Update app.py

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
 import torch
-import logging
 import gc
 import time
+import logging
 from transformers import (
     pipeline,
     AutoProcessor,
@@ -12,548 +12,243 @@ from transformers import (
     WhisperProcessor,
 )
 
-# Try to import flash attention capability (only relevant for some seq2seq models)
-try:
-    from transformers.utils import is_flash_attn_2_available
-    FLASH_ATTN_AVAILABLE = True
-except Exception:
-    FLASH_ATTN_AVAILABLE = False
-    def is_flash_attn_2_available():
-        return False
-
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
 class MultiASRApp:
-    """
-    Supports BOTH:
-      - Whisper / seq2seq ASR (openai/whisper-*, fine-tuned whisper)
-      - XLS-R / Wav2Vec2 CTC ASR (e.g., ilsp/xls-r-greek-cretan)
-    """
-
     def __init__(self):
         self.pipe = None
         self.current_model = None
         self.current_kind = None  # "whisper" | "ctc"
 
         self.available_models = [
-            "openai/whisper-tiny",
-            "openai/whisper-base",
             "openai/whisper-small",
             "openai/whisper-medium",
-            "openai/whisper-large-v2",
-            "openai/whisper-large-v3",
             "ilsp/whisper_greek_dialect_of_lesbos",
             "ilsp/xls-r-greek-cretan",
         ]
 
-    # ----------------------------
-    # Model classification
-    # ----------------------------
-    def detect_model_kind(self, model_name: str) -> str:
-        """
-        Decide which loading path to use.
-        - Whisper models -> seq2seq
-        - XLS-R / wav2vec2 CTC -> ctc
-        """
+    # ------------------------
+    # Model detection
+    # ------------------------
+    def detect_model_kind(self, model_name):
         name = model_name.lower()
-
-        # Your known XLS-R model:
         if "xls-r" in name or "xlsr" in name:
             return "ctc"
-
-        # Heuristic: Whisper is usually named whisper
-        if "whisper" in name:
-            return "whisper"
-
-        # Fallback: try whisper first (safer for your list), else ctc
         return "whisper"
 
-    def is_fine_tuned_whisper(self, model_name: str) -> bool:
-        """
-        Fine-tuned whisper models may need conservative settings.
-        (This is NOT for XLS-R.)
-        """
-        n = model_name.lower()
-        indicators = ["ilsp/", "dialect", "fine", "custom"]
-        return any(x in n for x in indicators) and ("whisper" in n)
+    def is_fine_tuned_whisper(self, model_name):
+        return "ilsp/" in model_name.lower() and "whisper" in model_name.lower()
 
-    # ----------------------------
-    # Pipeline creation
-    # ----------------------------
-    def _pick_device_and_dtype(self, kind: str, conservative: bool):
+    # ------------------------
+    # Device & dtype
+    # ------------------------
+    def pick_device(self, conservative=True):
         if torch.cuda.is_available():
-            device = "cuda:0"
-            # For CTC, fp16 can work, but fp32 is often safer across community models.
-            if kind == "ctc":
-                torch_dtype = torch.float32
-            else:
-                torch_dtype = torch.float32 if conservative else torch.float16
-        else:
-            device = "cpu"
-            torch_dtype = torch.float32
-        return device, torch_dtype
+            return "cuda:0", torch.float32 if conservative else torch.float16
+        return "cpu", torch.float32
 
