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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}")
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