| 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() | |