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Merge branch 'main' into py_MinmaxStreamTTS_test

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CGD
2025-09-09 13:17:09 +08:00
committed by GitHub
parent 6bc64aa4aa ddd63f4136
commit c3c65708e0
10 changed files with 221 additions and 16 deletions
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main/manager-api/src/main/java/xiaozhi/modules/config/service/impl/ConfigServiceImpl.java
+14
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@@ -298,6 +298,20 @@ public class ConfigServiceImpl implements ConfigService {
Map<String, Object> voiceprintConfig = new HashMap<>();
voiceprintConfig.put("url", voiceprintUrl);
voiceprintConfig.put("speakers", speakers);
// 获取声纹识别相似度阈值，默认0.4
String thresholdStr = sysParamsService.getValue("server.voiceprint_similarity_threshold", true);
if (StringUtils.isNotBlank(thresholdStr) && !"null".equals(thresholdStr)) {
try {
double threshold = Double.parseDouble(thresholdStr);
voiceprintConfig.put("similarity_threshold", threshold);
} catch (NumberFormatException e) {
// 如果解析失败，使用默认值0.4
voiceprintConfig.put("similarity_threshold", 0.4);
}
} else {
voiceprintConfig.put("similarity_threshold", 0.4);
}
result.put("voiceprint", voiceprintConfig);
} catch (Exception e) {
main/manager-api/src/main/resources/db/changelog/202508271113.sql
+24
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@@ -0,0 +1,24 @@
-- VOSK ASR模型供应器
delete from `ai_model_provider` where id = 'SYSTEM_ASR_VoskASR';
INSERT INTO `ai_model_provider` (`id`, `model_type`, `provider_code`, `name`, `fields`, `sort`, `creator`, `create_date`, `updater`, `update_date`) VALUES
('SYSTEM_ASR_VoskASR', 'ASR', 'vosk', 'VOSK离线语音识别', '[{"key": "model_path", "type": "string", "label": "模型路径"}, {"key": "output_dir", "type": "string", "label": "输出目录"}]', 11, 1, NOW(), 1, NOW());
-- VOSK ASR模型配置
delete from `ai_model_config` where id = 'ASR_VoskASR';
INSERT INTO `ai_model_config` VALUES ('ASR_VoskASR', 'ASR', 'VoskASR', 'VOSK离线语音识别', 0, 1, '{\"type\": \"vosk\", \"model_path\": \"\", \"output_dir\": \"tmp/\"}', NULL, NULL, 11, NULL, NULL, NULL, NULL);
-- 更新VOSK ASR配置说明
UPDATE `ai_model_config` SET
`doc_link` = 'https://alphacephei.com/vosk/',
`remark` = 'VOSK ASR配置说明：
1. VOSK是一个离线语音识别库，支持多种语言
2. 需要先下载模型文件：https://alphacephei.com/vosk/models
3. 中文模型推荐使用vosk-model-small-cn-0.22或vosk-model-cn-0.22
4. 完全离线运行，无需网络连接
5. 输出文件保存在tmp/目录
使用步骤：
1. 访问 https://alphacephei.com/vosk/models 下载中文模型
2. 解压模型文件到项目目录下的models/vosk/文件夹
3. 在配置中指定正确的模型路径
4. 注意：VOSK中文模型输出不带标点符号，词与词之间会有空格
' WHERE `id` = 'ASR_VoskASR';
main/manager-api/src/main/resources/db/changelog/202509081140.sql
+4
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@@ -0,0 +1,4 @@
-- 添加声纹识别相似度阈值参数配置
delete from `sys_params` where id = 115;
INSERT INTO `sys_params` (id, param_code, param_value, value_type, param_type, remark)
VALUES (115, 'server.voiceprint_similarity_threshold', '0.4', 'string', 1, '声纹识别相似度阈值，范围0.0-1.0，默认0.4，数值越高越严格');
main/manager-api/src/main/resources/db/changelog/db.changelog-master.yaml
+15 -1
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@@ -303,6 +303,13 @@ databaseChangeLog:
- sqlFile:
encoding: utf8
path: classpath:db/changelog/202508131557.sql
- changeSet:
id: 202508271113
author: cgd
changes:
- sqlFile:
encoding: utf8
path: classpath:db/changelog/202508271113.sql
- changeSet:
id: 202509051745
author: RanChen
@@ -310,10 +317,17 @@ databaseChangeLog:
- sqlFile:
encoding: utf8
path: classpath:db/changelog/202509051745.sql
- changeSet:
id: 202509081140
author: cgd
changes:
- sqlFile:
encoding: utf8
path: classpath:db/changelog/202509081140.sql
- changeSet:
id: 202509091042
author: cgd
changes:
- sqlFile:
encoding: utf8
path: classpath:db/changelog/202509091042.sql
path: classpath:db/changelog/202509091042.sql
main/xiaozhi-server/config.yaml
+24 -2
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@@ -114,7 +114,10 @@ plugins:
