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| import streamlit as st | |
| from st_audiorec import st_audiorec | |
| # from src.model.transcriber import transcribe_audio | |
| def studio(): | |
| st.title("SISE ultimate challenge") | |
| st.write("C'est le dernier challenge de la formation SISE.") | |
| st.markdown(""" | |
| **Overview:** | |
| - Analyse de logs | |
| - Analyse de données | |
| - Machine learning | |
| """) | |
| st.markdown("---") | |
| st.header("🎧 Audio File Studio") | |
| tab1, tab2, tab3 = st.tabs(["⬆️ Record Audio", "🔈 Realtime Audio", "📝 Transcription"]) | |
| with tab1: | |
| st.header("⬆️ Upload Audio Record") | |
| st.write("Here you can upload a pre-recorded audio.") | |
| audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "ogg"]) | |
| if "audio_file" not in st.session_state: | |
| st.session_state.audio_file = None | |
| if audio_file is not None: | |
| st.success("Audio file uploaded successfully !") | |
| st.session_state.audio_file = audio_file | |
| # with open(os.path.join(DIRECTORY,FILE_NAME), "wb") as f: | |
| # f.write(audio_file.getbuffer()) | |
| # st.success(f"Saved file: {FILE_NAME}") | |
| with tab2: | |
| st.header("🔈 Realtime Audio Record") | |
| st.write("Here you can record an audio.") | |
| if "audio_file" not in st.session_state: | |
| st.session_state.audio_file = None | |
| audio_file = st_audiorec() | |
| if audio_file is not None: | |
| st.audio(audio_file, format='audio/wav') | |
| st.success("Audio recorded successfully !") | |
| st.session_state.audio_file = audio_file | |
| # Boutons pour démarrer et arrêter l'enregistrement | |
| # start_button = st.button("Démarrer l'enregistrement") | |
| # stop_button = st.button("Arrêter l'enregistrement") | |
| # start_stop = st.button("Démarrer/Arrêter l'enregistrement") | |
| # Zone de visualisation des émotions en temps réel | |
| # emotion_placeholder = st.empty() | |
| # final_emotion_placeholder = st.empty() | |
| # audio = pyaudio.PyAudio() | |
| # audio_buffer = np.array([]) | |
| # emotion_prediction = "Aucune prédiction" | |
| # is_recording = False | |
| # if start_stop: | |
| # is_recording = not is_recording | |
| # # Variables globales pour le partage de données entre threads | |
| # def audio_callback(in_data, frame_count, time_info, status): | |
| # global audio_buffer | |
| # audio_data = np.frombuffer(in_data, dtype=np.float32) | |
| # audio_buffer = np.concatenate((audio_buffer, audio_data)) | |
| # return (in_data, pyaudio.paContinue) | |
| # def predict_emotion_thread(): | |
| # global audio_buffer, emotion_prediction | |
| # while is_recording: | |
| # if len(audio_buffer) >= CHUNK: | |
| # chunk = audio_buffer[:CHUNK] | |
| # audio_buffer = audio_buffer[STRIDE:] | |
| # emotion_prediction = predict_emotion(chunk, output_probs=False, sampling_rate=RATE) # Utilisez votre modèle ici | |
| # # time.sleep(0.1) | |
| # if is_recording: | |
| # audio_buffer = np.array([]) | |
| # stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, | |
| # frames_per_buffer=CHUNK, stream_callback=audio_callback) | |
| # stream.start_stream() | |
| # threading.Thread(target=predict_emotion_thread, daemon=True).start() | |
| # st.write("Enregistrement en cours...") | |
| # else: | |
| # stream.stop_stream() | |
| # stream.close() | |
| # st.write("Enregistrement arrêté.") | |
| # emotion_display = st.empty() | |
| # while is_recording: | |
| # emotion_display.write(f"Émotion détectée : {emotion_prediction}") | |
| # # time.sleep(0.1) | |
| # audio.terminate() | |
| # stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) | |
| # frames = [] | |
| # real_time_emotions = [] | |
| # while not stop_button: | |
| # data = stream.read(CHUNK) | |
| # frames.append(data) | |
| # # Traitement en temps réel (par tranche de 1 seconde) | |
| # if len(frames) >= RATE // CHUNK: | |
| # audio_segment = np.frombuffer(b''.join(frames[-(RATE // CHUNK):]), dtype=np.int16) | |
| # emotion = predict_emotion(audio_segment, output_probs=False, sampling_rate=RATE) | |
| # real_time_emotions.append(emotion) | |
| # emotion_placeholder.line_chart(real_time_emotions) # Affichage graphique des émotions | |
| # # Arrêt de l'enregistrement | |
| # stream.stop_stream() | |
| # stream.close() | |
| # audio.terminate() | |
| # # Sauvegarde de l'audio enregistré | |
| # wf = wave.open("output.wav", "wb") | |
| # wf.setnchannels(CHANNELS) | |
| # wf.setsampwidth(audio.get_sample_size(FORMAT)) | |
| # wf.setframerate(RATE) | |
| # wf.writeframes(b"".join(frames)) | |
| # wf.close() | |
| # # Prédiction finale sur tout l'audio enregistré | |
| # full_audio_data = np.frombuffer(b''.join(frames), dtype=np.int16) | |
| # final_emotion = predict_emotion(full_audio_data) | |
| # final_emotion_placeholder.write(f"Émotion finale prédite : {final_emotion}") | |
| with tab3: | |
| st.header("📝 Speech2Text Transcription") | |
| st.write("Here you can get the audio transcript.") | |
| save = st.checkbox("Save transcription to .txt", value=False, key="save-transcript") | |
| ############################# A décommenté quand ce sera débogué | |
| if st.button("Transcribe", key="transcribe-button"): | |
| # # Fonction pour transcrire l'audio | |
| # transcription = transcribe_audio(st.audio) | |
| # # Charger et transcrire l'audio | |
| # # audio, rate = load_audio(audio_file_path) # (re)chargement de l'audio si nécessaire | |
| # transcription = transcribe_audio(audio_file, sampling_rate=16000) | |
| # # Afficher la transcription | |
| # st.write("Transcription :", transcription) | |
| st.success("Audio registered successfully.") | |
| # if save: | |
| # file_path = "transcript.txt" | |
| # # Write the text to the file | |
| # with open(file_path, "w") as file: | |
| # file.write(transcription) | |
| # st.success(f"Text saved to {file_path}") | |