-    def create_whisper_pipe(self, model_name: str, use_flash_attention: bool = True):
+    # ------------------------
+    # Pipeline creation
+    # ------------------------
+    def create_whisper_pipe(self, model_name):
         conservative = self.is_fine_tuned_whisper(model_name)
-        device, torch_dtype = self._pick_device_and_dtype("whisper", conservative)
-
-        logger.info(f"[WHISPER] Loading {model_name} on {device} dtype={torch_dtype} conservative={conservative}")
-
-        # Flash attention is only meaningful for some GPU seq2seq configs
-        attn_implementation = "eager"
-        if (
-            use_flash_attention
-            and not conservative
-            and FLASH_ATTN_AVAILABLE
-            and is_flash_attn_2_available()
-            and torch.cuda.is_available()
-        ):
-            attn_implementation = "flash_attention_2"
-            logger.info("[WHISPER] Using flash_attention_2")
-
-        # Some fine-tuned repos are saved as WhisperForConditionalGeneration; others as generic SpeechSeq2Seq
+        device, dtype = self.pick_device(conservative)
+
         try:
             model = WhisperForConditionalGeneration.from_pretrained(
                 model_name,
-                torch_dtype=torch_dtype,
+                torch_dtype=dtype,
                 low_cpu_mem_usage=True,
-                cache_dir="./cache",
             )
-            processor = WhisperProcessor.from_pretrained(model_name, cache_dir="./cache")
-        except Exception as e:
-            logger.info(f"[WHISPER] WhisperForConditionalGeneration load failed ({e}); trying AutoModelForSpeechSeq2Seq")
+            processor = WhisperProcessor.from_pretrained(model_name)
+        except Exception:
             model = AutoModelForSpeechSeq2Seq.from_pretrained(
                 model_name,
-                torch_dtype=torch_dtype,
+                torch_dtype=dtype,
                 low_cpu_mem_usage=True,
-                use_safetensors=not conservative,
-                attn_implementation=attn_implementation,
-                cache_dir="./cache",
             )
-            processor = AutoProcessor.from_pretrained(model_name, cache_dir="./cache")
+            processor = AutoProcessor.from_pretrained(model_name)
 
         model.to(device)
 
         return pipeline(
-            task="automatic-speech-recognition",
+            "automatic-speech-recognition",
             model=model,
             tokenizer=processor.tokenizer,
             feature_extractor=processor.feature_extractor,
             device=device,
-            torch_dtype=torch_dtype,
-            # chunking is supported for whisper
-            chunk_length_s=30 if conservative else None,
+            torch_dtype=dtype,
+            chunk_length_s=30,
         )
 
-    def create_ctc_pipe(self, model_name: str):
-        """
-        XLS-R / Wav2Vec2 CTC path.
-        Key differences:
-          - AutoModelForCTC
-          - No generate_kwargs (CTC decoding)
-          - Timestamps are typically NOT supported in the same way as Whisper chunks
-        """
-        device, torch_dtype = self._pick_device_and_dtype("ctc", conservative=True)
-
-        logger.info(f"[CTC] Loading {model_name} on {device} dtype={torch_dtype}")
+    def create_ctc_pipe(self, model_name):
+        device, dtype = self.pick_device(conservative=True)
 
-        processor = AutoProcessor.from_pretrained(model_name, cache_dir="./cache")
+        processor = AutoProcessor.from_pretrained(model_name)
         model = AutoModelForCTC.from_pretrained(
             model_name,
-            torch_dtype=torch_dtype,
+            torch_dtype=dtype,
             low_cpu_mem_usage=True,
-            cache_dir="./cache",
         )
         model.to(device)
 
-        # Pipeline can take tokenizer + feature_extractor if present
-        tokenizer = getattr(processor, "tokenizer", None)
-        feature_extractor = getattr(processor, "feature_extractor", None)
-
         return pipeline(
-            task="automatic-speech-recognition",
+            "automatic-speech-recognition",
             model=model,
-            tokenizer=tokenizer,
-            feature_extractor=feature_extractor,
+            tokenizer=getattr(processor, "tokenizer", None),
+            feature_extractor=getattr(processor, "feature_extractor", None),
             device=device,
-            torch_dtype=torch_dtype,
-            # For long audio, CTC pipelines can also chunk; keep conservative defaults.
+            torch_dtype=dtype,
             chunk_length_s=20,
-            stride_length_s=(4, 2),  # helps continuity between chunks
+            stride_length_s=(4, 2),
         )
 
-    def create_pipe(self, model_name: str, use_flash_attention: bool = True):
-        kind = self.detect_model_kind(model_name)
-        if kind == "ctc":
-            return self.create_ctc_pipe(model_name), "ctc"
-        else:
-            # Disable flash attention automatically for fine-tuned whisper
-            if self.is_fine_tuned_whisper(model_name):
-                use_flash_attention = False
-            return self.create_whisper_pipe(model_name, use_flash_attention=use_flash_attention), "whisper"
-
-    # ----------------------------
-    # Load / unload
-    # ----------------------------
-    def clear_model(self):
-        if self.pipe is not None:
-            try:
-                del self.pipe
-            except Exception:
-                pass
-            self.pipe = None
-            self.current_model = None
-            self.current_kind = None
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        gc.collect()
-
-    def load_model(self, model_name: str, use_flash_attention: bool = True) -> bool:
+    def load_model(self, model_name):
         if self.current_model == model_name and self.pipe is not None:
-            logger.info("Model already loaded")
             return True
 
