app.py

from transformers import pipeline
import torch
import gradio as gr

# Identifiant du modèle sur Hugging Face
model_id = "ilyes25/wav2vec2-large-mms-1b-DZ"

# Détermine le device (GPU si disponible, sinon CPU)
device = 0 if torch.cuda.is_available() else -1
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Création du pipeline pour l'ASR
pipe = pipeline(
    task="automatic-speech-recognition",
    model=model_id,
    device=device,
    torch_dtype=torch_dtype,
    framework="pt"
)

LANGUAGES = {
    "Kabyle": "kab",
    "Arabe": "ar",
    "Français": "fr"
}

def transcribe_audio(audio_path, lang_name):
    lang = LANGUAGES[lang_name]
    pipe.model.load_adapter(lang)
    pipe.tokenizer.set_target_lang(lang)
    result = pipe(audio_path)
    return result["text"].replace("</s>", "").replace("<s>", "").strip()

# Interface Gradio avec deux onglets (microphone et upload)
with gr.Blocks() as app:
    gr.Markdown("## Transcription Speech-to-Text avec sélection de langue")
    with gr.Tabs():
        with gr.TabItem("Utiliser le Microphone"):
            mic_input = gr.Audio(sources="microphone", type="filepath", label="Enregistrez votre audio")
            lang_dropdown = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Langue", value="Kabyle")
            mic_output = gr.Textbox(label="Transcription")
            mic_button = gr.Button("Transcrire")
            mic_button.click(transcribe_audio, inputs=[mic_input, lang_dropdown], outputs=mic_output)
        
        with gr.TabItem("Téléverser un Fichier Audio"):
            file_input = gr.Audio(sources="upload", type="filepath", label="Téléversez votre audio")
            lang_dropdown_file = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Langue", value="Kabyle")
            file_output = gr.Textbox(label="Transcription")
            file_button = gr.Button("Transcrire")
            file_button.click(transcribe_audio, inputs=[file_input, lang_dropdown_file], outputs=file_output)

if __name__ == "__main__":
    app.launch()