Allegements

requirements.txt

@@ -6,16 +6,13 @@ numpy==1.23.5
 seaborn==0.13.2
 nltk==3.8.1
 scikit-learn==1.1.3
-scikit-learn-extra==0.3.0
 gensim==4.3.2
 sacrebleu==2.4.0
-pyspellchecker==0.8.1
 spacy==3.6.0
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0.tar.gz 
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-3.6.0/fr_core_news_sm-3.6.0.tar.gz
 pillow==9.5.0
 wordcloud==1.9.3
-pathlib==1.0.1
 networkx==2.7.0
 transformers==4.37.2
 keras-nlp==0.6.1
@@ -23,13 +20,9 @@ keras==2.12.0
 tensorflow==2.12.0
 sentencepiece==0.1.99
 openai-whisper==20231117
-sounddevice==0.4.6
 torch==2.2.0
-xformers==0.0.24
-translate==3.6.1
 speechrecognition==3.10.1
 audio_recorder_streamlit==0.0.8
-wave==0.0.2
 whisper==1.1.10
 wavio==0.0.8
 filesplit==4.0.1
@@ -39,7 +32,4 @@ graphviz==0.20.1
 gTTS==2.5.1
 https://files.pythonhosted.org/packages/cc/58/96aff0e5cb8b59c06232ea7e249ed902d04ec89f52636f5be06ceb0855fe/extra_streamlit_components-0.1.60-py3-none-any.whl
 streamlit-option-menu==0.3.12
-plotly==5.18.0
-bokeh==3.3.4
-shap==0.44.1
 deep-translator==1.11.4

tabs/data_viz_tab.py

@@ -7,9 +7,6 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-import plotly.express as px
-import plotly.graph_objects as go
-import plotly.figure_factory as ff
 from wordcloud import WordCloud
 import nltk
 from nltk.corpus import stopwords

tabs/exploration_tab.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import os
-import numpy as np
 import pandas as pd
 import collections
 from nltk.tokenize import word_tokenize
@@ -8,7 +7,7 @@ from nltk import download
 from ast import literal_eval
 from translate_app import tr
 if st.session_state.Cloud == 0:
-    import nltk
+    # import nltk
     import contextlib
     import re
     from nltk.corpus import stopwords

tabs/id_lang_tab.py

@@ -1,10 +1,8 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import os
 import matplotlib.pyplot as plt
 import tiktoken
-import random
 import joblib
 import json
 import csv
@@ -12,7 +10,6 @@ from transformers import pipeline
 import keras
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from sklearn.preprocessing import LabelEncoder
-from sklearn.feature_extraction.text import CountVectorizer
 from tensorflow.keras.utils import plot_model
 from filesplit.merge import Merge
 from extra_streamlit_components import tab_bar, TabBarItemData

tabs/modelisation_dict_tab.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
 import os
 from sacrebleu import corpus_bleu
 if st.session_state.Cloud == 0:

tabs/modelisation_seq2seq_tab.py

@@ -4,12 +4,12 @@ import numpy as np
 import os
 from sacrebleu import corpus_bleu
 from transformers import pipeline
-from translate import Translator
+# from translate import Translator
+from deep_translator import GoogleTranslator
 from audio_recorder_streamlit import audio_recorder
 import speech_recognition as sr
 import whisper
 import io
-# import wave
 import wavio
 from filesplit.merge import Merge
 import tensorflow as tf
@@ -19,7 +19,7 @@ from tensorflow import keras
 from keras_nlp.layers import TransformerEncoder
 from tensorflow.keras import layers
 from tensorflow.keras.utils import plot_model
-from PIL import Image
+# from PIL import Image
 from gtts import gTTS
 from extra_streamlit_components import tab_bar, TabBarItemData
 from translate_app import tr
@@ -463,7 +463,8 @@ def run():
         with col2:
             st.write(":red[**Trad. Google Translate**]")
             try:
-                translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
+                # translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
+                translator = GoogleTranslator(source=Lang_detected, target=l_tgt)
                 if custom_sentence!="":
                     translation = translator.translate(custom_sentence)
                     st.write("**"+l_tgt+" :**  "+translation)
@@ -488,31 +489,39 @@ def run():
             st.write("## **"+tr("Résultats")+" :**\n")
             st.audio(audio_bytes, format="audio/wav")
             try:
-                if detection:
-                    # Create a BytesIO object from the audio stream
-                    audio_stream_bytesio = io.BytesIO(audio_bytes)
+                # Create a BytesIO object from the audio stream
+                audio_stream_bytesio = io.BytesIO(audio_bytes)
 
-                    # Read the WAV stream using wavio
-                    wav = wavio.read(audio_stream_bytesio) 
+                # Read the WAV stream using wavio
+                wav = wavio.read(audio_stream_bytesio) 
 
-                    # Extract the audio data from the wavio.Wav object
-                    audio_data = wav.data
+                # Extract the audio data from the wavio.Wav object
+                audio_data = wav.data
 
-                    # Convert the audio data to a NumPy array
-                    audio_input = np.array(audio_data, dtype=np.float32)
-                    audio_input = np.mean(audio_input, axis=1)/32768
-            
+                # Convert the audio data to a NumPy array
+                audio_input = np.array(audio_data, dtype=np.float32)
+                audio_input = np.mean(audio_input, axis=1)/32768
+                
+                if detection:            
                     result = model_speech.transcribe(audio_input)
                     st.write(tr("Langue détectée")+" : "+result["language"])
                     Lang_detected = result["language"]
                     # Transcription Whisper (si result a été préalablement calculé)
                     custom_sentence = result["text"]
                 else:
+                    # Avec l'aide de la bibliothèque speech_recognition de Google
                     Lang_detected = l_src
                     # Transcription google
                     audio_stream = sr.AudioData(audio_bytes, 32000, 2) 
                     r = sr.Recognizer()
                     custom_sentence = r.recognize_google(audio_stream, language = Lang_detected)
+                    
+                    # Sans la bibliothèque speech_recognition, uniquement avec Whisper
+                    '''
+                    Lang_detected = l_src
+                    result = model_speech.transcribe(audio_input, language=Lang_detected)
+                    custom_sentence = result["text"]
+                    '''
 
                 if custom_sentence!="":
                     # Lang_detected = lang_classifier (custom_sentence)[0]['label']
@@ -520,7 +529,8 @@ def run():
                     st.write("")
                     st.write("**"+Lang_detected+" :**  :blue["+custom_sentence+"]")
                     st.write("")
-                    translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
+                    # translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
+                    translator = GoogleTranslator(source=Lang_detected, target=l_tgt)
                     translation = translator.translate(custom_sentence)
                     st.write("**"+l_tgt+" :**  "+translation)
                     st.write("")