# 想稳定一点就自行申请替换，每天有1000次免费调用
# 申请地址：https://console.qweather.com/#/apps/create-key/over
# 申请后通过这个链接可以找到自己的apihost：https://console.qweather.com/setting?lang=zh
get_weather: {"api_host":"mj7p3y7naa.re.qweatherapi.com", "api_key": "a861d0d5e7bf4ee1a83d9a9e4f96d4da", "default_location": "广州" }
get_weather:
api_host: "mj7p3y7naa.re.qweatherapi.com"
api_key: "a861d0d5e7bf4ee1a83d9a9e4f96d4da"
default_location: "广州"
# 获取新闻插件的配置，这里根据需要的新闻类型传入对应的url链接，默认支持社会、科技、财经新闻
# 更多类型的新闻列表查看 https://www.chinanews.com.cn/rss/
get_news_from_chinanews:
@@ -148,6 +151,9 @@ voiceprint:
- "test1,张三,张三是一个程序员"
- "test2,李四,李四是一个产品经理"
- "test3,王五,王五是一个设计师"
# 声纹识别相似度阈值，范围0.0-1.0，默认0.4
# 数值越高越严格，减少误识别但可能增加拒识率
similarity_threshold: 0.4
# #####################################################################################
# ################################以下是角色模型配置######################################
@@ -389,6 +395,22 @@ ASR:
base_url: https://api.groq.com/openai/v1/audio/transcriptions
model_name: whisper-large-v3-turbo
output_dir: tmp/
VoskASR:
# 官方网站：https://alphacephei.com/vosk/
# 配置说明：
# 1. VOSK是一个离线语音识别库，支持多种语言
# 2. 需要先下载模型文件：https://alphacephei.com/vosk/models
# 3. 中文模型推荐使用vosk-model-small-cn-0.22或vosk-model-cn-0.22
# 4. 完全离线运行，无需网络连接
# 5. 输出文件保存在tmp/目录
# 使用步骤：
# 1. 访问 https://alphacephei.com/vosk/models 下载对应的模型
# 2. 解压模型文件到项目目录下的models/vosk/文件夹
# 3. 在配置中指定正确的模型路径
# 4. 注意：VOSK中文模型输出不带标点符号，词与词之间会有空格
type: vosk
model_path: 你的模型路径，如：models/vosk/vosk-model-small-cn-0.22
output_dir: tmp/
@@ -884,4 +906,4 @@ TTS:
audio_format: "pcm"
# 默认音色，如需其他音色可到项目assets文件夹下注册
voice: "jay_klee"
output_dir: tmp/
output_dir: tmp/
main/xiaozhi-server/core/connection.py
+7 -3
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@@ -432,10 +432,14 @@ class ConnectionHandler:
try:
voiceprint_config = self.config.get("voiceprint", {})
if voiceprint_config:
self.voiceprint_provider = VoiceprintProvider(voiceprint_config)
self.logger.bind(tag=TAG).info("声纹识别功能已在连接时动态启用")
voiceprint_provider = VoiceprintProvider(voiceprint_config)
if voiceprint_provider is not None and voiceprint_provider.enabled:
self.voiceprint_provider = voiceprint_provider
self.logger.bind(tag=TAG).info("声纹识别功能已在连接时动态启用")
else:
self.logger.bind(tag=TAG).warning("声纹识别功能启用但配置不完整")
else:
self.logger.bind(tag=TAG).info("声纹识别功能未启用或配置不完整")
self.logger.bind(tag=TAG).info("声纹识别功能未启用")
except Exception as e:
self.logger.bind(tag=TAG).warning(f"声纹识别初始化失败: {str(e)}")
main/xiaozhi-server/core/providers/asr/vosk.py
+114
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@@ -0,0 +1,114 @@
import os
import json
import time
from typing import Optional, Tuple, List
from .base import ASRProviderBase
from config.logger import setup_logging
from core.providers.asr.dto.dto import InterfaceType
import vosk
TAG = __name__
logger = setup_logging()
class ASRProvider(ASRProviderBase):
def __init__(self, config: dict, delete_audio_file: bool = True):
super().__init__()
self.interface_type = InterfaceType.LOCAL
self.model_path = config.get("model_path")
self.output_dir = config.get("output_dir", "tmp/")
self.delete_audio_file = delete_audio_file
# 初始化VOSK模型
self.model = None
self.recognizer = None
self._load_model()
# 确保输出目录存在
os.makedirs(self.output_dir, exist_ok=True)
def _load_model(self):
"""加载VOSK模型"""
try:
if not os.path.exists(self.model_path):
raise FileNotFoundError(f"VOSK模型路径不存在: {self.model_path}")
logger.bind(tag=TAG).info(f"正在加载VOSK模型: {self.model_path}")
self.model = vosk.Model(self.model_path)
# 初始化VOSK识别器（采样率必须为16kHz）
self.recognizer = vosk.KaldiRecognizer(self.model, 16000)
logger.bind(tag=TAG).info("VOSK模型加载成功")
except Exception as e:
logger.bind(tag=TAG).error(f"加载VOSK模型失败: {e}")
raise
async def speech_to_text(
self, audio_data: List[bytes], session_id: str, audio_format: str = "opus"
) -> Tuple[Optional[str], Optional[str]]:
"""将语音数据转换为文本"""
file_path = None
try:
# 检查模型是否加载成功
if not self.model:
logger.bind(tag=TAG).error("VOSK模型未加载，无法进行识别")
