Upload folder using huggingface_hub
Browse files- README.md +2 -8
- tts_gradio.py +255 -0
README.md
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---
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title:
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colorFrom: green
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colorTo: yellow
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sdk: gradio
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sdk_version: 4.36.1
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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title: grd
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app_file: tts_gradio.py
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sdk: gradio
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sdk_version: 4.36.1
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---
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tts_gradio.py
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import warnings
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warnings.filterwarnings("ignore")
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# 外部库
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import re
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import requests
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import argparse
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import json
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import os
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import re
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import tempfile
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# import librosa
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# import numpy as np
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# import torch
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# from torch import no_grad, LongTensor
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# import commons
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import gradio as gr
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# import gradio.utils as gr_utils
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# import gradio.processing_utils as gr_processing_utils
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all_example = "my voice is my passport verify me."
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microsoft_model_list = [
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"en-US-AvaMultilingualNeural"
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]
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openai_model_list = [
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"alloy",
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"echo",
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"fable",
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"onyx",
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"nova",
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"shimmer"
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]
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eleven_voice_id = [
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"21m00Tcm4TlvDq8ikWAM",
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"29vD33N1CtxCmqQRPOHJ",
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"2EiwWnXFnvU5JabPnv8n",
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"5Q0t7uMcjvnagumLfvZi",
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"AZnzlk1XvdvUeBnXmlld",
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"CYw3kZ02Hs0563khs1Fj",
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"D38z5RcWu1voky8WS1ja",
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"EXAVITQu4vr4xnSDxMaL",
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"ErXwobaYiN019PkySvjV",
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"GBv7mTt0atIp3Br8iCZE",
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"IKne3meq5aSn9XLyUdCD",
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"JBFqnCBsd6RMkjVDRZzb",
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"LcfcDJNUP1GQjkzn1xUU",
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"MF3mGyEYCl7XYWbV9V6O",
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"N2lVS1w4EtoT3dr4eOWO",
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"ODq5zmih8GrVes37Dizd",
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"SOYHLrjzK2X1ezoPC6cr",
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"TX3LPaxmHKxFdv7VOQHJ",
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"ThT5KcBeYPX3keUQqHPh",
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"TxGEqnHWrfWFTfGW9XjX",
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"VR6AewLTigWG4xSOukaG",
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"XB0fDUnXU5powFXDhCwa",
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"Xb7hH8MSUJpSbSDYk0k2",
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"XrExE9yKIg1WjnnlVkGX",
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"ZQe5CZNOzWyzPSCn5a3c",
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"Zlb1dXrM653N07WRdFW3",
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"bVMeCyTHy58xNoL34h3p",
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"flq6f7yk4E4fJM5XTYuZ",
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"g5CIjZEefAph4nQFvHAz",
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"iP95p4xoKVk53GoZ742B",
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"jBpfuIE2acCO8z3wKNLl",
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"jsCqWAovK2LkecY7zXl4",
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"nPczCjzI2devNBz1zQrb",
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"oWAxZDx7w5VEj9dCyTzz",
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"onwK4e9ZLuTAKqWW03F9",
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"pFZP5JQG7iQjIQuC4Bku",
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"pMsXgVXv3BLzUgSXRplE",
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"pNInz6obpgDQGcFmaJgB",
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"piTKgcLEGmPE4e6mEKli",
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"pqHfZKP75CvOlQylNhV4",
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"t0jbNlBVZ17f02VDIeMI",
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"yoZ06aMxZJJ28mfd3POQ",
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"z9fAnlkpzviPz146aGWa",
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"zcAOhNBS3c14rBihAFp1",
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"zrHiDhphv9ZnVXBqCLjz",
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]
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eleven_name = [
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"Rachel",
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"Drew",
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"Clyde",
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"Paul",
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"Domi",
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"Dave",
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"Fin",
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"Sarah",
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"Antoni",
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"Thomas",
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"Charlie",
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"George",
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"Emily",
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"Elli",
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"Callum",
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"Patrick",
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"Harry",
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"Liam",
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"Dorothy",
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"Josh",
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"Arnold",
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"Charlotte",
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"Alice",
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"Matilda",
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"James",
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"Joseph",
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"Jeremy",
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"Michael",
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"Ethan",
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"Chris",
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"Gigi",
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"Freya",
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"Brian",
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"Grace",
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"Daniel",
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"Lily",
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"Serena",
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"Adam",
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"Nicole",
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"Bill",
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"Jessie",
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"Sam",
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"Glinda",
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"Giovanni",
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"Mimi",
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]
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eleven_id_model_name_dict = dict(zip(eleven_name, eleven_voice_id))
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def openai(text, name):
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headers = {
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'Authorization': 'Bearer ' + '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',
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'Content-Type': 'application/json',
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}
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json_data = {
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'model': 'tts-1-hd',
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'input': f'{text}',
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'voice': f'{name}',
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}
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response = requests.post('https://api.openai.com/v1/audio/speech', headers=headers, json=json_data)
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# Note: json_data will not be serialized by requests
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# exactly as it was in the original request.
