Update app.py

app.py

@@ -21,8 +21,8 @@ import torch
 import os
 import traceback
 import shutil
-import re  # Added for timestamp feature
-import uuid # Added for timestamp feature
+import re  
+import uuid 
 
 from vibevoice.modular.configuration_vibevoice import VibeVoiceConfig
 from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference, VibeVoiceGenerationOutput
@@ -41,7 +41,7 @@ def drive_save(file_copy):
         print("Running on Google Colab and auto-saving to Google Drive...")
         os.makedirs(save_folder, exist_ok=True)
         dest_path = os.path.join(save_folder, os.path.basename(file_copy))
-        shutil.copy2(file_copy, dest_path)  # preserves metadata
+        shutil.copy2(file_copy, dest_path)  
         print(f"File saved to: {dest_path}")
         return dest_path
     else:
@@ -52,12 +52,8 @@ import os, requests, urllib.request, urllib.error
 from tqdm.auto import tqdm
 
 def download_file(url, download_file_path, redownload=False):
-    """Download a single file with urllib + tqdm progress bar."""
-
     base_path = os.path.dirname(download_file_path)
     os.makedirs(base_path, exist_ok=True)
-
-    # skip logic
     if os.path.exists(download_file_path):
         if redownload:
             os.remove(download_file_path)
@@ -65,7 +61,6 @@ def download_file(url, download_file_path, redownload=False):
         elif os.path.getsize(download_file_path) > 0:
             tqdm.write(f"✔️ Skipped (already exists): {os.path.basename(download_file_path)}")
             return True
-
     try:
         request = urllib.request.urlopen(url)
         total = int(request.headers.get('Content-Length', 0))
@@ -73,7 +68,6 @@ def download_file(url, download_file_path, redownload=False):
         print(f"❌ Error: Unable to open URL: {url}")
         print(f"Reason: {e.reason}")
         return False
-
     with tqdm(total=total, desc=os.path.basename(download_file_path), unit='B', unit_scale=True, unit_divisor=1024) as progress:
         try:
             urllib.request.urlretrieve(
@@ -85,77 +79,54 @@ def download_file(url, download_file_path, redownload=False):
             print(f"❌ Error: Failed to download {url}")
             print(f"Reason: {e.reason}")
             return False
-
     tqdm.write(f"⬇️ Downloaded: {os.path.basename(download_file_path)}")
     return True
 
-
 def download_model(repo_id, download_folder="./", redownload=False):
-    # normalize empty string as current dir
     if not download_folder.strip():
         download_folder = "."
     url = f"https://huggingface.co/api/models/{repo_id}"
     download_dir = os.path.abspath(f"{download_folder.rstrip('/')}/{repo_id.split('/')[-1]}")
     os.makedirs(download_dir, exist_ok=True)
-
     print(f"📂 Download directory: {download_dir}")
-
     response = requests.get(url)
     if response.status_code != 200:
         print("❌ Error:", response.status_code, response.text)
         return None
-
     data = response.json()
     siblings = data.get("siblings", [])
     files = [f["rfilename"] for f in siblings]
-
     print(f"📦 Found {len(files)} files in repo '{repo_id}'. Checking cache ...")
-
     for file in tqdm(files, desc="Processing files", unit="file"):
         file_url = f"https://huggingface.co/{repo_id}/resolve/main/{file}"
         file_path = os.path.join(download_dir, file)
         download_file(file_url, file_path, redownload=redownload)
-
     return download_dir
 
-
-
-
-
-
-
-# NEW FEATURE: Function to generate unique filenames for output
 def generate_file_name(text):
-    """Generates a unique, clean filename based on the script's first line."""
     output_dir = "./podcast_audio"
     os.makedirs(output_dir, exist_ok=True)
-    # Clean the text to get a base for the filename
     cleaned = re.sub(r"^\s*speaker\s*\d+\s*:\s*", "", text, flags=re.IGNORECASE)
     short = cleaned[:30].strip()
     short = re.sub(r'[^a-zA-Z0-9\s]', '', short)
     short = short.lower().strip().replace(" ", "_")
     if not short:
         short = "podcast_output"
-    # Add a unique identifier
     unique_name = f"{short}_{uuid.uuid4().hex[:6]}"
-
     return os.path.join(output_dir, unique_name)
 
