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import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
import io
from fastapi import FastAPI, File, UploadFile
from werkzeug.utils import secure_filename
import speech_recognition as sr
import subprocess
import os
import requests
import random
import pandas as pd
from pydub import AudioSegment
from datetime import datetime
from datetime import date
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import shutil
import json
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
from pydantic import BaseModel
from typing import Annotated
from transformers import BertTokenizerFast, EncoderDecoderModel
import torch
import random
import string
import time
from fastapi import Form
class Query(BaseModel):
text: str
code:str
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')
# model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/fastspeech2-en-ljspeech",
# 'facebook/fastspeech2-en-200_speaker-cv4',
arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator([model], cfg)
from fastapi import FastAPI, Request, Depends, UploadFile, File
from fastapi.exceptions import HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=['*'],
allow_credentials=True,
allow_methods=['*'],
allow_headers=['*'],
)
# cred = credentials.Certificate('key.json')
# app1 = firebase_admin.initialize_app(cred)
# db = firestore.client()
# data_frame = pd.read_csv('data.csv')
@app.on_event("startup")
async def startup_event():
print("on startup")
audio_space="https://audiospace-1-u9912847.deta.app/upload"
# @app.post("/code")
# async def get_code(request: Request):
# data = await request.form()
# code = data.get("code")
# global audio_space
# print("code ="+code)
# audio_space= audio_space+code
import threading
@app.post("/")
async def get_answer(q: Query ):
text = q.text
code= q.code
N = 20
res = ''.join(random.choices(string.ascii_uppercase +
string.digits, k=N))
res= res+ str(time.time())
filename= res
t = threading.Thread(target=do_ML, args=(filename,text,code))
t.start()
return JSONResponse({"id": filename})
return "hello"
import requests
import io
import torch
from scipy.io import wavfile
import soundfile as sf
import wave
import audioop
import io
def do_ML(filename:str,text:str,code:str):
sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
def compress_audio_wav_bytes(wav_bytes, output_format):
# Load the WAV data from bytes
audio_data, sample_rate = sf.read((wav_bytes))
# Compress and save the audio data with the specified output format
output_bytes = io.BytesIO()
sf.write(output_bytes, audio_data, sample_rate, format=output_format)
# Retrieve the compressed audio data as bytes
compressed_bytes = output_bytes.getvalue()
return compressed_bytes
wav_bytes = io.BytesIO()
# Write the audio data to the byte stream
sf.write(wav_bytes, wav.numpy(), rate, format='WAV', subtype='PCM_16')
# Set the position of the byte stream to the beginning
wav_bytes.seek(0)
format = 'flac' # Specify the output format ('flac', 'mp3', etc.)
wav_bytes = compress_audio_wav_bytes(wav_bytes, format)
files = {'file': wav_bytes}
# global audio_space
url = code
data = {"filename": filename}
response = requests.post(url, files=files,data= data)
print(response.text)
if response.status_code == 200:
print("File uploaded successfully.")
# Handle the response as needed
else:
print("File upload failed.")
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