return "", None
# 解码音频（如果原始格式是Opus）
if audio_format == "pcm":
pcm_data = audio_data
else:
pcm_data = self.decode_opus(audio_data)
if not pcm_data:
logger.bind(tag=TAG).warning("解码后的PCM数据为空，无法进行识别")
return "", None
# 合并PCM数据
combined_pcm_data = b"".join(pcm_data)
if len(combined_pcm_data) == 0:
logger.bind(tag=TAG).warning("合并后的PCM数据为空")
return "", None
# 判断是否保存为WAV文件
if not self.delete_audio_file:
file_path = self.save_audio_to_file(pcm_data, session_id)
start_time = time.time()
# 进行识别（VOSK推荐每次送入2000字节的数据）
chunk_size = 2000
text_result = ""
for i in range(0, len(combined_pcm_data), chunk_size):
chunk = combined_pcm_data[i:i+chunk_size]
if self.recognizer.AcceptWaveform(chunk):
result = json.loads(self.recognizer.Result())
text = result.get('text', '')
if text:
text_result += text + " "
# 获取最终结果
final_result = json.loads(self.recognizer.FinalResult())
final_text = final_result.get('text', '')
if final_text:
text_result += final_text
logger.bind(tag=TAG).debug(
f"VOSK语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text_result.strip()}"
)
return text_result.strip(), file_path
except Exception as e:
logger.bind(tag=TAG).error(f"VOSK语音识别失败: {e}")
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/core/providers/tools/server_mcp/mcp_client.py
+5 -6
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@@ -167,14 +167,13 @@ class ServerMCPClient:
# 建立SSEClient
elif "url" in self.config:
headers = dict(self.config.get("headers", {}))
# TODO 兼容旧版本
if "API_ACCESS_TOKEN" in self.config:
headers = {
"Authorization": f"Bearer {self.config['API_ACCESS_TOKEN']}"
}
else:
headers = {}
headers["Authorization"] = f"Bearer {self.config['API_ACCESS_TOKEN']}"
self.logger.bind(tag=TAG).warning(f"你正在使用旧过时的配置 API_ACCESS_TOKEN ，请在.mcp_server_settings.json中将API_ACCESS_TOKEN直接设置在headers中，例如 'Authorization': 'Bearer API_ACCESS_TOKEN'")
sse_r, sse_w = await stack.enter_async_context(
sse_client(self.config["url"], headers=headers)
sse_client(self.config["url"], headers=headers, timeout=self.config.get("timeout", 5), sse_read_timeout=self.config.get("sse_read_timeout", 60 * 5))
)
read_stream, write_stream = sse_r, sse_w
main/xiaozhi-server/core/utils/voiceprint_provider.py
+8 -4
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@@ -19,6 +19,8 @@ class VoiceprintProvider:
self.original_url = config.get("url", "")
self.speakers = config.get("speakers", [])
self.speaker_map = self._parse_speakers()
# 声纹识别相似度阈值，默认0.4
self.similarity_threshold = float(config.get("similarity_threshold", 0.4))
# 解析API地址和密钥
self.api_url = None
@@ -62,7 +64,7 @@ class VoiceprintProvider:
# 进行健康检查，验证服务器是否可用
if self._check_server_health():
self.enabled = True
logger.bind(tag=TAG).info(f"声纹识别已启用: API={self.api_url}, 说话人={len(self.speaker_ids)}个")
logger.bind(tag=TAG).info(f"声纹识别已启用: API={self.api_url}, 说话人={len(self.speaker_ids)}个, 相似度阈值={self.similarity_threshold}")
else:
self.enabled = False
logger.bind(tag=TAG).warning(f"声纹识别服务器不可用，声纹识别已禁用: {self.api_url}")
@@ -169,12 +171,14 @@ class VoiceprintProvider:
logger.bind(tag=TAG).info(f"声纹识别耗时: {total_elapsed_time:.3f}s")
# 置信度检查
if score < 0.5:
logger.bind(tag=TAG).warning(f"声纹识别置信度较低: {score:.3f}")
# 相似度阈值检查
if score < self.similarity_threshold:
logger.bind(tag=TAG).warning(f"声纹识别相似度{score:.3f}低于阈值{self.similarity_threshold}")
return "未知说话人"
if speaker_id and speaker_id in self.speaker_map:
result_name = self.speaker_map[speaker_id]["name"]
logger.bind(tag=TAG).info(f"声纹识别成功: {result_name} (相似度: {score:.3f})")
return result_name
else:
logger.bind(tag=TAG).warning(f"未识别的说话人ID: {speaker_id}")
main/xiaozhi-server/mcp_server_settings.json
+6
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@@ -36,6 +36,12 @@
"command": "npx",
"args": ["-y", "@simonb97/server-win-cli"],
"link": "https://github.com/SimonB97/win-cli-mcp-server"
},
"sse-mcp-server": {
"url": "http://localhost:8080/sse",
"headers": {
"Authorization": "Bearer YOUR TOKEN"
}
}
}
}