增加ASR情绪和语种返回

This commit is contained in:
Sakura-RanChen
2026-01-05 11:17:04 +08:00
parent 85919e800e
commit e6108ccbbd
10 changed files with 115 additions and 175 deletions
@@ -1,14 +0,0 @@
-- 为阿里百炼流式语音合成添加多语言音色配置字段
UPDATE `ai_model_provider` SET fields = '[{"key":"api_key","type":"string","label":"API密钥"},{"key":"output_dir","type":"string","label":"输出目录"},{"key":"model","type":"string","label":"模型名称"},{"key":"format","label":"音频格式","type":"string"},{"key":"sample_rate","label":"采样率","type":"number"},{"key": "volume", "type": "number", "label": "音量"},{"key": "rate", "type": "number", "label": "语速"},{"key": "pitch", "type": "number", "label": "音调"},{"key":"voice","type":"string","label":"默认音色"},{"key": "voice_zh", "type": "string", "label": "中文音色"},{"key": "voice_yue", "type": "string", "label": "粤语音色"},{"key": "voice_en", "type": "string", "label": "英语音色"},{"key": "voice_ja", "type": "string", "label": "日语音色"},{"key": "voice_ko", "type": "string", "label": "韩语音色"}]' WHERE id = 'SYSTEM_TTS_AliBLStreamTTS';
-- 更新配置说明
UPDATE `ai_model_config` SET
`doc_link` = 'https://bailian.console.aliyun.com/?apiKey=1#/api-key',
`remark` = '阿里百炼流式TTS说明:
1. 访问 https://bailian.console.aliyun.com/?apiKey=1#/api-key 创建项目并获取appkey
2. 支持实时流式合成,具有较低的延迟
3. 支持多种音色设置和音频参数调整
4. 使用FunASR进行语音识别时,可以自动选择对应语言音色
5. 支持CosyVoice-V3-Flash等大模型音色,价格实惠(1元/万字符)
6. 支持实时调节音量、语速、音调等参数
' WHERE `id` = 'TTS_AliBLStreamTTS';
-10
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@@ -887,16 +887,6 @@ TTS:
access_key_secret: 你的阿里云账号access_key_secret
# 截至2025年7月21日大模型音色只有北京节点采用,其他节点暂不支持
host: nls-gateway-cn-beijing.aliyuncs.com
# 多语言音色配置 - 根据ASR识别的语言标签自动切换音色
# 多语言仅限搭配FunASR SenseVoiceSmall模型使用
voice_zh: longxiaochun_v2 # 中文音色
voice_en: loongeva_v2 # 英文音色
voice_yue: longjiayi_v2 # 粤语音色
voice_ja: oongtomoka_v2 # 日语音色
voice_ko: loongkyong_v2 # 韩语音色
default_voice: longxiaochun_v2 # 默认音色(当语言标签不匹配或无语言标签时使用)
# 以下可不用设置,使用默认设置
# format: pcm # 音频格式:pcm、wav、mp3
# sample_rate: 16000 # 采样率:8000、16000、24000
+1
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@@ -130,6 +130,7 @@ class ConnectionHandler:
# 所以涉及到ASR的变量,需要在这里定义,属于connection的私有变量
self.asr_audio = []
self.asr_audio_queue = queue.Queue()
self.current_speaker = None # 存储当前说话人
self.current_language_tag = None # 存储当前ASR识别的语言标签
# llm相关变量
@@ -1,4 +1,3 @@
import re
import time
import json
import asyncio
@@ -41,33 +40,8 @@ async def resume_vad_detection(conn):
async def startToChat(conn, text):
# 检查输入是否是JSON格式(包含说话人信息)
speaker_name = None
actual_text = text
language_tag = None
# 检查当前使用的ASR是否为FunASR(本地或服务版本)
is_funasr = False
if hasattr(conn, 'asr') and conn.asr:
asr_module = conn.asr.__class__.__module__
if 'fun_local' in asr_module or 'fun_server' in asr_module:
is_funasr = True
conn.logger.bind(tag=TAG).debug(f"检测到FunASR语音识别: {asr_module}")
# 只有在使用FunASR时才处理语言标签
if is_funasr:
# 检查是否包含语言标签(如<|zh|>、<|en|>等)
lang_pattern = r'<\|([a-z]{2,3})\|>'
lang_match = re.search(lang_pattern, text)
if lang_match:
language_tag = lang_match.group(1)
conn.current_language_tag = language_tag
conn.logger.bind(tag=TAG).info(f"检测到FunASR语言标签: {language_tag}")
# 移除语言标签,保留纯文本内容
actual_text = re.sub(lang_pattern, '', text).strip()
conn.logger.bind(tag=TAG).debug(f"移除语言标签后的文本: {actual_text}")
else:
# 没有检测到语言标签时,清空之前的标签
conn.current_language_tag = None
actual_text = text
try:
# 尝试解析JSON格式的输入
@@ -75,6 +49,7 @@ async def startToChat(conn, text):
data = json.loads(text)
if "speaker" in data and "content" in data:
speaker_name = data["speaker"]
language_tag = data["language"]
actual_text = data["content"]
conn.logger.bind(tag=TAG).info(f"解析到说话人信息: {speaker_name}")
@@ -89,10 +64,11 @@ async def startToChat(conn, text):
conn.current_speaker = speaker_name
else:
conn.current_speaker = None
# 如果不是FunASR,清空语言标签,不影响其他ASR
if not is_funasr:
conn.current_language_tag = None
# 保存语种信息到连接对象
if language_tag:
conn.current_language_tag = language_tag
else:
conn.current_language_tag = "zh"
if conn.need_bind:
await check_bind_device(conn)
+31 -9
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@@ -118,23 +118,45 @@ class ASRProviderBase(ABC):
else:
speaker_name = voiceprint_result
if raw_text:
logger.bind(tag=TAG).info(f"识别文本: {raw_text}")
if speaker_name:
logger.bind(tag=TAG).info(f"识别说话人: {speaker_name}")
# 判断 ASR 结果类型
if isinstance(raw_text, dict):
# FunASR 返回的 dict 格式
if speaker_name:
raw_text["speaker"] = speaker_name
# 记录识别结果
if raw_text.get("language"):
logger.bind(tag=TAG).info(f"识别语言: {raw_text['language']}")
if raw_text.get("emotion"):
logger.bind(tag=TAG).info(f"识别情绪: {raw_text['emotion']}")
if raw_text.get("content"):
logger.bind(tag=TAG).info(f"识别文本: {raw_text['content']}")
if speaker_name:
logger.bind(tag=TAG).info(f"识别说话人: {speaker_name}")
# 转换为 JSON 字符串用于下游
enhanced_text = json.dumps(raw_text, ensure_ascii=False)
content_for_length_check = raw_text.get("content", "")
else:
# 其他 ASR 返回的纯文本
if raw_text:
logger.bind(tag=TAG).info(f"识别文本: {raw_text}")
if speaker_name:
logger.bind(tag=TAG).info(f"识别说话人: {speaker_name}")
# 构建包含说话人信息的JSON字符串
enhanced_text = self._build_enhanced_text(raw_text, speaker_name)
content_for_length_check = raw_text
# 性能监控
total_time = time.monotonic() - total_start_time
logger.bind(tag=TAG).debug(f"总处理耗时: {total_time:.3f}s")
# 检查文本长度
text_len, _ = remove_punctuation_and_length(raw_text)
text_len, _ = remove_punctuation_and_length(content_for_length_check)
self.stop_ws_connection()
if text_len > 0:
# 构建包含说话人信息的JSON字符串
enhanced_text = self._build_enhanced_text(raw_text, speaker_name)
# 使用自定义模块进行上报
await startToChat(conn, enhanced_text)
enqueue_asr_report(conn, enhanced_text, asr_audio_task)
@@ -145,7 +167,7 @@ class ASRProviderBase(ABC):
logger.bind(tag=TAG).debug(f"异常详情: {traceback.format_exc()}")
def _build_enhanced_text(self, text: str, speaker_name: Optional[str]) -> str:
"""构建包含说话人信息的文本"""
"""构建包含说话人信息的文本(仅用于纯文本ASR"""
if speaker_name and speaker_name.strip():
return json.dumps({
"speaker": speaker_name,
@@ -6,11 +6,10 @@ import shutil
import psutil
import asyncio
from funasr import AutoModel
from config.logger import setup_logging
from typing import Optional, Tuple, List
from core.providers.asr.base import ASRProviderBase
from core.providers.asr.utils import lang_tag_filter
from funasr import AutoModel
from core.providers.asr.base import ASRProviderBase
from core.providers.asr.dto.dto import InterfaceType
@@ -103,11 +102,9 @@ class ASRProvider(ASRProviderBase):
use_itn=True,
batch_size_s=60,
)
# text = await asyncio.to_thread(rich_transcription_postprocess, result[0]["text"])
# 使用lang_tag_filter处理识别结果
text = await asyncio.to_thread(lang_tag_filter, result[0]["text"])
text = lang_tag_filter(result[0]["text"])