-
 class VibeVoiceDemo:
     def __init__(self, model_path: str, device: str = "cuda", inference_steps: int = 5):
-        """Initialize the VibeVoice demo with model loading."""
         self.model_path = model_path
         self.device = device
         self.inference_steps = inference_steps
-        self.is_generating = False  # Track generation state
-        self.stop_generation = False  # Flag to stop generation
+        self.is_generating = False  
+        self.stop_generation = False  
         self.load_model()
         self.setup_voice_presets()
-        self.load_example_scripts()  # Load example scripts
+        self.load_example_scripts()  
 
     def load_model(self):
-        """Load the VibeVoice model and processor."""
         print(f"Loading processor & model from {self.model_path}")
         self.processor = VibeVoiceProcessor.from_pretrained(self.model_path) 
         if self.device == "cuda":
@@ -167,10 +138,9 @@ class VibeVoiceDemo:
         else:
             self.model = VibeVoiceForConditionalGenerationInference.from_pretrained(
                 self.model_path,
-                torch_dtype=torch.float32,  # Use float32 for CPU
+                torch_dtype=torch.float32, 
                 device_map="cpu",
             )
-
         self.model.eval()
         self.model.model.noise_scheduler = self.model.model.noise_scheduler.from_config(
             self.model.model.noise_scheduler.config,
@@ -182,81 +152,62 @@ class VibeVoiceDemo:
             print(f"Language model attention: {self.model.model.language_model.config._attn_implementation}")
 
     def setup_voice_presets(self):
-        """Setup voice presets by scanning the voices directory."""
         voices_dir = os.path.join(os.path.dirname(__file__), "voices")
         if not os.path.exists(voices_dir):
             print(f"Warning: Voices directory not found at {voices_dir}, creating it.")
             os.makedirs(voices_dir, exist_ok=True)
         self.voice_presets = {}
-        audio_files = [f for f in os.listdir(voices_dir)
-                    if f.lower().endswith(('.wav', '.mp3', '.flac', '.ogg', '.m4a', '.aac')) and os.path.isfile(os.path.join(voices_dir, f))]
+        audio_files = [f for f in os.listdir(voices_dir) if f.lower().endswith(('.wav', '.mp3', '.flac', '.ogg', '.m4a', '.aac')) and os.path.isfile(os.path.join(voices_dir, f))]
         for audio_file in audio_files:
             name = os.path.splitext(audio_file)[0]
-            full_path = os.path.join(voices_dir, audio_file)
-            self.voice_presets[name] = full_path
+            self.voice_presets[name] = os.path.join(voices_dir, audio_file)
         self.voice_presets = dict(sorted(self.voice_presets.items()))
         self.available_voices = {name: path for name, path in self.voice_presets.items() if os.path.exists(path)}
-        if not self.available_voices:
-            print("Warning: No voice presets found.")
+        if not self.available_voices: print("Warning: No voice presets found.")
         print(f"Found {len(self.available_voices)} voice files in {voices_dir}")
 
     def read_audio(self, audio_path: str, target_sr: int = 24000) -> np.ndarray:
-        """Read and preprocess audio file."""
         try:
             wav, sr = sf.read(audio_path)
-            if len(wav.shape) > 1:
-                wav = np.mean(wav, axis=1)
-            if sr != target_sr:
-                wav = librosa.resample(wav, orig_sr=sr, target_sr=target_sr)
+            if len(wav.shape) > 1: wav = np.mean(wav, axis=1)
+            if sr != target_sr: wav = librosa.resample(wav, orig_sr=sr, target_sr=target_sr)
             return wav
         except Exception as e:
             print(f"Error reading audio {audio_path}: {e}")
             return np.array([])
 
