File size: 9,697 Bytes
8fd08e0 aaa3b8b da5b41d 8fd08e0 aaa3b8b 41d9375 aaa3b8b 8fd08e0 aaa3b8b 8fd08e0 41d9375 8fd08e0 41d9375 8fd08e0 aaa3b8b 8fd08e0 aaa3b8b 8fd08e0 aaa3b8b 8fd08e0 41d9375 8fd08e0 da5b41d 8fd08e0 41d9375 aaa3b8b 41d9375 aaa3b8b 41d9375 aaa3b8b 41d9375 aaa3b8b 41d9375 da5b41d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
import streamlit as st
import pandas as pd
from st_audiorec import st_audiorec
import datetime
import os
import matplotlib.pyplot as plt
import librosa
from src.model.transcriber import transcribe_audio
from src.model.predict import predict_emotion
DIRECTORY = "audios"
FILE_NAME = "audio.wav"
CHUNK = 1024
# FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
def application():
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("---")
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 is not None:
with open(os.path.join(DIRECTORY,FILE_NAME), "wb") as f:
f.write(audio_file.getbuffer())
st.success(f"Saved file: {FILE_NAME}")
start_inference = st.button("Start emotion recogniton","inf_on_upl_btn")
emotion_labels = ["joie", "colère", "neutre"]
colors = ['#f6d60a', '#f71c1c', '#cac8c8']
if start_inference:
# Configuration Streamlit
with st.spinner("Real-time emotion analysis..."):
# uploaded_file = st.file_uploader("Choisissez un fichier audio", type=["wav", "mp3"])
if audio_file is not None:
# Charger et rééchantillonner l'audio
audio, sr = librosa.load(audio_file, sr=RATE)
# chunk = audio_file
# Paramètres de la fenêtre glissante
window_size = 1 # en secondes
hop_length = 0.5 # en secondes
# Créer un graphique en temps réel
fig, ax = plt.subplots()
lines = [ax.plot([], [], label=emotion)[0] for emotion in emotion_labels]
ax.set_ylim(0, 1)
ax.set_xlim(0, len(audio) / sr)
ax.set_xlabel("Temps (s)")
ax.set_ylabel("Probabilité")
ax.legend()
chart = st.pyplot(fig)
scores = [[],[],[]] # 3 émotions pour l'instant
# Traitement par fenêtre glissante
for i in range(0, len(audio), int(hop_length * sr)):
chunk = audio[i:i + int(window_size * sr)]
if len(chunk) < int(window_size * sr):
break
emotion_scores = predict_emotion(chunk, output_probs=True, sampling_rate=RATE)
# Mettre à jour le graphique
for emotion, line in zip(emotion_labels, lines):
xdata = list(line.get_xdata())
ydata = list(line.get_ydata())
xdata.append(i / sr)
ydata.append(emotion_scores[emotion])
scores[list(emotion_scores).index(emotion)].append(emotion_scores[emotion])
line.set_data(xdata, ydata)
ax.relim()
ax.autoscale_view()
chart.pyplot(fig, use_container_width=True)
# Prepare the styling
st.markdown("""
<style>
.colored-box {
padding: 10px;
border-radius: 5px;
color: white;
font-weight: bold;
text-align: center;
}
</style>
"""
, unsafe_allow_html=True)
# Dynamically create the specified number of columns
columns = st.columns(len(emotion_scores))
# emotion_scores_mean = [sum(sublist) / len(sublist) for sublist in scores]
emotion_scores_mean = {emotion:sum(sublist) / len(sublist) for emotion, sublist in zip(emotion_labels, scores)}
max_emo = max(emotion_scores_mean)
emotion_scores_sorted = dict(sorted(emotion_scores_mean.items(), key=lambda x: x[1], reverse=True))
colors_sorted = [colors[list(emotion_scores_mean.keys()).index(key)] for key in list(emotion_scores_sorted.keys())]
# Add content to each column
for i, (col, emotion) in enumerate(zip(columns, emotion_scores_sorted)):
color = colors_sorted[i % len(colors_sorted)] # Cycle through colors if more columns than colors
col.markdown(f"""
<div class="colored-box" style="background-color: {color};">
{emotion} : {100*emotion_scores_sorted[emotion]:.2f} %
</div>
"""
, unsafe_allow_html=True)
st.success("Analyse terminée !")
else:
st.warning("You need to load an audio file !")
st.subheader("Feedback")
# Initialisation du fichier CSV
csv_file = os.path.join("src","predictions","feedback.csv")
# Vérifier si le fichier CSV existe, sinon le créer avec des colonnes appropriées
if not os.path.exists(csv_file):
df = pd.DataFrame(columns=["filepath", "prediction", "feedback"])
df.to_csv(csv_file, index=False)
# Charger les données existantes du CSV
df = pd.read_csv(csv_file)
with st.form("feedback_form"):
st.write("What should have been the correct prediction ? (*Choose the same emotion if the prediction was correct*).")
feedback = st.selectbox("Your answer :", ['Sadness','Anger', 'Disgust', 'Fear', 'Surprise', 'Joy', 'Neutral'])
submit_button = st.form_submit_button("Submit")
st.write("En cliquant sur ce bouton, vous acceptez que votre audio soit sauvegardé dans notre base de données.")
if submit_button:
# Ajouter le feedback au DataFrame
new_entry = {"filepath": audio_file.name, "prediction": max_emo, "feedback": feedback}
df = df.append(new_entry, ignore_index=True)
# Sauvegarder les données mises à jour dans le fichier CSV
df.to_csv(csv_file, index=False)
# Sauvegarder le fichier audio
with open(os.path.join("src","predictions","data"), "wb") as f:
f.write(audio_file.getbuffer())
# Confirmation pour l'utilisateur
st.success("Merci pour votre retour ! Vos données ont été sauvegardées.")
with tab2:
st.header("🔈 Realtime Audio Record")
st.write("Here you can record an audio.")
if st.button("Register", key="register-button"):
st.success("Audio registered successfully.")
audio_file = st_audiorec()
if audio_file is not None:
st.audio(audio_file, format='audio/wav')
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}")
|