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") # requests.get("https://audiospace-1-u9912847.deta.app/sendcode") 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 = audio_space+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.")