Delete app.py

app.py

@@ -1,251 +0,0 @@
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-from text import text_to_sequence
-import numpy as np
-from scipy.io import wavfile
-import torch
-import json
-import commons
-import utils
-import sys
-import pathlib
-import onnxruntime as ort
-import gradio as gr
-import argparse
-import time
-import os
-import io
-from scipy.io.wavfile import write
-from flask import Flask, request
-from threading import Thread
-import openai
-import requests
-class VitsGradio:
-    def __init__(self):
-        self.lan = ["中文","日文","自动"]
-        self.chatapi = ["gpt-3.5-turbo","gpt3"]
-        self.modelPaths = []
-        for root,dirs,files in os.walk("checkpoints"):
-            for dir in dirs:
-                self.modelPaths.append(dir)
-        with gr.Blocks() as self.Vits:
-            with gr.Tab("调试用"):
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Row():
-                            with gr.Column():
-                                self.text = gr.TextArea(label="Text", value="你好")
-                                with gr.Accordion(label="测试api", open=False):
-                                    self.local_chat1 = gr.Checkbox(value=False, label="使用网址+文本进行模拟")
-                                    self.url_input = gr.TextArea(label="键入测试", value="http://127.0.0.1:8080/chat?Text=")
-                                    butto = gr.Button("测试从网页端获取文本")
-                                btnVC = gr.Button("测试tts+对话程序")
-                            with gr.Column():
-                                output2 = gr.TextArea(label="回复")
-                                output1 = gr.Audio(label="采样率22050")
-                                output3 = gr.outputs.File(label="44100hz: output.wav")
-                butto.click(self.Simul, inputs=[self.text, self.url_input], outputs=[output2,output3])
-                btnVC.click(self.tts_fn, inputs=[self.text], outputs=[output1,output2])
-            with gr.Tab("控制面板"):
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Row():
-                            with gr.Column():
-                                self.api_input1 = gr.TextArea(label="输入api-key或ChATGLM模型的路径", value="https://platform.openai.com/account/api-keys")
-                                with gr.Accordion(label="chatbot选择", open=False):
-                                                self.api_input2 = gr.Checkbox(value=True, label="采用gpt3.5")
-                                                self.local_chat1 = gr.Checkbox(value=False, label="启动本地chatbot")
-                                                self.local_chat2 = gr.Checkbox(value=True, label="是否量化")
-                                                res = gr.TextArea()
-                                Botselection = gr.Button("聊天机器人选择")
-                                Botselection.click(self.check_bot, inputs=[self.api_input1,self.api_input2,self.local_chat1,self.local_chat2], outputs = [res])
-                                self.input1 = gr.Dropdown(label = "vits模型加载", choices = self.modelPaths, value = self.modelPaths[0], type = "value")
-                                self.input2 = gr.Dropdown(label="Language", choices=self.lan, value="自动", interactive=True)
-                            with gr.Column():
-                                btnVC = gr.Button("Submit")
-                                self.input3 = gr.Dropdown(label="Speaker", choices=list(range(101)), value=0, interactive=True)
-                                self.input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.267)
-                                self.input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.7)
-                                self.input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1)
-                                statusa = gr.TextArea()
-                btnVC.click(self.create_tts_fn, inputs=[self.input1, self.input2, self.input3, self.input4, self.input5, self.input6], outputs = [statusa])
-
-    def Simul(self,text,url_input):
-        web = url_input + text
-        res = requests.get(web)
-        music = res.content
-        with open('output.wav', 'wb') as code:
-            code.write(music)
-        file_path = "output.wav"
-        return web,file_path
-
-
-    def chatgpt(self,text):
-        self.messages.append({"role": "user", "content": text},)
-        chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages= self.messages)
-        reply = chat.choices[0].message.content
-        return reply
-    
-    def ChATGLM(self,text):
-        if text == 'clear':
-            self.history = []
-        response, new_history = self.model.chat(self.tokenizer, text, self.history)
-        response = response.replace(" ",'').replace("\n",'.')