-        logger.info(f"Loading new model: {model_name}")
         self.clear_model()
+        kind = self.detect_model_kind(model_name)
 
         try:
-            pipe, kind = self.create_pipe(model_name, use_flash_attention=use_flash_attention)
-            self.pipe = pipe
+            if kind == "ctc":
+                self.pipe = self.create_ctc_pipe(model_name)
+            else:
+                self.pipe = self.create_whisper_pipe(model_name)
+
             self.current_model = model_name
             self.current_kind = kind
-            logger.info(f"Loaded {model_name} as {kind}")
             return True
         except Exception as e:
-            logger.error(f"Error loading model {model_name}: {e}", exc_info=True)
+            logger.error(e)
             self.clear_model()
             return False
 
-    # ----------------------------
-    # Transcription
-    # ----------------------------
-    def transcribe_audio(
-        self,
-        audio_file,
-        model_name="openai/whisper-small",
-        language="Automatic Detection",
-        task="transcribe",
-        chunk_length_s=30,
-        batch_size=4,
-        use_flash_attention=False,
-        return_timestamps=True,
-    ):
-        if audio_file is None:
-            return "Please upload an audio file", "", ""
-
-        start_time = time.time()
-
-        ok = self.load_model(model_name, use_flash_attention=use_flash_attention)
-        if not ok:
-            return "Failed to load model", "", "Failed to load model"
-
-        kind = self.current_kind or self.detect_model_kind(model_name)
+    def clear_model(self):
+        if self.pipe is not None:
+            del self.pipe
+        self.pipe = None
+        self.current_model = None
+        self.current_kind = None
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
 
-        try:
-            if kind == "ctc":
-                # XLS-R / CTC: no generate_kwargs; timestamps usually not available as chunks.
-                if return_timestamps:
-                    ts_note = (
-                        "=== TIMESTAMPS ===\n"
-                        "Timestamps are not provided for XLS-R/CTC models in this demo.\n"
-                    )
-                else:
-                    ts_note = "=== TIMESTAMPS ===\nDisabled.\n"
-
-                # For CTC, use chunk settings already set in the pipeline; batch_size works but keep conservative
-                out = self.pipe(
-                    audio_file,
-                    batch_size=min(int(batch_size), 4),
-                )
-                text = out.get("text", "") if isinstance(out, dict) else str(out)
-
-                total = time.time() - start_time
-                details = self._format_detailed_output(
-                    transcription=text,
-                    model_name=model_name,
-                    language=language,
-                    task=task,
-                    transcription_time=total,
-                    chunk_length_s=chunk_length_s,
-                    batch_size=batch_size,
-                    use_flash_attention=False,
-                    num_chunks=0,
-                    model_kind="XLS-R / CTC",
-                    timestamps_supported=False,
-                )
-                return text.strip(), ts_note, details
-
-            # ---------------- Whisper / seq2seq ----------------
-            generate_kwargs = {}
-
-            if language != "Automatic Detection" and not model_name.endswith(".en"):
-                language_map = {
-                    "Greek": "greek",
-                    "English": "english",
-                    "Spanish": "spanish",
-                    "French": "french",
-                    "German": "german",
-                    "Italian": "italian",
-                }
-                generate_kwargs["language"] = language_map.get(language, language.lower())
-
-            if not model_name.endswith(".en"):
-                generate_kwargs["task"] = task
-
-            # Fine-tuned whisper: more conservative runtime params
-            conservative = self.is_fine_tuned_whisper(model_name)
-            if conservative:
-                chunk_length_s = min(int(chunk_length_s), 30)
-                batch_size = min(int(batch_size), 2)
-                # more deterministic defaults
-                generate_kwargs.update({
-                    "do_sample": False,
-                    "num_beams": 1,
-                    "max_length": 448,
-                })
-
-            out = self.pipe(
-                audio_file,
-                chunk_length_s=int(chunk_length_s),
-                batch_size=int(batch_size),
-                generate_kwargs=generate_kwargs,
-                return_timestamps=bool(return_timestamps),
+    # ------------------------
+    # Transcription
+    # ------------------------
+    def transcribe(self, audio, model_name, return_timestamps):
+        if audio is None:
+            return "Ανέβασε ένα ηχητικό αρχείο.", "", ""
+
+        start = time.time()
+        if not self.load_model(model_name):
+            return "Σφάλμα φόρτωσης μοντέλου.", "", ""
+
+        if self.current_kind == "ctc":
+            result = self.pipe(audio)
+            text = result.get("text", "")
+
+            timestamps = (
+                "Οι χρονικές σημάνσεις δεν υποστηρίζονται για αυτό το μοντέλο."
+                if return_timestamps
+                else ""
             )
 
