添加EmotionRecognition模型注释

README .md

@@ -1,48 +0,0 @@
-# 部署步骤
-
-1. 将该仓库clone到本地
-2. 创建虚拟环境并启动
-
-``` shell
-conda create -n takway python=3.9
-conda activate takway
-```
-
-3. cd进入仓库目录下，安装依赖
-
-``` shell
-cd ~/TakwayDisplayPlatform
-pip install -r requirements.txt
-```
-
-4. 在./utils/目录下创建vits_model文件夹
-
-   从[链接](https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai/tree/main/model)下载vits_model并放入该文件夹下，只需下载config.json和G_953000.pth即可
-
-5. 在./utils/bert_vits2/目录下创建bert，slm文件夹
-
-​	从[链接1](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)，[链接2](https://huggingface.co/microsoft/deberta-v3-large)，[链接3](https://huggingface.co/ku-nlp/deberta-v2-large-japanese-char-wwm)下载模型，放入bert目录下
-
-​	从[链接4](https://huggingface.co/microsoft/wavlm-base-plus)下载模型，放入slm目录下
-
-​	在./utils/bert_vits2/bert目录下创建bert_models.json文件，填入如下内容
-
-``` json
-{
-    "deberta-v2-large-japanese-char-wwm": {
-        "repo_id": "ku-nlp/deberta-v2-large-japanese-char-wwm",
-        "files": ["pytorch_model.bin"]
-    },
-    "chinese-roberta-wwm-ext-large": {
-        "repo_id": "hfl/chinese-roberta-wwm-ext-large",
-        "files": ["pytorch_model.bin"]
-    },
-    "deberta-v3-large": {
-        "repo_id": "microsoft/deberta-v3-large",
-        "files": ["spm.model", "pytorch_model.bin"]
-    }
-}
-```
-
-6. 在 ./utils/bert_vits2/data/mix/目录下创建models文件夹，并放入预训练模型250000_G.pth
-7. 回到根目录，`python main.py`启动程序

README.md

@@ -0,0 +1,23 @@
+# 部署步骤
+
+1. 将该仓库clone到本地
+2. 创建虚拟环境并启动
+
+``` shell
+conda create -n takway python=3.9
+conda activate takway
+```
+
+3. cd进入仓库目录下，安装依赖
+
+``` shell
+cd ~/TakwayDisplayPlatform
+pip install -r requirements.txt
+```
+
+4. 在utils/目录下创建vits_model文件夹
+
+   从[链接](https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai/tree/main/model)下载vits_model并放入该文件夹下，只需下载config.json和G_953000.pth即可
+
+5. 回到仓库根目录下运行`python main.py`启动程序
+

app/concrete.py

@@ -6,8 +6,7 @@ from .abstract import *
 from .public import *
 from .exception import *
 from .dependency import get_logger
-from utils.vits_utils import TextToSpeech as VITS_TextToSpeech
-from utils.bert_vits2_utils import TextToSpeech as BertVits_TextToSpeech
+from utils.vits_utils import TextToSpeech
 from config import Config
 import threading
 import requests
@@ -18,11 +17,7 @@ import time
 import json
 
 # ----------- 初始化vits ----------- #
-vits = VITS_TextToSpeech()
-# ---------------------------------- #
-
-# -------- 初始化bert-vits --------- #
-bert_vits = BertVits_TextToSpeech()
+vits = TextToSpeech()
 # ---------------------------------- #
 
 # ---------- 初始化logger ---------- #
@@ -299,14 +294,6 @@ class VITS_TTS(TTS):
     def synthetize(self, assistant, text):
         tts_info = json.loads(assistant.tts_info)
         return vits.synthesize(text, tts_info)
-
-class BertVits_TTS(TTS):
-    def __init__(self):
-        pass
-    
-    def synthetize(self, assistant, text):
-        tts_info = json.loads(assistant.tts_info)
-        return bert_vits.synthesize(text, tts_info)
 # --------------------------------- #
 
 
@@ -332,8 +319,6 @@ class TTSFactory:
     def create_tts(self,tts_type:str) -> TTS:
         if tts_type == 'VITS':
             return VITS_TTS()
-        if tts_type == 'BertVits':
-            return BertVits_TTS()
 # --------------------------------- #
 
 
@@ -435,12 +420,7 @@ class Agent():
         self.tts_audio_service_chain.add_service(TTSAudioRecordService())
     
     def init_recorder(self,user_id):
-        input_sr = 16000
-        if isinstance(self.tts, BertVits_TTS):
-            output_sr = 44100
-        elif isinstance(self.tts, VITS_TTS):
-            output_sr = 22050
-        self.recorder = Recorder(user_id,input_sr,output_sr)
+        self.recorder = Recorder(user_id)
     
