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Speech-to-Pictograms

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cecilemacaire commited on Jul 7

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765d42b

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1 Parent(s): afec4d5

Create app.py

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app.py +173 -0

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import pandas as pd
+from fpdf import FPDF
+import whisper
+import tempfile
+from st_audiorec import st_audiorec
+import numpy as np
+# Interface utilisateur
+st.set_page_config(
+    page_title="Traduction de la parole en pictogrammes ARASAAC",
+    page_icon="📝",
+    layout="wide"
+)
+# Charger le modèle et le tokenizer
+checkpoint = "Propicto/t2p-nllb-200-distilled-600M-all"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+# Charger le modèle Whisper
+whisper_model = whisper.load_model("base")
+# Lire le lexique
+@st.cache_data
+def read_lexicon(lexicon):
+    df = pd.read_csv(lexicon, sep='\t')
+    df['keyword_no_cat'] = df['lemma'].str.split(' #').str[0].str.strip().str.replace(' ', '_')
+    return df
+lexicon = read_lexicon("lexicon.csv")
+# Processus de sortie de la traduction
+def process_output_trad(pred):
+    return pred.split()
+def get_id_picto_from_predicted_lemma(df_lexicon, lemma):
+    if lemma.endswith("!"):
+        lemma = lemma[:-1]
+    id_picto = df_lexicon.loc[df_lexicon['keyword_no_cat'] == lemma, 'id_picto'].tolist()
+    return (id_picto[0], lemma) if id_picto else (0, lemma)
+# Génération du contenu HTML pour afficher les pictogrammes
+def generate_html(ids):
+    html_content = '<html><head><style>'
+    html_content += '''
+        figure {
+            display: inline-block;
+            text-align: center;
+            font-family: Arial, sans-serif;
+            margin: 0;
+        }
+        figcaption {
+            color: black;
+            background-color: white;
+            border-radius: 5px;
+        }
+        img {
+            background-color: white;
+            margin: 0;
+            padding: 0;
+            border-radius: 6px;
+        }
+    '''
+    html_content += '</style></head><body>'
+    for picto_id, lemma in ids:
+        if picto_id != 0:  # ignore invalid IDs
+            img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
+            html_content += f'''
+            <figure>
+                <img src="{img_url}" alt="{lemma}" width="200" height="200"/>
+                <figcaption>{lemma}</figcaption>
+            </figure>
+            '''
+    html_content += '</body></html>'
+    return html_content
+# Génération du PDF
+def generate_pdf(ids):
+    pdf = FPDF(orientation='L', unit='mm', format='A4')  # 'L' for landscape orientation
+    pdf.add_page()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    # Start positions
+    x_start = 10
+    y_start = 10
+    img_width = 50
+    img_height = 50
+    spacing = 1
+    max_width = 297  # A4 landscape width in mm
+    current_x = x_start
+    current_y = y_start
+    for picto_id, lemma in ids:
+        if picto_id != 0:  # ignore invalid IDs
+            img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
+            pdf.image(img_url, x=current_x, y=current_y, w=img_width, h=img_height)
+            pdf.set_xy(current_x, current_y + img_height + 5)
+            pdf.set_font("Arial", size=12)
+            pdf.cell(img_width, 10, txt=lemma, ln=1, align='C')
+            current_x += img_width + spacing
+            # Move to the next line if exceeds max width
+            if current_x + img_width > max_width:
+                current_x = x_start
+                current_y += img_height + spacing + 10  # Adjust for image height and some spacing
+    pdf_path = "pictograms.pdf"
+    pdf.output(pdf_path)
+    return pdf_path
+# Initialiser l'état de session
+if 'transcription' not in st.session_state:
+    st.session_state['transcription'] = None
+if 'pictogram_ids' not in st.session_state:
+    st.session_state['pictogram_ids'] = None
+if 'previous_audio_file' not in st.session_state:
+    st.session_state['previous_audio_file'] = None
+# Interface utilisateur pour l'audio et le bouton de téléchargement
+st.title("Traduction de la parole en pictogrammes ARASAAC")
+col1, col2 = st.columns(2)
+with col1:
+    audio_file = st.file_uploader("Ajouter un fichier audio :", type=["wav", "mp3"])
+    # Réinitialiser les informations si le fichier audio change
+    if audio_file is not None and audio_file != st.session_state['previous_audio_file']:
+        st.session_state['transcription'] = None
+        st.session_state['pictogram_ids'] = None
+        st.session_state['previous_audio_file'] = audio_file
+with col2:
+    if audio_file is not None:
+        with st.spinner("Transcription de l'audio en cours..."):
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+                temp_file.write(audio_file.read())
+                temp_file_path = temp_file.name
+            transcription = whisper_model.transcribe(temp_file_path, language='fr')
+            if 'transcription' in locals():
+                st.text_area("Transcription :", transcription['text'])
+                st.session_state['transcription'] = transcription['text']
+if st.session_state['transcription'] is not None:
+    inputs = tokenizer(transcription['text'].lower(), return_tensors="pt").input_ids
+    outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
+    pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    sentence_to_map = process_output_trad(pred)
+    pictogram_ids = [get_id_picto_from_predicted_lemma(lexicon, lemma) for lemma in sentence_to_map]
+    st.session_state['pictogram_ids'] = [get_id_picto_from_predicted_lemma(lexicon, lemma) for lemma in sentence_to_map]
+if st.session_state['pictogram_ids'] is not None:
+    html = generate_html(st.session_state['pictogram_ids'])
+    st.components.v1.html(html, height=500, scrolling=True)
+    # Container to hold the download button
+    pdf_path = generate_pdf(st.session_state['pictogram_ids'])
+    with open(pdf_path, "rb") as pdf_file:
+        st.download_button(label="Télécharger la traduction en PDF", data=pdf_file, file_name="pictograms.pdf", mime="application/pdf")
+# record_audio = st_audiorec()
+# if record_audio:
+#     audio = np.array(record_audio)
+#     transcription = whisper_model.transcribe(audio, language='fr')
+#     st.success("Enregistrement terminé !")