logger.bind(tag=TAG).debug(
f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}"
f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text['content']}"
)
return text, file_path
@@ -1,12 +1,13 @@
import ssl
import json
import asyncio
import websockets
from config.logger import setup_logging
from typing import Optional, Tuple, List
from core.providers.asr.base import ASRProviderBase
from core.providers.asr.utils import lang_tag_filter
from core.providers.asr.dto.dto import InterfaceType
import ssl
import json
import websockets
from config.logger import setup_logging
import asyncio
TAG = __name__
logger = setup_logging()
@@ -156,7 +157,7 @@ class ASRProvider(ASRProviderBase):
# if match:
# result = match.group(4).strip()
# Handle language tags
# Handle tags
result = lang_tag_filter(result)
return (
result,
+63 -27
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@@ -4,40 +4,76 @@ from config.logger import setup_logging
TAG = __name__
logger = setup_logging()
EMOTION_EMOJI_MAP = {
"HAPPY": "🙂",
"SAD": "😔",
"ANGRY": "😡",
"NEUTRAL": "😶",
"FEARFUL": "😰",
"DISGUSTED": "🤢",
"SURPRISED": "😲",
"EMO_UNKNOWN": "😶", # 未知情绪默认用中性表情
}
# EVENT_EMOJI_MAP = {
# "<|BGM|>": "🎼",
# "<|Speech|>": "",
# "<|Applause|>": "👏",
# "<|Laughter|>": "😀",
# "<|Cry|>": "😭",
# "<|Sneeze|>": "🤧",
# "<|Breath|>": "",
# "<|Cough|>": "🤧",
# }
def lang_tag_filter(text):
def lang_tag_filter(text: str) -> dict | str:
"""
过滤函数:只保留语言标签,移除其他所有标签
用于FunASR识别结果的处理,保留语言标签(如<|zh|>、<|en|>等),
但移除其他所有格式的标签(如时间戳、情感标签等)
解析 FunASR 识别结果,按顺序提取标签和纯文本内容
Args:
text: ASR识别的原始文本,可能包含多种标签
text: ASR 识别的原始文本,可能包含多种标签
Returns:
str: 处理后的文本,只保留语言标签(如果存在)
dict: {"language": "zh", "emotion": "SAD", "emoji": "😔", "content": "你好"} 如果有标签
str: 纯文本,如果没有标签
Examples:
>>> lang_tag_filter("<|zh|><|emotion:happy|>你好")
'<|zh|>你好'
>>> lang_tag_filter("<|en|>hello world")
'<|en|>hello world'
FunASR 输出格式:<|语种|><|情绪|><|事件|><|其他选项|>原文
>>> lang_tag_filter("<|zh|><|SAD|><|Speech|><|withitn|>你好啊,测试测试。")
{"language": "zh", "emotion": "SAD", "emoji": "😔", "content": "你好啊,测试测试。"}
>>> lang_tag_filter("<|en|><|HAPPY|><|Speech|><|withitn|>Hello hello.")
{"language": "en", "emotion": "HAPPY", "emoji": "🙂", "content": "Hello hello."}
>>> lang_tag_filter("plain text")
"plain text"
"""
# 定义语言标签模式
lang_pattern = r"<\|(zh|en|yue|ja|ko|nospeech)\|>"
lang_tags = re.findall(lang_pattern, text)
# 提取所有标签(按顺序)
tag_pattern = r"<\|([^|]+)\|>"
all_tags = re.findall(tag_pattern, text)
# 移除所有 < | ... | > 格式的标签
clean_text = re.sub(r"<\|.*?\|>", "", text)
# 移除所有 <|...|> 格式的标签,获取纯文本
clean_text = re.sub(tag_pattern, "", text).strip()
# 在开头添加语言标签(如果存在)
if lang_tags:
if len(lang_tags) > 1:
logger.bind(tag=TAG).warning(
f"检测到多个语言标签: {lang_tags},仅使用第一个: {lang_tags[0]}"
)
clean_text = f"<|{lang_tags[0]}|>{clean_text}"
# 如果没有标签,直接返回纯文本
if not all_tags:
return clean_text
return clean_text.strip()
# 按照 FunASR 的固定顺序提取标签,返回 dict
language = all_tags[0] if len(all_tags) > 0 else "zh"
emotion = all_tags[1] if len(all_tags) > 1 else "NEUTRAL"
# event = all_tags[2] if len(all_tags) > 2 else "Speech" # 事件标签暂不使用
result = {
"content": clean_text,
"language": language,
"emotion": emotion,
# "event": event,
}
# 添加 emoji 映射
if emotion in EMOTION_EMOJI_MAP:
result["emotion"] = EMOTION_EMOJI_MAP[emotion]
# 事件标签暂不使用
# if event in EVENT_EMOJI_MAP:
# result["event"] = EVENT_EMOJI_MAP[event]
return result