     # 对用户输入的音频进行预处理
     def user_audio_process(self, audio):

app/public.py

@@ -30,12 +30,12 @@ class SentenceSegmentation():
         return self.__sentenceSegmentation(llm_chunk)
 
 class Recorder:
-    def __init__(self, user_id, input_sr, output_sr):
+    def __init__(self, user_id):
         self.input_wav_path = 'storage/wav/'+ datetime.now().strftime('%Y%m%d%H%M%S') + 'U' + user_id + 'i.wav'
         self.output_wav_path = 'storage/wav/'+ datetime.now().strftime('%Y%m%d%H%M%S') + 'U' + user_id + 'o.wav'
         self.out_put_text_path = 'storage/record/'+ datetime.now().strftime('%Y%m%d%H%M%S') + 'U' + user_id + 'o.txt'
-        self.input_sr = input_sr
-        self.output_sr = output_sr
+        self.input_sr = 16000
+        self.output_sr = 22050
         self.user_audio = b''
         self.tts_audio = b''
         self.input_text = ""

app/schemas.py

@@ -33,13 +33,8 @@ class update_assistant_deatil_params_request(BaseModel):
     platform:str
     model :str
     temperature :float
-    tts_engine:str
     speaker_id:int
     length_scale:float
-    language:str
-    style_text:str
-    style_weight:float
-    
     
 class update_assistant_max_tokens_request(BaseModel):
     max_tokens:int

config.py

@@ -1,5 +1,8 @@
 class Config:
     SQLITE_URL = 'sqlite:///takway.db'
+    ASR = "XF" #在此处选择语音识别引擎
+    LLM = "MINIMAX" #在此处选择大模型
+    TTS = "VITS" #在此处选择语音合成引擎
     LOG_LEVEL = "DEBUG"
     class UVICORN:
         HOST = '0.0.0.0'

main.py

@@ -122,16 +122,7 @@ async def update_assistant_deatil_params(id: str,request: update_assistant_deati
         llm_info['temperature'] = request.temperature
         tts_info['speaker_id'] = request.speaker_id
         tts_info['length_scale'] = request.length_scale
-        tts_info['language'] = request.language
-        tts_info['style_text'] = request.style_text
-        tts_info['style_weight'] = request.style_weight
-        tts_info['sdp_ratio'] = 0.5
-        tts_info['opt_cut_by_send'] = False
-        tts_info['interval_between_para'] = 1.0
-        tts_info['interval_between_sent'] = 0.2
-        tts_info['en_ratio'] = 1.0
         user_info['llm_type'] = request.platform
-        user_info['tts_type'] = request.tts_engine
         assistant.llm_info = json.dumps(llm_info, ensure_ascii=False)
         assistant.tts_info = json.dumps(tts_info, ensure_ascii=False)
         assistant.user_info = json.dumps(user_info, ensure_ascii=False)
@@ -236,7 +227,15 @@ async def streaming_chat(ws: WebSocket):
         agent.recorder.input_text = prompt
         logger.debug("开始调用大模型")
         llm_frames = await agent.chat(assistant, prompt)
+        
+        start_time = time.time()
+        is_first_response = True
+        
         for llm_frame in llm_frames:
+            if is_first_response:
+                end_time = time.time()
+                logger.debug(f"第一帧返回耗时：{round(end_time-start_time,3)}s")
+                is_first_response = False
             resp_msgs = agent.llm_msg_process(llm_frame)
             for resp_msg in resp_msgs: 
                 llm_text += resp_msg

mqtt/publish_audio.py

@@ -1,23 +0,0 @@
-import paho.mqtt.client as mqtt
-
-# MQTT Broker信息
-broker = '127.0.0.1'
-port = 1883
-topic = 'audio/test'
-
-# 音频文件路径
-audio_file_path = 'tmp2.wav'
-
-def publish_audio():
-    client = mqtt.Client()
-    client.connect(broker, port)
-
-    with open(audio_file_path, 'rb') as audio_file:
-        audio_data = audio_file.read()
-
-    client.publish(topic, audio_data)
-    client.disconnect()
-
-if __name__ == "__main__":
-    publish_audio()
-    print("Audio published successfully.")