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#data = '{\n "model": "tts-1",\n "input": "The quick brown fox jumped over the lazy dog.",\n "voice": "alloy"\n }'
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#response = requests.post('https://api.openai.com/v1/audio/speech', headers=headers, data=data)
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return "Success", response
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def elevenlabs(text,name):
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url = f"https://api.elevenlabs.io/v1/text-to-speech/{eleven_id_model_name_dict[name]}"
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CHUNK_SIZE = 1024
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#url = "https://api.elevenlabs.io/v1/text-to-speech/<voice-id>"
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headers = {
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"Accept": "audio/mpeg",
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"Content-Type": "application/json",
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"xi-api-key": "a3391f0e3ff8472b61978dbb70ccc6fe"
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}
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data = {
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"text": f"{text}",
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"model_id": "eleven_monolingual_v1",
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"voice_settings": {
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"stability": 0.5,
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"similarity_boost": 0.5
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}
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}
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response = requests.post(url, json=data, headers=headers)
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# with open('output.mp3', 'wb') as f:
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# for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
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# if chunk:
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# f.write(chunk)
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return "Success", response
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def microsoft(text, name, style="Neural"):
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"""
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:param text:
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:param name:
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:param style:
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:return:
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"""
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headers = {
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'Ocp-Apim-Subscription-Key': '1f1ef0ce53b84261be94fab81df7e628',
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'Content-Type': 'application/ssml+xml',
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'X-Microsoft-OutputFormat': 'audio-16khz-128kbitrate-mono-mp3',
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'User-Agent': 'curl',
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}
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data = ("<speak version='1.0' xml:lang='en-US'>"
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f"<voice xml:lang='en-US' name='{name}'>" # xml:gender='Female'
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f"{text}"
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"</voice>"
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"</speak>")
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response = requests.post(
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'https://japaneast.tts.speech.microsoft.com/cognitiveservices/v1',
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headers=headers,
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data=data,
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)
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return "Success", "sss"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--device', type=str, default='cuda')
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parser.add_argument("--share", action="store_true", default=True, help="share gradio app")
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parser.add_argument("--port", type=int, default=8081, help="port")
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parser.add_argument('--model_info_path', type=str, default='/gluster/speech_data/info.json')
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args = parser.parse_args()
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app = gr.Blocks()
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with app:
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gr.Markdown("## Japanese TTS Demo")
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with gr.Tabs():
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with gr.TabItem("11Labs"):
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tts_input1 = gr.TextArea(label="Text", value=all_example)
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tts_input2 = gr.Dropdown(eleven_name, label="name")
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tts_submit = gr.Button("Generate", variant="primary")
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tts_output1 = gr.Textbox(label="Output Message")
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tts_output2 = gr.Audio(label="Output Audio")
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tts_submit.click(elevenlabs, [tts_input1, tts_input2],
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[tts_output1, tts_output2])
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with gr.TabItem("微软"):
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tts_input1 = gr.TextArea(label="Text", value=all_example)
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tts_input2 = gr.Dropdown(microsoft_model_list, label="name")
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tts_submit = gr.Button("Generate", variant="primary")
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tts_output1 = gr.Textbox(label="Output Message")
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tts_output2 = gr.Audio(label="Output Audio")
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tts_submit.click(microsoft, [tts_input1, tts_input2],
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[tts_output1, tts_output2])
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with gr.TabItem("openai"):
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tts_input1 = gr.TextArea(label="Text", value=all_example)
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tts_input2 = gr.Dropdown(openai_model_list, label="name")
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tts_submit = gr.Button("Generate", variant="primary")
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tts_output1 = gr.Textbox(label="Output Message")
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tts_output2 = gr.Audio(label="Output Audio")
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tts_submit.click(openai, [tts_input1, tts_input2],
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[tts_output1, tts_output2])
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app.queue(max_size=10)
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app.launch(share=True)
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# _, audio = openai(all_example,'alloy')
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# print(audio)
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# with open("test99.mp3", "wb") as f:
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# f.write(audio.content)
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