-            total = time.time() - start_time
-            text = out.get("text", "") if isinstance(out, dict) else str(out)
-            chunks = out.get("chunks", []) if isinstance(out, dict) else []
-
-            ts_text = ""
-            if return_timestamps:
-                ts_text = self._format_timestamps(chunks) if chunks else "=== TIMESTAMPS ===\nNo chunks returned.\n"
-            else:
-                ts_text = "=== TIMESTAMPS ===\nDisabled.\n"
-
-            details = self._format_detailed_output(
-                transcription=text,
-                model_name=model_name,
-                language=language,
-                task=task,
-                transcription_time=total,
-                chunk_length_s=chunk_length_s,
-                batch_size=batch_size,
-                use_flash_attention=use_flash_attention and not conservative,
-                num_chunks=len(chunks),
-                model_kind="Whisper / Seq2Seq" + (" (fine-tuned)" if conservative else ""),
-                timestamps_supported=True,
+        else:
+            result = self.pipe(
+                audio,
+                return_timestamps=return_timestamps,
             )
+            text = result.get("text", "")
+            timestamps = self.format_timestamps(result.get("chunks", []))
 
-            return text.strip(), ts_text, details
+        details = (
+            f"Μοντέλο: {model_name}\n"
+            f"Χρόνος επεξεργασίας: {time.time() - start:.2f} δευτ."
+        )
 
-        except Exception as e:
-            logger.error(f"Transcription error: {e}", exc_info=True)
-            msg = f"Transcription error: {str(e)}"
-            return msg, "", msg
-
-    # ----------------------------
-    # Formatting helpers
-    # ----------------------------
-    def _format_timestamps(self, chunks):
-        txt = "=== TIMESTAMPS ===\n"
-        for i, ch in enumerate(chunks or []):
-            try:
-                ts = ch.get("timestamp", None)
-                t = ch.get("text", "")
-                if isinstance(ts, (list, tuple)) and len(ts) >= 2 and ts[0] is not None and ts[1] is not None:
-                    txt += f"[{float(ts[0]):.1f}s - {float(ts[1]):.1f}s]: {t}\n"
-                else:
-                    txt += f"[Chunk {i}]: {t}\n"
-            except Exception as e:
-                txt += f"[Chunk {i} error]: {e}\n"
-        return txt
-
-    def _format_detailed_output(
-        self,
-        transcription,
-        model_name,
-        language,
-        task,
-        transcription_time,
-        chunk_length_s,
-        batch_size,
-        use_flash_attention,
-        num_chunks,
-        model_kind,
-        timestamps_supported,
-    ):
-        out = "=== TRANSCRIPTION ===\n"
-        out += f"{transcription}\n\n"
-
-        out += "=== MODEL INFORMATION ===\n"
-        out += f"Model: {model_name}\n"
-        out += f"Kind: {model_kind}\n"
-        out += f"Language setting: {language}\n"
-        out += f"Task: {task}\n"
-        out += f"Processing time: {transcription_time:.2f} seconds\n"
-        out += f"Chunks: {num_chunks}\n"
-        out += f"Timestamps supported: {'Yes' if timestamps_supported else 'No'}\n"
-
-        out += "\n=== SETTINGS ===\n"
-        out += f"Chunk length (UI): {chunk_length_s} seconds\n"
-        out += f"Batch size (UI): {batch_size}\n"
-        out += f"Flash Attention: {'Enabled' if use_flash_attention else 'Disabled'}\n"
+        return text.strip(), timestamps, details
+
+    def format_timestamps(self, chunks):
+        if not chunks:
+            return ""
+        out = ""
+        for c in chunks:
+            ts = c.get("timestamp")
+            if ts and ts[0] is not None and ts[1] is not None:
+                out += f"[{ts[0]:.1f}–{ts[1]:.1f}] {c.get('text','')}\n"
         return out
 
