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import streamlit as st
import speech_recognition as sr
from deep_translator import GoogleTranslator
from pydub import AudioSegment
from io import BytesIO
import tempfile
# Title of the app
st.title("Speech-to-Text with Translation to English")
# Initialize recognizer
recognizer = sr.Recognizer()
# Choice for input language
language_options = {"English": "en", "Hindi": "hi"}
input_language = st.selectbox("Select Input Language", options=language_options.keys())
selected_lang_code = language_options[input_language]
# Function to convert audio chunk to text
def speech_to_text(audio_data, lang="en"):
try:
st.info("Converting speech to text...")
detected_text = recognizer.recognize_google(audio_data, language=lang)
return detected_text
except Exception as e:
st.error(f"Error in speech recognition: {e}")
return None
# Process uploaded audio file
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "ogg"])
if uploaded_file:
with st.spinner("Processing uploaded audio..."):
try:
# Convert uploaded file to WAV format using pydub
audio = AudioSegment.from_file(BytesIO(uploaded_file.read()))
# Split audio into 30-second chunks
chunk_duration_ms = 30000
chunks = [audio[i:i+chunk_duration_ms] for i in range(0, len(audio), chunk_duration_ms)]
text_output = ""
for i, chunk in enumerate(chunks):
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav_file:
chunk.export(tmp_wav_file.name, format="wav")
with sr.AudioFile(tmp_wav_file.name) as source:
audio_data = recognizer.record(source)
detected_text = speech_to_text(audio_data, lang=selected_lang_code)
if detected_text:
text_output += detected_text + " "
# Display detected text and translate
if text_output:
st.write("Detected Speech Text:", text_output)
translator = GoogleTranslator(source='auto', target='en')
translated_text = translator.translate(text_output)
st.write("Translated Text (English):", translated_text)
except Exception as e:
st.error(f"Error processing the audio file: {e}")
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