mqtt/publish_audio_multithread.py

@@ -1,33 +0,0 @@
-import paho.mqtt.client as mqtt
-import threading
-import time
-
-# MQTT Broker信息
-broker = '127.0.0.1'
-port = 1883
-topic = 'audio/test'
-
-# 音频文件路径列表
-audio_file_paths = ['tmp2.wav', 'tmp3.wav', 'tmp4.wav']  # 添加多个音频文件路径
-
-def publish_audio(file_path):
-    client = mqtt.Client()
-    client.connect(broker, port)
-
-    with open(file_path, 'rb') as audio_file:
-        audio_data = audio_file.read()
-
-    client.publish(topic, audio_data)
-    client.disconnect()
-    print(f"Audio from {file_path} published successfully.")
-
-if __name__ == "__main__":
-    threads = []
-    for file_path in audio_file_paths:
-        thread = threading.Thread(target=publish_audio, args=(file_path,))
-        thread.start()
-        threads.append(thread)
-        time.sleep(1)  # 可选：给每个线程一些间隔时间
-
-    for thread in threads:
-        thread.join()

mqtt/subscribe_audio.py

@@ -1,33 +0,0 @@
-import paho.mqtt.client as mqtt
-from pydub import AudioSegment
-import io
-
-# MQTT Broker信息
-broker = '127.0.0.1'
-port = 1883
-topic = 'audio/test'
-output_file_path = 'received_audio.wav'
-
-def on_connect(client, userdata, flags, rc):
-    print("Connected with result code " + str(rc))
-    client.subscribe(topic)
-
-def on_message(client, userdata, msg):
-    print("Audio received")
-    audio_data = msg.payload
-    audio = AudioSegment.from_file(io.BytesIO(audio_data), format="wav")
-    # 将音频保存为文件
-    with open(output_file_path, 'wb') as f:
-        f.write(audio_data)
-    print(f"Audio saved as {output_file_path}")
-
-def subscribe_audio():
-    client = mqtt.Client()
-    client.on_connect = on_connect
-    client.on_message = on_message
-
-    client.connect(broker, port)
-    client.loop_forever()
-
-if __name__ == "__main__":
-    subscribe_audio()

mqtt/subscribe_audio_multithread.py

@@ -1,42 +0,0 @@
-import paho.mqtt.client as mqtt
-from pydub import AudioSegment
-import io
-import threading
-
-# MQTT Broker信息
-broker = '127.0.0.1'
-port = 1883
-topic = 'audio/test'
-output_file_path = 'received_audio.wav'
-
-def on_connect(client, userdata, flags, rc):
-    print("Connected with result code " + str(rc))
-    client.subscribe(topic)
-
-def handle_audio(audio_data):
-    # 将音频保存为文件
-    with open(output_file_path, 'wb') as f:
-        f.write(audio_data)
-    print(f"Audio saved as {output_file_path}")
-
-def on_message(client, userdata, msg):
-    print("Audio received")
-    audio_data = msg.payload
-    # 使用线程来处理音频
-    audio_thread = threading.Thread(target=handle_audio, args=(audio_data,))
-    audio_thread.start()
-
-def subscribe_audio():
-    client = mqtt.Client()
-    client.on_connect = on_connect
-    client.on_message = on_message
-
-    client.connect(broker, port)
-    client.loop_forever()
-
-def mqtt_thread():
-    subscribe_audio()
-
-if __name__ == "__main__":
-    mqtt_thread = threading.Thread(target=mqtt_thread)
-    mqtt_thread.start()

requirements.txt

@@ -14,4 +14,3 @@ librosa
 aiohttp
 'volcengine-python-sdk[ark]'
 zhipuai
-pyopenjtalk

test.py

@@ -0,0 +1,13 @@
+from utils.bert_vits2_utils import TextToSpeech
+import soundfile as sf
+tts = TextToSpeech()
+tts.print_speakers_info()
+
+audio, sample_rate= tts.synthesize("你好，我好开心",        # 文本
+                                   0,                      # 说话人 id
+                                   style_text="我很难过！！！！呜呜呜！！！",   # 情绪prompt，当language=="ZH" 才有效 
+                                   style_weight=0.9,        # 情绪prompt权重
+                                   language="mix",          # 语言类型，包括 "ZH" "EN" "mix"
+                                   en_ratio=1.)             # mix语言类型下，英文文本速度，越大速度越慢
+save_path = "./tmp2.wav"
+sf.write(save_path, audio, sample_rate)

utils/bert_vits2_utils.py

@@ -395,28 +395,29 @@ class TextToSpeech:
     
