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import gradio as gr
from transformers import pipeline
from pydub import AudioSegment
import wordtodigits
model = pipeline("automatic-speech-recognition",
"facebook/wav2vec2-base-960h")
model2 = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")
def asr(speech):
try:
transcript = model(speech)['text']
strings = transcript.split()
text = ""
equation = ""
symbols = {"plus":"+","minus":"-","times":"*","divide":"/"}
for i in range(len(strings)):
if strings[i].lower() in symbols:
text = wordtodigits.convert(text)
equation += text + symbols[strings[i].lower()]
text=""
continue
text += strings[i].lower() + " "
if i == len(strings)-1:
text = wordtodigits.convert(text)
equation += text
ans = round(eval(equation),2)
speech = transcript + " is equal to "+str(ans)
except:
transcript = "Error in Translation/Format of Audio"
equation = "Error in Translation/Format of Audio"
ans = "Error in Translation/Format of Audio"
speech = "Error in Translation or Format of Audio"
return transcript, equation, ans, model2(speech)
gr.Interface(fn=asr,
#inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=False, label="Please record your voice"),
inputs = gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here"),
outputs = [gr.outputs.Textbox(type="str", label="Text Translation"),
gr.outputs.Textbox(type="str", label="Equation"),
gr.outputs.Textbox(type="str", label="Answer"),
gr.outputs.Audio(type="file", label="Speech Answer")],
title = "Speech Equation Solver",
description = "This app aims to translate speech into an equation, solve the equation and generate a speech to tell the user the answer to a problem <br> <b>Addition:</b> x plus y <br> <b>Subtraction:</b> x minus y <br> <b>Multiplication:</b> x times y <br> <b>Division:</b> x divide y",
article = "Models: Wav2Vec2-Base-960h, fastspeech2-en-ljspeech",
examples=["additionTest.mp3","minusTest.mp3","multiplyTest.mp3","divideTest.mp3"]
).launch() |