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import gradio as gr
from transformers import pipeline
from espnet2.bin.tts_inference import Text2Speech
import warnings
from krill_to_latin import Perevod
perevod = Perevod()
warnings.filterwarnings('ignore')
def generateTextAndAudio(inputText, numGen):
# --- Generating the Text ---
# With the provided text from user, generate more text up to `numGen` tokens/sub-words
inputText = perevod.translate_for_summarizer(inputText)
textOutput = textGenerator(inputText, max_length=numGen)
# The output of the text generator is a list of dictionaries, grab the first dictionary
# then get the generated text from the dictionary using the `generated_text` key
genText = textOutput[0]['generated_text']
print("-" * 75)
print("Input Text:", inputText)
print("Generated Text:", genText)
print("-" * 75)
# --- Generating the Audio ---
# With the newly generated text, generate some speech
#audioOutput = audioGenerator(genText)
# Get the wav data
#genAudio = audioOutput['wav']
# Return two things
# 1) Generated Text
# 2) 24k sampling rate, and the Generated Audio (wav) as numpy (instead of tensor)
# return genText, (24000, genAudio.numpy())
return genText
# Main
textGenerator = pipeline('text-generation', model='rifkat/GPTuz')
audioGenerator = Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan")
input1_textbox = gr.Textbox(label="Tekst*")
input2_slider = gr.Slider(minimum=1, maximum=100, step=1, default=30, label="Generatsiya bo'ladigan so'zlar soni")
output1_textbox = gr.Textbox(label="Generatsiya bo'lgan tekst")
# output2_Audio = gr.Audio(label="Generatsiya bo'lgan audio")
title = "Tekst gereratsiya qiling!"
description = "Tekst kiriting va nechta so'z generatsiya qilishini"
examples = [
["Давлат хавфсизлик хизмати", 50],
["Шунинг учун биз", 30],
["Лекин бугун бу нарсани қилмасак", 60]
]
iface = gr.Interface(fn=generateTextAndAudio,
inputs=[input1_textbox, input2_slider],
# outputs=[output1_textbox, output2_Audio],
outputs=output1_textbox,
title=title,
description=description,
examples=examples).launch(debug=True) |