@@ -39,13 +39,6 @@ class TTSProvider(TTSProviderBase):
if config.get("private_voice"):
self.voice = config.get("private_voice")
# 多语言音色配置
self.voice_zh = config.get("voice_zh", self.voice) # 中文音色
self.voice_yue = config.get("voice_yue", self.voice) # 粤语音色
self.voice_en = config.get("voice_en", self.voice) # 英语音色
self.voice_ja = config.get("voice_ja", self.voice) # 日语音色
self.voice_ko = config.get("voice_ko", self.voice) # 韩语音色
# 音频参数配置
self.format = config.get("format", "pcm")
sample_rate = config.get("sample_rate", "24000")
@@ -72,36 +65,6 @@ class TTSProvider(TTSProviderBase):
sample_rate=self.sample_rate, channels=1, frame_size_ms=60
)
def get_voice_by_language(self, language_tag):
"""根据语言标签返回对应的音色(仅在FunASR语音识别时生效)"""
if not language_tag:
return self.voice
# 检查当前ASR是否为FunASR
is_funasr = False
if hasattr(self, 'conn') and self.conn and hasattr(self.conn, 'asr') and self.conn.asr:
asr_module = self.conn.asr.__class__.__module__
if 'fun_local' in asr_module or 'fun_server' in asr_module:
is_funasr = True
# 只有在使用FunASR时才应用多语言音色选择
if is_funasr:
language_tag = language_tag.lower()
voice_map = {
'zh': self.voice_zh,
'yue': self.voice_yue,
'en': self.voice_en,
'ja': self.voice_ja,
'ko': self.voice_ko
}
selected_voice = voice_map.get(language_tag, self.voice)
logger.bind(tag=TAG).info(f"FunASR语言标签 '{language_tag}' 选择音色: {selected_voice}")
return selected_voice
else:
# 非FunASR时使用默认音色
return self.voice
async def _ensure_connection(self):
"""确保WebSocket连接可用,支持60秒内连接复用"""
try:
@@ -265,9 +228,6 @@ class TTSProvider(TTSProviderBase):
# 启动监听任务
self._monitor_task = asyncio.create_task(self._start_monitor_tts_response())
# 根据当前语言标签选择音色
current_voice = self.get_voice_by_language(getattr(self.conn, 'current_language_tag', None))
# 发送run-task消息启动会话
run_task_message = {
"header": {
@@ -282,7 +242,7 @@ class TTSProvider(TTSProviderBase):
"model": self.model,
"parameters": {
"text_type": "PlainText",
"voice": current_voice,
"voice": self.voice,
"format": self.format,
"sample_rate": self.sample_rate,
"volume": self.volume,
@@ -452,12 +412,6 @@ class TTSProvider(TTSProviderBase):
)
try:
# 选择音色:优先使用当前连接的语言标签,否则使用默认音色
if hasattr(self, 'conn') and self.conn and hasattr(self.conn, 'current_language_tag'):
current_voice = self.get_voice_by_language(self.conn.current_language_tag)
else:
current_voice = self.voice
# 发送run-task消息启动会话
run_task_message = {
"header": {
@@ -472,7 +426,7 @@ class TTSProvider(TTSProviderBase):
"model": self.model,
"parameters": {
"text_type": "PlainText",
"voice": current_voice,
"voice": self.voice,
"format": self.format,
"sample_rate": self.sample_rate,
"volume": self.volume,
@@ -565,4 +519,4 @@ class TTSProvider(TTSProviderBase):
except Exception as e:
logger.bind(tag=TAG).error(f"生成音频数据失败: {str(e)}")
return []
return []
-23
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@@ -15,29 +15,6 @@ from pydub import AudioSegment
from typing import Callable, Any
TAG = __name__
emoji_map = {
"neutral": "😶",
"happy": "🙂",
"laughing": "😆",
"funny": "😂",
"sad": "😔",
"angry": "😠",
"crying": "😭",
"loving": "😍",
"embarrassed": "😳",
"surprised": "😲",
"shocked": "😱",
"thinking": "🤔",
"winking": "😉",
"cool": "😎",
"relaxed": "😌",
"delicious": "🤤",
"kissy": "😘",
"confident": "😏",
"sleepy": "😴",
"silly": "😜",
"confused": "🙄",
}
def get_local_ip():