....

app.py

@@ -43,7 +43,7 @@ if st.session_state.Cloud == 0:
     os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
 
 # Tabs in the ./tabs folder, imported here.
-from tabs import intro, text2speech_tab, sentence_similarity_tab
+from tabs import intro, sentence_similarity_tab, speech2text_tab
 
 
 with open("style.css", "r") as f:
@@ -58,8 +58,8 @@ st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
 TABS = OrderedDict(
     [
         (tr(intro.sidebar_name), intro),
-        (tr(text2speech_tab.sidebar_name), text2speech_tab),
         (tr(sentence_similarity_tab.sidebar_name), sentence_similarity_tab),
+        (tr(speech2text_tab.sidebar_name), speech2text_tab),
     ]
 )
 

tabs/sentence_similarity_tab.py

@@ -380,12 +380,12 @@ def run():
 
     model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
     embeddings = model.encode(sentences)
-    st.write("Transformation de chaque phrase en vecteur (dimension = 384 ):")
+    st.write(tr("Transformation de chaque phrase en vecteur (dimension = 384 ):"))
     st.write(embeddings)
     st.write("")
     # Calculate cosine similarity between the two sentences
     similarity = cosine_similarity([embeddings[0]], [embeddings[1]])
-    st.write(f"Cosine similarity comprise entre 0 et 1: {similarity[0][0]}")
+    st.write(f"**Cosine similarity** comprise entre 0 et 1: {similarity[0][0]}")
     st.write("")
     st.write("")
     st.write("")

tabs/{text2speech_tab.py → speech2text_tab.py}

@@ -17,8 +17,8 @@ if st.session_state.Cloud == 0:
 # import time
 # import random
 
-title = "Test 2 Speech"
-sidebar_name = "Text 2 Speech"
+title = "Speech 2 Text"
+sidebar_name = "Speech 2 Speech"
 dataPath = st.session_state.DataPath
 '''
 # Indiquer si l'on veut enlever les stop words. C'est un processus long