     def trim_silence_from_numpy(self, audio_np: np.ndarray, sample_rate: int, silence_thresh: int = -45, min_silence_len: int = 100, keep_silence: int = 50) -> np.ndarray:
-        """Removes silence from a NumPy audio array using pydub."""
         audio_int16 = (audio_np * 32767).astype(np.int16)
-        sound = AudioSegment(
-            data=audio_int16.tobytes(),
-            sample_width=audio_int16.dtype.itemsize,
-            frame_rate=sample_rate,
-            channels=1
-        )
-        audio_chunks = split_on_silence(
-            sound, min_silence_len=min_silence_len, silence_thresh=silence_thresh, keep_silence=keep_silence
-        )
-        if not audio_chunks:
-            return np.array([0.0], dtype=np.float32)
-        
+        sound = AudioSegment(data=audio_int16.tobytes(), sample_width=audio_int16.dtype.itemsize, frame_rate=sample_rate, channels=1)
+        audio_chunks = split_on_silence(sound, min_silence_len=min_silence_len, silence_thresh=silence_thresh, keep_silence=keep_silence)
+        if not audio_chunks: return np.array([0.0], dtype=np.float32)
         combined = sum(audio_chunks)
         samples = np.array(combined.get_array_of_samples())
-        trimmed_audio_np = samples.astype(np.float32) / 32767.0
-        return trimmed_audio_np
+        return samples.astype(np.float32) / 32767.0
 
     def generate_podcast_with_timestamps(self,
                                  num_speakers: int,
                                  script: str,
-                                 speaker_1: str = None,
-                                 speaker_2: str = None,
-                                 speaker_3: str = None,
-                                 speaker_4: str = None,
-                                 cfg_scale: float = 1.3,
-                                 remove_silence: bool = False,
+                                 speaker_1: str, speaker_2: str, speaker_3: str, speaker_4: str,
+                                 cfg_scale: float,
+                                 remove_silence: bool,
                                  progress=gr.Progress()):
+        
+        # Initial UI state: Clear previous results, show stop button
+        yield None, None, None, gr.update(visible=False), gr.update(visible=True)
+
+        final_audio_path, final_json_path = None, None
         try:
             self.stop_generation = False
             self.is_generating = True
-
-            # --- Input Validation and Setup ---
+            
             if not script.strip(): raise gr.Error("Error: Please provide a script.")
             script = script.replace("’", "'")
             if not 1 <= num_speakers <= 4: raise gr.Error("Error: Number of speakers must be between 1 and 4.")
-
             selected_speakers = [speaker_1, speaker_2, speaker_3, speaker_4][:num_speakers]
             for i, speaker in enumerate(selected_speakers):
                 if not speaker or speaker not in self.available_voices:
                     raise gr.Error(f"Error: Please select a valid speaker for Speaker {i+1}.")
-
             voice_samples = [self.read_audio(self.available_voices[name]) for name in selected_speakers]
             if any(len(vs) == 0 for vs in voice_samples): raise gr.Error("Error: Failed to load one or more audio files.")
 
@@ -269,82 +220,68 @@ class VibeVoiceDemo:
                     formatted_script_lines.append(line)
                 else:
                     speaker_id = len(formatted_script_lines) % num_speakers
-                    formatted_script_lines.append(f"Speaker {speaker_id}: {line}")
+                    formatted_script_lines.append(f"Speaker {speaker_id+1}: {line}")
 
             if not formatted_script_lines: raise gr.Error("Error: Script is empty after formatting.")
 
-            # --- Prepare for Generation ---
             timestamps = {}
             current_time = 0.0
             sample_rate = 24000
-            total_lines = len(formatted_script_lines)
-
+            
             base_filename = generate_file_name(formatted_script_lines[0])
             final_audio_path = base_filename + ".wav"
             final_json_path = base_filename + ".json"
 
