github.com/xinnan-tech/xiaozhi-esp32-server.git
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update:更新Qwen-asr-flash语音识别
This commit is contained in:
FAN-yeB
@@ -0,0 +1,7 @@
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-- 添加通义千问Qwen3-ASR-Flash语音识别服务配置
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delete from `ai_model_provider` where id = 'SYSTEM_ASR_Qwen3Flash';
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INSERT INTO `ai_model_provider` (`id`, `model_type`, `provider_code`, `name`, `fields`, `sort`, `creator`, `create_date`, `updater`, `update_date`) VALUES
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('SYSTEM_ASR_Qwen3Flash', 'ASR', 'qwen3_asr_flash', 'Qwen3-ASR-Flash语音识别', '[{"key":"api_key","label":"API密钥","type":"password"},{"key":"base_url","label":"服务地址","type":"string"},{"key":"model_name","label":"模型名称","type":"string"},{"key":"output_dir","label":"输出目录","type":"string"}]', 17, 1, NOW(), 1, NOW());
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delete from `ai_model_config` where id = 'ASR_Qwen3Flash';
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INSERT INTO `ai_model_config` VALUES ('ASR_Qwen3Flash', 'ASR', 'Qwen3-ASR-Flash', '通义千问语音识别服务', 0, 1, '{"type": "qwen3_asr_flash", "api_key": "", "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", "model_name": "qwen3-asr-flash", "output_dir": "tmp/", "enable_lid": true, "enable_itn": true}', 'https://help.aliyun.com/zh/bailian/', '支持多语言识别、歌唱识别、噪声拒识功能', 20, NULL, NULL, NULL, NULL);
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@@ -316,4 +316,12 @@ databaseChangeLog:
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changes:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202508271113.sql
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path: classpath:db/changelog/202508271113.sql
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- changeSet:
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id: 202509091633
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author: cgd
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changes:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202509091633.sql
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@@ -411,7 +411,22 @@ ASR:
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type: vosk
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model_path: 你的模型路径，如：models/vosk/vosk-model-small-cn-0.22
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output_dir: tmp/
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Qwen3ASRFlash:
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# 通义千问Qwen3-ASR-Flash语音识别服务，需要先在阿里云百炼平台创建API密钥
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# 申请步骤：
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# 1.登录阿里云百炼平台。https://bailian.console.aliyun.com/
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# 2.创建API-KEY https://bailian.console.aliyun.com/#/api-key
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# 3.Qwen3-ASR-Flash基于通义千问多模态基座，支持多语言识别、歌唱识别、噪声拒识等功能
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type: qwen3_asr_flash
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api_key: 你的阿里云百炼API密钥
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base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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model_name: qwen3-asr-flash
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output_dir: tmp/
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# ASR选项配置
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enable_lid: true # 自动语种检测
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enable_itn: true # 逆文本归一化
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#language: "zh" # 语种，支持zh、en、ja、ko等
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context: "" # 上下文信息，用于提高识别准确率，不超过10000 Token
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VAD:
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@@ -0,0 +1,169 @@
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import os
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import json
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import asyncio
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import tempfile
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import difflib
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from typing import Optional, Tuple, List
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import dashscope
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from config.logger import setup_logging
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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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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.STREAM
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"""Qwen3-ASR-Flash ASR初始化"""
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# 配置参数
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self.api_key = config.get("api_key")
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if not self.api_key:
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raise ValueError("Qwen3-ASR-Flash 需要配置 api_key")
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self.model_name = config.get("model_name", "qwen3-asr-flash")
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self.output_dir = config.get("output_dir", "./audio_output")
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self.delete_audio_file = delete_audio_file
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# ASR选项配置
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self.enable_lid = config.get("enable_lid", True) # 自动语种检测
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self.enable_itn = config.get("enable_itn", True) # 逆文本归一化
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self.language = config.get("language", None) # 指定语种，默认自动检测
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self.context = config.get("context", "") # 上下文信息，用于提高识别准确率
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# 确保输出目录存在
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os.makedirs(self.output_dir, exist_ok=True)
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def _prepare_audio_file(self, pcm_data: bytes) -> str:
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"""将PCM数据转换为WAV文件并返回文件路径"""
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try:
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import wave
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# 创建临时WAV文件
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with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file:
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temp_path = temp_file.name
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# 写入WAV格式
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with wave.open(temp_path, '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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return temp_path
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except Exception as e:
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logger.bind(tag=tag).error(f"音频文件准备失败: {e}")
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return None
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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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temp_file_path = None
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file_path = None
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try:
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# 解码音频数据
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if audio_format == "pcm":
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pcm_data = opus_data
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else:
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pcm_data = self.decode_opus(opus_data)
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combined_pcm_data = b"".join(pcm_data)
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if len(combined_pcm_data) == 0:
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logger.bind(tag=tag).warning("音频数据为空")
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return "", None
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# 准备音频文件
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temp_file_path = self._prepare_audio_file(combined_pcm_data)
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if not temp_file_path:
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return "", None
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# 保存音频文件（如果需要）
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if not self.delete_audio_file:
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file_path = self.save_audio_to_file(pcm_data, session_id)
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# 构造请求消息
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messages = [
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{
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"role": "user",
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"content": [
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{"audio": temp_file_path}
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]
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}
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]
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# 如果有上下文信息，添加system消息
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if self.context:
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messages.insert(0, {
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"role": "system",
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"content": [
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{"text": self.context}
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]
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})
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# 准备ASR选项
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asr_options = {
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"enable_lid": self.enable_lid,
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"enable_itn": self.enable_itn
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}
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# 如果指定了语种，添加到选项中
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if self.language:
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asr_options["language"] = self.language
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# 设置API密钥
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dashscope.api_key = self.api_key
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# 发送流式请求
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response = dashscope.MultiModalConversation.call(
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model=self.model_name,
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messages=messages,
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result_format="message",
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asr_options=asr_options,
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stream=True
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)
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# 处理流式响应
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full_text = ""
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last_text = "" # 用于存储上一个文本片段
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for chunk in response:
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try:
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text = chunk["output"]["choices"][0]["message"].content[0]["text"]
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# 标准化文本片段（去除首尾空格）
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normalized_text = text.strip()
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# 只有当新文本片段与上一个不同时才处理
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if normalized_text != last_text:
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# 提取新增的文本部分
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# 通过比较当前文本和上一个文本，找到新增的部分
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if normalized_text.startswith(last_text):
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# 如果当前文本以最后一个文本开头，则新增部分是两者的差集
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new_part = normalized_text[len(last_text):]
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else:
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# 如果不以最后一个文本开头，说明识别结果发生了较大变化，直接使用当前文本
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new_part = normalized_text
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# 将新增部分添加到完整文本中
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full_text += new_part
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last_text = normalized_text
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# 这里可以实时处理文本片段，例如通过回调函数
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except:
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pass
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return full_text, file_path
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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}")
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return "", file_path
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finally:
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# 清理临时文件
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if temp_file_path and os.path.exists(temp_file_path):
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try:
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os.unlink(temp_file_path)
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except Exception as e:
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logger.bind(tag=tag).warning(f"清理临时文件失败: {e}")
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