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import gradio as gr | |
import json | |
import spacy | |
import re | |
import string | |
import pandas as pd | |
import os | |
import requests | |
os.system('python -m spacy download en_core_web_sm') | |
nlp = spacy.load("en_core_web_sm") | |
nlp.add_pipe('sentencizer') | |
def read_gs(sheet_url): | |
s_url = sheet_url.replace('/edit#gid=', '/export?format=csv&gid=') | |
df = pd.read_csv(s_url) | |
return df | |
def download_and_save_file(URL, audio_dir): | |
headers = { | |
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36', | |
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', | |
'referer': 'https://www.google.com/', | |
'accept-encoding': 'gzip, deflate, br', | |
'accept-language': 'en-US,en;q=0.9,', | |
'cookie': 'prov=6bb44cc9-dfe4-1b95-a65d-5250b3b4c9fb; _ga=GA1.2.1363624981.1550767314; __qca=P0-1074700243-1550767314392; notice-ctt=4%3B1550784035760; _gid=GA1.2.1415061800.1552935051; acct=t=4CnQ70qSwPMzOe6jigQlAR28TSW%2fMxzx&s=32zlYt1%2b3TBwWVaCHxH%2bl5aDhLjmq4Xr', | |
} | |
doc = requests.get(URL, headers=headers) | |
file_name = URL.split('/')[-1].split('?')[0] | |
audio_path = f'{audio_dir}/{file_name}' | |
with open(audio_path, 'wb') as f: | |
f.write(doc.content) | |
return audio_path | |
def select_samples(): | |
df = read_gs('https://docs.google.com/spreadsheets/d/17QG4puJRXN8V5froIv8YrJIMsns0GTt4/edit#gid=1020901598') | |
audio_dir = 'AUDIO' | |
os.makedirs(audio_dir, exist_ok=True) | |
df = df.sample(1) | |
audio_url = df.url.values[0] | |
audio_path = download_and_save_file(audio_url, audio_dir) | |
return audio_path, df['text'].values[0] | |
title = '🎵 Annotate audio' | |
description = '''Choose a sentence that describes audio the best if there's no such sentence please choose `No audio description`''' | |
audio_path, full_text = select_samples() | |
full_text = full_text.translate(str.maketrans('', '', string.punctuation)) | |
sents = [re.sub(r'###audio###\d###', '', s.text) for s in nlp(full_text).sents] | |
sents.append('No audio description') | |
def audio_demo(text, audio, audio_id): | |
with open('data.json', 'w') as f: | |
data = { | |
'audio':audio_id, | |
'text':text | |
} | |
json.dump(data, f) | |
return 'success!' | |
iface = gr.Interface( | |
audio_demo, | |
inputs=[gr.Dropdown(sents, label='audio description'), gr.Audio(audio_path, type="filepath"), gr.Textbox(value=audio_path, visible=False)], | |
outputs=[gr.Textbox(label="output")], | |
allow_flagging="never", | |
title=title, | |
description=description, | |
) | |
if __name__ == "__main__": | |
iface.launch(show_error=True, debug=True) |