-            # --- Open file and write chunks sequentially (MEMORY EFFICIENT) ---
             with sf.SoundFile(final_audio_path, 'w', samplerate=sample_rate, channels=1, subtype='PCM_16') as audio_file:
                 for i, line in enumerate(formatted_script_lines):
                     if self.stop_generation:
+                        print("\n🚫 Generation interrupted by user. Finalizing partial files...")
                         break
-
-                    progress(i / total_lines, desc=f"Generating line {i+1}/{total_lines}")
-
+                    progress(i / len(formatted_script_lines), desc=f"Generating line {i+1}/{len(formatted_script_lines)}")
                     match = re.match(r'Speaker\s*(\d+):\s*(.*)', line, re.IGNORECASE)
                     if not match: continue
-
                     speaker_idx = int(match.group(1)) - 1
                     text_content = match.group(2).strip()
-
-                    if speaker_idx < 0 or speaker_idx >= len(voice_samples):
-                        continue
-
-                    inputs = self.processor(
-                        text=[line], voice_samples=[voice_samples], padding=True, return_tensors="pt"
-                    )
-
-                    output_waveform: VibeVoiceGenerationOutput = self.model.generate(
-                        **inputs, max_new_tokens=None, cfg_scale=cfg_scale, tokenizer=self.processor.tokenizer,
-                        generation_config={'do_sample': False}, verbose=False, refresh_negative=True
-                    )
-
+                    if not (0 <= speaker_idx < len(voice_samples)): continue
+                    
+                    inputs = self.processor(text=[line], voice_samples=[voice_samples[speaker_idx]], padding=True, return_tensors="pt")
+                    output_waveform = self.model.generate(**inputs, max_new_tokens=None, cfg_scale=cfg_scale, tokenizer=self.processor.tokenizer, generation_config={'do_sample': False}, verbose=False, refresh_negative=True)
                     audio_np = output_waveform.speech_outputs[0].cpu().float().numpy().squeeze()
-
-                    # NEW FEATURE: Remove silence if enabled
-                    if remove_silence:
-                        audio_np = self.trim_silence_from_numpy(audio_np, sample_rate)
-
+                    
+                    if remove_silence: audio_np = self.trim_silence_from_numpy(audio_np, sample_rate)
                     duration = len(audio_np) / sample_rate
-                    audio_int16 = (audio_np * 32767).astype(np.int16)
-                    audio_file.write(audio_int16)
-
-                    timestamps[str(i + 1)] = {
-                        "text": text_content, "speaker_id": speaker_idx+1,
-                        "start": current_time, "end": current_time + duration
-                    }
+                    audio_file.write((audio_np * 32767).astype(np.int16))
+                    timestamps[str(i + 1)] = {"text": text_content, "speaker_id": speaker_idx + 1, "start": current_time, "end": current_time + duration}
                     current_time += duration
 
-            # --- Finalize and Save JSON ---
-            progress(1.0, desc="Saving timestamp file...")
-            with open(final_json_path, "w") as f:
-                json.dump(timestamps, f, indent=2)
+            if not timestamps:
+                self.is_generating = False
+                if os.path.exists(final_audio_path): os.remove(final_audio_path)
+                yield None, None, None, gr.update(visible=True), gr.update(visible=False)
+                return
+
+            progress(1.0, desc="Saving generated files...")
+            with open(final_json_path, "w") as f: json.dump(timestamps, f, indent=2)
             try:
-              drive_save(final_audio_path)
-              drive_save(final_json_path)
-            except Exception as e:
-              print(f"Error saving files to Google Drive: {e}")
+                drive_save(final_audio_path)
+                drive_save(final_json_path)
+            except Exception as e: print(f"Error saving files to Google Drive: {e}")
 
-            print(f"\n✨ Generation successful!\n🎵 Audio: {final_audio_path}\n📄 Timestamps: {final_json_path}\n")
+            message = "Partial" if self.stop_generation else "Full"
+            print(f"\n✨ {message} generation successful!\n🎵 Audio: {final_audio_path}\n📄 Timestamps: {final_json_path}\n")
+            
             self.is_generating = False
-
-            return final_audio_path, final_audio_path, final_json_path, gr.update(visible=True), gr.update(visible=False)
+            yield final_audio_path, final_audio_path, final_json_path, gr.update(visible=True), gr.update(visible=False)
 