-        self.history = new_history
-        return response
-    
-    def gpt3_chat(self,text):
-        call_name = "Waifu"
-        openai.api_key = args.key
-        identity = ""
-        start_sequence = '\n'+str(call_name)+':'
-        restart_sequence = "\nYou: "
-        if 1 == 1:
-            prompt0 = text #当期prompt
-        if text == 'quit':
-            return prompt0
-        prompt = identity + prompt0 + start_sequence
-        response = openai.Completion.create(
-            model="text-davinci-003",
-            prompt=prompt,
-            temperature=0.5,
-            max_tokens=1000,
-            top_p=1.0,
-            frequency_penalty=0.5,
-            presence_penalty=0.0,
-            stop=["\nYou:"]
-        )
-        return response['choices'][0]['text'].strip()
-    
-    def check_bot(self,api_input1,api_input2,local_chat1,local_chat2):
-        if local_chat1:
-            from transformers import AutoTokenizer, AutoModel
-            self.tokenizer = AutoTokenizer.from_pretrained(api_input1, trust_remote_code=True)
-            if local_chat2:
-                self.model = AutoModel.from_pretrained(api_input1, trust_remote_code=True).half().quantize(4).cuda()
-            else:
-                self.model = AutoModel.from_pretrained(api_input1, trust_remote_code=True)
-            self.history = []
-        else:
-            self.messages = []
-            openai.api_key = api_input1
-        return "Finished"
-    
-    def is_japanese(self,string):
-        for ch in string:
-            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
-                return True
-        return False
-    
-    def is_english(self,string):
-        import re
-        pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$')
-        if pattern.fullmatch(string):
-            return True
-        else:
-            return False
-
-    def get_symbols_from_json(self,path):
-        assert os.path.isfile(path)
-        with open(path, 'r') as f:
-            data = json.load(f)
-        return data['symbols']
-
-    def sle(self,language,text):
-        text = text.replace('\n','。').replace(' ',',')
-        if language == "中文":
-            tts_input1 = "[ZH]" + text + "[ZH]"
-            return tts_input1
-        elif language == "自动":
-            tts_input1 = f"[JA]{text}[JA]" if self.is_japanese(text) else f"[ZH]{text}[ZH]"
-            return tts_input1
-        elif language == "日文":
-            tts_input1 = "[JA]" + text + "[JA]"
-            return tts_input1
-
-    def get_text(self,text,hps_ms):
-        text_norm = text_to_sequence(text,hps_ms.data.text_cleaners)
-        if hps_ms.data.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = torch.LongTensor(text_norm)
-        return text_norm
-
-    def create_tts_fn(self,path, input2, input3, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
-        self.symbols = self.get_symbols_from_json(f"checkpoints/{path}/config.json")
-        self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
-        phone_dict = {
-                symbol: i for i, symbol in enumerate(self.symbols)
-            }
-        self.ort_sess = ort.InferenceSession(f"checkpoints/{path}/model.onnx")
-        self.language = input2
-        self.speaker_id = input3
-        self.n_scale = n_scale
-        self.n_scale_w = n_scale_w
-        self.l_scale = l_scale
-        print(self.language,self.speaker_id,self.n_scale)
-        return 'success'
-    
-    def tts_fn(self,text):
-        if self.local_chat1:
-            text = self.chatgpt(text)
-        elif self.api_input2:
-            text = self.ChATGLM(text)
-        else:
-            text = self.gpt3_chat(text)
-        print(text)
-        text =self.sle(self.language,text)
-        seq = text_to_sequence(text, cleaner_names=self.hps.data.text_cleaners)
-        if self.hps.data.add_blank:
-            seq = commons.intersperse(seq, 0)
-        with torch.no_grad():
-            x = np.array([seq], dtype=np.int64)
-            x_len = np.array([x.shape[1]], dtype=np.int64)
-            sid = np.array([self.speaker_id], dtype=np.int64)
-            scales = np.array([self.n_scale, self.n_scale_w, self.l_scale], dtype=np.float32)
-            scales.resize(1, 3)
-            ort_inputs = {
-                        'input': x,
-                        'input_lengths': x_len,
-                        'scales': scales,
-                        'sid': sid
-                    }
-            t1 = time.time()
-            audio = np.squeeze(self.ort_sess.run(None, ort_inputs))
-            audio *= 32767.0 / max(0.01, np.max(np.abs(audio))) * 0.6
-            audio = np.clip(audio, -32767.0, 32767.0)
-            t2 = time.time()
-            spending_time = "推理时间："+str(t2-t1)+"s" 
-            print(spending_time)
-            bytes_wav = bytes()
-            byte_io = io.BytesIO(bytes_wav)
-            wavfile.write('moe/temp1.wav',self.hps.data.sampling_rate, audio.astype(np.int16))
-            cmd = 'ffmpeg -y -i '  + 'moe/temp1.wav' + ' -ar 44100 ' + 'moe/temp2.wav'
-            os.system(cmd)    
-        return (self.hps.data.sampling_rate, audio),text.replace('[JA]','').replace('[ZH]','')
-
-app = Flask(__name__)
-print("开始部署")
-grVits = VitsGradio()
-
-@app.route('/chat')
-def text_api():
-    message = request.args.get('Text','')
-    audio,text = grVits.tts_fn(message)
-    text = text.replace('[JA]','').replace('[ZH]','')
-    with open('moe/temp2.wav','rb') as bit:
-        wav_bytes = bit.read()
-    headers = {
-            'Content-Type': 'audio/wav',
-            'Text': text.encode('utf-8')}
-    return wav_bytes, 200, headers
-
-def gradio_interface():
-    return grVits.Vits.launch()
-
-if __name__ == '__main__':
-    api_thread = Thread(target=app.run, args=("0.0.0.0", 8080))
-    gradio_thread = Thread(target=gradio_interface)
-    api_thread.start()
-    gradio_thread.start()