flask_upload_only_vn.py

import numpy as np
from flask import Flask, request, render_template, url_for
import argparse
import os
import sys
import numpy as np
import timeit
import scipy.io.wavfile as wav_
# from infer import restore_model, load_audio
# from g2pNp2g_simple.p2gFuntion import p2g_simple as p2g

app = Flask(__name__)

config = 'config/quartznet12x1_abc.yaml'
encoder_checkpoint = 'quartznet12x1_abc_them100h/checkpoints/JasperEncoder-STEP-289936.pt'
decoder_checkpoint = 'quartznet12x1_abc_them100h/checkpoints/JasperDecoderForCTC-STEP-289936.pt'

# neural_factory = restore_model(config, encoder_checkpoint, decoder_checkpoint)
# print('restore model checkpoint done!')

@app.route('/')
def upload_form():
	return render_template('upload_templates/upload.html', audio_path = 'select file to predict!')

@app.route('/', methods=['POST'])
def get_prediction():
    print('PREDICT MODE')
    if request.method == 'POST':
        _file = request.files['file']
        if _file.filename == '':
            return upload_form()
        print('\n\nfile uploaded:',_file.filename)
        _file.save(os.path.join('static/uploaded', _file.filename))
        print('Write file success!')
        start = timeit.default_timer()
        # sig = load_audio(os.path.join('static/uploaded', _file.filename))
        end_load = timeit.default_timer()
        # greedy_hypotheses, beam_hypotheses = neural_factory.infer_signal(sig)
        # #predict_grapheme = p2g(predict)
        # end_predict = timeit.default_timer()
        # print('Load audio time:',end_load-start)
        # print('Predict time:',end_predict-end_load)
        # print('greedy predict:{}'.format(greedy_hypotheses))
        # print('beamLM predict:{}'.format(beam_hypotheses))

        return render_template('upload_templates/upload.html', _file.filename)

if __name__ == '__main__':
    #load_model()  # load model at the beginning once only
    app.secret_key = 'dangvansam'
    app.run(host='localhost', port=5000, debug=True)