     def synthesize(self,
                    text,
-                   tts_info, 
+                   speaker_idx=0,       # self.speakers 的 index，指定说话
+                   sdp_ratio=0.5,
+                   noise_scale=0.6,
+                   noise_scale_w=0.9,
+                   length_scale=1.0,    # 越大语速越慢
+                   language="mix",      # ["ZH", "EN", "mix"] 三选一
+                   opt_cut_by_send=False, # 按句切分    在按段落切分的基础上再按句子切分文本
+                   interval_between_para=1.0, # 段间停顿(秒)，需要大于句间停顿才有效
+                   interval_between_sent=0.2, # 句间停顿(秒)，勾选按句切分才生效
+                   audio_prompt=None,
+                   text_prompt="",
+                   prompt_mode="Text prompts",
+                   style_text="",        #    "使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
+                                         # "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
+                                         # "效果较不明确，留空即为不使用该功能"
+                   style_weight=0.7,     # "主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本
+                   en_ratio=1.0          # 中英混合时，英文速度控制，越大英文速度越慢 
                    ):
         """
         return: audio, sample_rate
         """
-        speaker_id = tts_info['speaker_id'] # self.speakers 的 index，指定说话
-        sdp_ratio = tts_info['sdp_ratio']
-        noise_scale = tts_info['noise_scale']
-        noise_scale_w = tts_info['noise_scale_w']
-        length_scale = tts_info['length_scale']
-        language = tts_info['language'] # ["ZH", "EN", "mix"] 三选一
-        opt_cut_by_send = tts_info['opt_cut_by_send'] 
-        interval_between_para = tts_info['interval_between_para'] # 段间停顿(秒)，需要大于句间停顿才有效
-        interval_between_sent = tts_info['interval_between_sent'] # 句间停顿(秒)，勾选按句切分才生效
-        audio_prompt = None
-        text_prompt = ""
-        prompt_mode = "Text prompts"
-        style_text = tts_info['style_text']
-        style_weight = tts_info['style_weight']
-        en_ratio = tts_info['en_ratio']
         
-        speaker = self.speakers[speaker_id]
+        speaker = self.speakers[speaker_idx]
 
         if language == "mix":
             language, text = self.format_utils(text, speaker)
@@ -454,17 +455,9 @@ class TextToSpeech:
                 style_weight
             )
         
-        return self.convert_numpy_to_bytes(audio_output[1])
+        # return text_output, audio_output
+        return audio_output[1], audio_output[0]
     
     def print_speakers_info(self):
         for i, speaker in enumerate(self.speakers):
             print(f"id: {i}, speaker: {speaker}")
-            
-    def convert_numpy_to_bytes(self, audio_data):
-        if isinstance(audio_data, np.ndarray):
-            if audio_data.dtype == np.dtype('float32'):
-                audio_data = np.int16(audio_data * np.iinfo(np.int16).max)
-            audio_data = audio_data.tobytes()
-            return audio_data
-        else:
-            raise TypeError("audio_data must be a numpy array")

utils/emotion2vec_utils.py

@@ -0,0 +1,54 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import json
+
+model_args = {
+    "task": Tasks.emotion_recognition,
+    "model": "iic/emotion2vec_base_finetuned"    # Alternative: iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large and iic/emotion2vec_base_finetuned
+    # "device": 不用指定，"device" 默认为gpu
+}
+
+class EmotionRecognition:
+"""
+    9-class emotion
+    0: angry
+    1: disgusted
+    2: fearful
+    3: happy
+    4: neutral
+    5: other
+    6: sad
+    7: surprised
+    8: <unk>        指unknown
+    return :  
+    [{"emotion": "angry", "weight": },
+     {"emotion": "disgusted", "weight": },
+     ...
+    ]
+"""
+    def __init__(self) -> None:
+        self.initialize(model_args=model_args)
+
+    # 初始化模型
+    def initialize(self, model_args=model_args):
+        self.inference_pipeline = pipeline(**model_args) 
+
+    def emotion_recognition(self, 
+                            audio:bytes,
+                            granularity="utterance", # 中间特征的维度，"utterance": [*768], "frame": [T*768]
+                            extract_embedding=False, # 是否保留提取到的特征，False表示不保留中间特征，只保留最终结果
+                            output_dir="./outputs"  # 中间特征的保存位置(extract_embedding为true时有效)
+                            ):
+        rec_result = self.inference_pipeline(audio, granularity=granularity, extract_embedding=extract_embedding, output_dir=output_dir)
+
+        # 保存结果
+        json_list = []
+        for emotion, score in zip(rec_result[0]["labels"], rec_result[0]["scores"]):
+            json_list.append({"emotion": emotion.split("/")[-1], "weight": round(score,4)})
+        recognize_result = json.dumps(json_list)
+        return recognize_result
+
+if __name__ == "__main__":
+    model = EmotionRecognition()
+    recognize_result = model.emotion_recognition("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+    print(recognize_result)