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import numpy as np
import streamlit as st
import speech_recognition as sr
import os
import tempfile
import librosa
import soundfile as sf
def convert_to_wav(audio_file):
y, sr = librosa.load(audio_file, sr=None)
with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_wav:
sf.write(tmp_wav.name, y, sr, format='wav')
return tmp_wav.name
def transcribe_audio(audio_file):
recognizer = sr.Recognizer()
with sr.AudioFile(audio_file) as source:
audio = recognizer.record(source)
try:
return recognizer.recognize_google(audio)
except sr.UnknownValueError:
return "Speech recognition could not understand the audio"
except sr.RequestError as e:
return f"Could not request results from speech recognition service; {e}"
def main():
st.title("Speech-to-Text Converter")
uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "m4a", "ogg", "flac"])
if uploaded_file is not None:
st.audio(uploaded_file)
if st.button("Transcribe"):
with tempfile.NamedTemporaryFile(delete=False, suffix='.' + uploaded_file.name.split('.')[-1]) as tmp_file:
tmp_file.write(uploaded_file.getvalue())
tmp_file_path = tmp_file.name
try:
wav_file_path = convert_to_wav(tmp_file_path)
transcription = transcribe_audio(wav_file_path)
st.write("Transcription:")
st.write(transcription)
except Exception as e:
st.error(f"An error occurred: {str(e)}")
finally:
os.unlink(tmp_file_path)
if 'wav_file_path' in locals():
os.unlink(wav_file_path)
if __name__ == "__main__":
main()
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