-    def get_model_info(self):
-        if self.pipe is None:
-            return "No model loaded"
-        try:
-            device = next(self.pipe.model.parameters()).device
-            dtype = next(self.pipe.model.parameters()).dtype
-            return f"✅ {self.current_model} ({self.current_kind}) - {device} ({dtype})"
-        except Exception:
-            return f"✅ {self.current_model} ({self.current_kind}) loaded"
-
-
-# ----------------------------
-# Gradio wiring
-# ----------------------------
-logger.info("Initializing Multi ASR App...")
-asr_app = MultiASRApp()
-
-
-def transcribe_wrapper(audio, model_name, language, task, chunk_length_s,
-                      batch_size, use_flash_attention, return_timestamps):
-    return asr_app.transcribe_audio(
-        audio_file=audio,
-        model_name=model_name,
-        language=language,
-        task=task,
-        chunk_length_s=chunk_length_s,
-        batch_size=batch_size,
-        use_flash_attention=use_flash_attention,
-        return_timestamps=return_timestamps,
+    def status(self):
+        if not self.current_model:
+            return "Δεν έχει φορτωθεί μοντέλο"
+        return f"✔ {self.current_model}"
+
+
+# ------------------------
+# App
+# ------------------------
+app = MultiASRApp()
+
+def run(audio, model, timestamps):
+    return app.transcribe(audio, model, timestamps)
+
+def status():
+    return app.status()
+
+
+with gr.Blocks(title="Ίντα λαλείς;", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+# Ίντα λαλείς;
+## Η Τεχνητή Νοημοσύνη μαθαίνει ελληνικές διαλέκτους
+
+🎧 Ανέβασε ένα ηχητικό αρχείο και δες πώς η Τεχνητή Νοημοσύνη
+αναγνωρίζει την ελληνική γλώσσα και τις διαλέκτους της.
+
+📍 Athens Science Festival 2025  
+🏛 Ωδείο Αθηνών | 18–21 Δεκεμβρίου 2025
+"""
     )
 
+    model_status = gr.Textbox(label="Κατάσταση μοντέλου", value=status(), interactive=False)
 
-def get_model_status():
-    return asr_app.get_model_info()
-
-
-def update_settings_for_model(model_name):
-    kind = asr_app.detect_model_kind(model_name)
-    if kind == "ctc":
-        # XLS-R recommendations
-        return {
-            "batch_size": gr.update(value=1, maximum=4),
-            "use_flash_attention": gr.update(value=False),
-            "chunk_length_s": gr.update(value=20),
-            "return_timestamps": gr.update(value=False),
-        }
-    else:
-        # Whisper recommendations (fine-tuned whisper: conservative)
-        conservative = asr_app.is_fine_tuned_whisper(model_name)
-        return {
-            "batch_size": gr.update(value=1 if conservative else 4, maximum=2 if conservative else 16),
-            "use_flash_attention": gr.update(value=False),
-            "chunk_length_s": gr.update(value=30),
-            "return_timestamps": gr.update(value=True),
-        }
-
-
-def create_interface():
-    with gr.Blocks(title="Multi-ASR (Whisper + XLS-R)", theme=gr.themes.Soft()) as interface:
-        gr.Markdown(
-            """
-            # 🚀 Multi-ASR Demo (Whisper + XLS-R)
-
-            This app supports:
-            - **Whisper** models (seq2seq) incl. fine-tuned dialect Whisper
-            - **XLS-R** models (CTC) e.g. **ilsp/xls-r-greek-cretan**
-
-            Notes:
-            - Whisper can return chunk timestamps.
-            - XLS-R/CTC typically **does not** return timestamps in this pipeline setup.
-            """
-        )
+    with gr.Row():
+        with gr.Column():
+            audio = gr.Audio(label="🎵 Ανέβασε ηχητικό αρχείο", type="filepath")
 
