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import gradio as gr
# from TTS.api import TTS
import librosa
import http.client
# Python 2.x引入urllib模块。
from restful_aliyun_tts import *
import urllib.parse
from base_chat import *
import json
from training import *
from process import *
title = "文本转语音"
# embed_record() # 把历史聊天记录向量化
def generateAudio(text):
token = "e329d9b11a1d4fa68e2a212c5a7892d7"
appKey = "YdRfHrZVeusHKfv6"
# text = '我是A i Vtuber,很高兴认识你,你有什么开心的事情嘛。'
# 采用RFC 3986规范进行urlencode编码。
result = run_question(text)
# textUrlencode = text
textUrlencode = result
# Python 2.x请使用urllib.quote。
# textUrlencode = urllib.quote(textUrlencode, '')
# Python 3.x请使用urllib.parse.quote_plus。
textUrlencode = urllib.parse.quote_plus(textUrlencode)
textUrlencode = textUrlencode.replace("+", "%20")
textUrlencode = textUrlencode.replace("*", "%2A")
textUrlencode = textUrlencode.replace("%7E", "~")
# print('text: ' + textUrlencode)
audioSaveFile = 'syAudios.wav'
format = 'wav'
sampleRate = 16000
processGETRequest(appKey, token, textUrlencode, audioSaveFile, format, sampleRate)
# audio, sr = librosa.load(path="syAudios.wav")
# return sr,audio
return audioSaveFile
def generateAudio_from_file(file):
# 验证获取文件的内容 ,需要把加载文件的内容 进行学习训练，然后再进行生成
# print(file.name)
files_path = "/".join((file.name).split("/")[0:-1]) + "/"
print(files_path)
res = train(files_path)
print(res)
# with open(file.name,"r",encoding="utf-8") as f:
# content = f.read()
# print(content)
def generateAudio_wav_file(text):
token = "e329d9b11a1d4fa68e2a212c5a7892d7"
appKey = "YdRfHrZVeusHKfv6"
# print(text.name)
print(text)
res = runPrompt(text)
print(res)
textUrlencode = res
textUrlencode = urllib.parse.quote_plus(textUrlencode)
textUrlencode = textUrlencode.replace("+", "%20")
textUrlencode = textUrlencode.replace("*", "%2A")
textUrlencode = textUrlencode.replace("%7E", "~")
# print('text: ' + textUrlencode)
audioSaveFile = 'syAudios.wav'
format = 'wav'
sampleRate = 16000
processGETRequest(appKey, token, textUrlencode, audioSaveFile, format, sampleRate)
# audio, sr = librosa.load(path="syAudios.wav")
# return sr,audio
return audioSaveFile
example = [["Ai Vtuber是什么"],["where is nanjing"],["who are you"],["今天吃什么好呢"],["武汉今天天气咋样"],["what are the Annie skills"]]
app = gr.Blocks()
with app :
gr.Markdown("# 文本转语音以及根据加载的文本来回答\n\n"
"Text：根据提问的问题回答之后转语音\n\n"
"File：根据加载的文本通过训练后，按照文本来进行回答，只回答相关的问题\n\n")
with gr.Tabs():
with gr.TabItem("TTS"):
with gr.Row():
with gr.Column():
textbox = gr.Textbox(label="Text",
placeholder="Type your sentence here",
value="what is Ai Vtuber",elem_id="tts-input")
filebox = gr.File(file_count="single",
type="file",
label=None
)
with gr.Column():
# text_output = gr.Textbox(label="Message")
# 通过对话
audio_output = gr.Audio(label="Output Audio",elem_id="tts-audio")
btn = gr.Button("Generate")
btn.click(fn=generateAudio,
inputs=[textbox],
outputs=[audio_output])
# 通过文本进行学习之后的 只回答和文本相关的内容 并且输出成音频还是
audio_output2 = gr.Audio(label="Output Audio", elem_id="tts-audio-from-file")
btn2 = gr.Button("Training data(click onece can train data)")
btn2.click(fn=generateAudio_from_file,
inputs=[filebox],
outputs=[audio_output2])
# 经过训练后，点击进行生成语音
btn3 = gr.Button("Generate from file")
btn3.click(fn=generateAudio_wav_file,
inputs=[textbox], # 传入的是文本 搜索的是经过向量化后的文本
outputs=[audio_output2])
gr.Examples(examples=example,
inputs=[textbox],
outputs=[audio_output],
fn=generateAudio)
#app.launch(share=True)
app.launch()
# gr.Markdown("通过提问，然后输出结果是语音\n 加载文本，然后根据文本的内容回答问题")
# app = gr.Interface(
# fn=generateAudio,
# inputs="text",
# outputs="audio",
# title=title,
# # streaming=True,
# # live=True,
# examples=[("Ai Vtuber是什么"),("where is nanjing"),("who are you"),("今天吃什么好呢"),("武汉今天天气咋样")]
# )
# app.launch(server_name="0.0.0.0", server_port=7865, share=True)
# app.launch(share=True)
# app.launch()
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