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| import streamlit as st | |
| from streamlit_audiorec import st_audiorec | |
| from transformers import pipeline | |
| from gtts import gTTS | |
| import tempfile | |
| import os | |
| import time | |
| import audio_recorder_streamlit as audio_recorder # Alternative package | |
| # ------------------------------ | |
| # Setup models with progress indicators | |
| # ------------------------------ | |
| def load_models(): | |
| with st.spinner("Loading speech-to-text model..."): | |
| stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-small") | |
| with st.spinner("Loading translation models..."): | |
| translator_en = pipeline("translation", model="Helsinki-NLP/opus-mt-ps-en") | |
| translator_ur = pipeline("translation", model="Helsinki-NLP/opus-mt-ps-ur") | |
| return stt_model, translator_en, translator_ur | |
| try: | |
| stt_model, translator_en, translator_ur = load_models() | |
| except Exception as e: | |
| st.error(f"Error loading models: {str(e)}") | |
| st.stop() | |
| # ------------------------------ | |
| # Streamlit UI | |
| # ------------------------------ | |
| st.set_page_config(page_title="Pashto Voice Translator", page_icon="🎙️") | |
| st.title("🎙️ Pashto Voice Translator") | |
| st.write("Speak in **Pashto** and get translation in **English** or **Urdu** with voice playback.") | |
| # Language preference | |
| target_lang = st.radio("Select output language:", ["English", "Urdu"]) | |
| st.write("Record your voice below:") | |
| # Using audio_recorder_streamlit instead | |
| wav_audio_data = audio_recorder.audio_recorder(text="Click to record", icon_size="2x") | |
| if wav_audio_data is not None: | |
| st.audio(wav_audio_data, format="audio/wav") | |
| # Save audio to a temporary file with proper handling | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_wav: | |
| temp_wav.write(wav_audio_data) | |
| temp_filename = temp_wav.name | |
| try: | |
| # ------------------------------ | |
| # Step 1: Speech to Text (Pashto) | |
| # ------------------------------ | |
| with st.spinner("Transcribing speech..."): | |
| pashto_text = stt_model(temp_filename)["text"] | |
| # Clean up temporary file | |
| os.unlink(temp_filename) | |
| st.subheader("Pashto transcription:") | |
| st.info(pashto_text) | |
| # ------------------------------ | |
| # Step 2: Translation | |
| # ------------------------------ | |
| with st.spinner("Translating..."): | |
| if target_lang == "English": | |
| translated = translator_en(pashto_text)[0]["translation_text"] | |
| else: | |
| translated = translator_ur(pashto_text)[0]["translation_text"] | |
| st.subheader(f"Translated ({target_lang}):") | |
| st.success(translated) | |
| # ------------------------------ | |
| # Step 3: Text-to-Speech | |
| # ------------------------------ | |
| with st.spinner("Generating audio..."): | |
| tts = gTTS(text=translated, lang="en" if target_lang == "English" else "ur") | |
| # Use BytesIO to avoid file handling issues | |
| from io import BytesIO | |
| audio_bytes = BytesIO() | |
| tts.write_to_fp(audio_bytes) | |
| audio_bytes.seek(0) | |
| st.audio(audio_bytes, format="audio/mp3") | |
| except Exception as e: | |
| st.error(f"An error occurred: {str(e)}") | |
| # Ensure temporary file is cleaned up even if error occurs | |
| if os.path.exists(temp_filename): | |
| os.unlink(temp_filename) |