-        model_status = gr.Textbox(value=get_model_status(), label="🔧 Current Model Status", interactive=False)
-
-        with gr.Row():
-            with gr.Column():
-                audio_input = gr.Audio(label="🎵 Upload Audio File", type="filepath")
-
-                model_dropdown = gr.Dropdown(
-                    choices=asr_app.available_models,
-                    value="openai/whisper-small",
-                    label="Model",
-                    info="Automatically switches loading path (Whisper vs XLS-R/CTC).",
-                )
-
-                with gr.Row():
-                    language_dropdown = gr.Dropdown(
-                        choices=["Automatic Detection", "Greek", "English", "Spanish", "French", "German", "Italian"],
-                        value="Automatic Detection",
-                        label="Language (Whisper only)",
-                    )
-                    task_dropdown = gr.Dropdown(
-                        choices=["transcribe", "translate"],
-                        value="transcribe",
-                        label="Task (Whisper only)",
-                    )
-
-                with gr.Accordion("Advanced Settings", open=False):
-                    chunk_length_s = gr.Slider(10, 60, value=30, step=5, label="Chunk Length (seconds)")
-                    batch_size = gr.Slider(1, 16, value=4, step=1, label="Batch Size")
-                    use_flash_attention = gr.Checkbox(label="Flash Attention 2 (Whisper only)", value=False)
-                    return_timestamps = gr.Checkbox(label="Return Timestamps (Whisper only)", value=True)
-
-                transcribe_btn = gr.Button("🚀 Transcribe", variant="primary", size="lg")
-
-            with gr.Column():
-                transcription_output = gr.Textbox(label="Transcription", lines=8, show_copy_button=True)
-
-                with gr.Accordion("Timestamps", open=False):
-                    timestamps_output = gr.Textbox(label="Timestamp Information", lines=10, show_copy_button=True)
-
-                with gr.Accordion("Detailed Information", open=False):
-                    detailed_output = gr.Textbox(label="Processing Details & Model Info", lines=15, show_copy_button=True)
-
-        transcribe_btn.click(
-            fn=transcribe_wrapper,
-            inputs=[
-                audio_input,
-                model_dropdown,
-                language_dropdown,
-                task_dropdown,
-                chunk_length_s,
-                batch_size,
-                use_flash_attention,
-                return_timestamps,
-            ],
-            outputs=[transcription_output, timestamps_output, detailed_output],
-            show_progress=True,
-        )
+            model = gr.Dropdown(
+                choices=app.available_models,
+                value="openai/whisper-small",
+                label="Μοντέλο αναγνώρισης ομιλίας",
+            )
 
-        # When model changes, auto-tune UI controls
-        def on_model_change(m):
-            rec = update_settings_for_model(m)
-            kind = asr_app.detect_model_kind(m)
-            status = f"Model will load on next transcription ({'XLS-R/CTC' if kind=='ctc' else 'Whisper'})"
-            return status, rec["batch_size"], rec["use_flash_attention"], rec["chunk_length_s"], rec["return_timestamps"]
-
-        model_dropdown.change(
-            fn=on_model_change,
-            inputs=[model_dropdown],
-            outputs=[model_status, batch_size, use_flash_attention, chunk_length_s, return_timestamps],
-        )
+            timestamps = gr.Checkbox(label="Χρονικές σημάνσεις", value=True)
 
-    return interface
+            btn = gr.Button("🗣️ Μετατροπή ομιλίας σε κείμενο", variant="primary")
 
+        with gr.Column():
+            text_out = gr.Textbox(label="📄 Κείμενο", lines=8, show_copy_button=True)
+            ts_out = gr.Textbox(label="Χρονικές σημάνσεις", lines=8)
+            info_out = gr.Textbox(label="Πληροφορίες", lines=4)
+
+    btn.click(
+        run,
+        inputs=[audio, model, timestamps],
+        outputs=[text_out, ts_out, info_out],
+    )
+
+    model.change(lambda _: status(), outputs=model_status)
+
+    gr.Markdown(
+        """
+🔬 Έρευνα & τεχνολογία για τη γλωσσική ποικιλία  
+🎙️ Η φωνή ως πολιτιστική κληρονομιά
+"""
+    )
 
 if __name__ == "__main__":
-    interface = create_interface()
-    interface.launch(share=True)
+    demo.launch()