         except Exception as e:
             self.is_generating = False
             print(f"❌ An unexpected error occurred: {str(e)}")
             traceback.print_exc()
-            return None, None, None, gr.update(visible=True), gr.update(visible=False)
+            try:
+                if final_audio_path and os.path.exists(final_audio_path): os.remove(final_audio_path)
+                if final_json_path and os.path.exists(final_json_path): os.remove(final_json_path)
+            except Exception as cleanup_e: print(f"Error during cleanup after exception: {cleanup_e}")
+            yield None, None, None, gr.update(visible=True), gr.update(visible=False)
 
     def stop_audio_generation(self):
         if self.is_generating:
@@ -361,40 +298,26 @@ class VibeVoiceDemo:
                 with open(os.path.join(examples_dir, txt_file), 'r', encoding='utf-8') as f:
                     script = f.read().strip()
                 if script: self.example_scripts.append([self._get_num_speakers_from_script(script), script])
-            except Exception as e:
-                print(f"Error loading example {txt_file}: {e}")
+            except Exception as e: print(f"Error loading example {txt_file}: {e}")
 
     def _get_num_speakers_from_script(self, script: str) -> int:
         speakers = set(re.findall(r'^Speaker\s+(\d+)\s*:', script, re.MULTILINE | re.IGNORECASE))
         return max(int(s) for s in speakers) if speakers else 1
 
 def create_demo_interface(demo_instance: VibeVoiceDemo):
-    with gr.Blocks(
-        title="VibeVoice AI Podcast Generator"
-    ) as interface:
-
+    with gr.Blocks(title="VibeVoice AI Podcast Generator") as interface:
         gr.HTML("""
         <div style="text-align: center; margin: 20px auto; max-width: 800px;">
             <h1 style="font-size: 2.5em; margin-bottom: 10px;">🎙️ Vibe Podcasting</h1>
-            <p style="font-size: 1.2em; color: #555; margin-bottom: 15px;">
-                Generate Long-form Multi-speaker AI Podcasts with VibeVoice
-            </p>
-            <a href="https://colab.research.google.com/github/NeuralFalconYT/AI-Podcast-Generator/blob/main/VibeVoice_Colab.ipynb" 
-               target="_blank" 
-               style="display: inline-block; padding: 10px 20px; background-color: #4285F4; color: white; 
-                      border-radius: 6px; text-decoration: none; font-size: 1em;">
-                🥳 Run on Google Colab
-            </a>
+            <p style="font-size: 1.2em; color: #555; margin-bottom: 15px;">Generate Long-form Multi-speaker AI Podcasts with VibeVoice</p>
+            <a href="https://colab.research.google.com/github/NeuralFalconYT/AI-Podcast-Generator/blob/main/VibeVoice_Colab.ipynb" target="_blank" style="display: inline-block; padding: 10px 20px; background-color: #4285F4; color: white; border-radius: 6px; text-decoration: none; font-size: 1em;">🥳 Run on Google Colab</a>
         </div>
         """)
-
         with gr.Row():
-            # Left column - Settings
             with gr.Column(scale=1):
                 with gr.Group():
                     gr.Markdown("### 🎛️ Podcast Settings")
                     num_speakers = gr.Slider(minimum=1, maximum=4, value=2, step=1, label="Number of Speakers")
-
                     gr.Markdown("### 🎭 Speaker Selection")
                     speaker_selections = []
                     available_voices = list(demo_instance.available_voices.keys())
@@ -403,42 +326,34 @@ def create_demo_interface(demo_instance: VibeVoiceDemo):
                         val = defaults[i] if i < len(defaults) and defaults[i] in available_voices else None
                         speaker = gr.Dropdown(choices=available_voices, value=val, label=f"Speaker {i+1}", visible=(i < 2))
                         speaker_selections.append(speaker)
-
                     with gr.Accordion("🎤 Upload Custom Voices", open=False):
                         upload_audio = gr.File(label="Upload Voice Samples", file_count="multiple", file_types=["audio"])
                         process_upload_btn = gr.Button("Add Uploaded Voices to Speaker Selection")
-
                     with gr.Accordion("⚙️ Advanced Settings", open=False):
                         cfg_scale = gr.Slider(minimum=1.0, maximum=2.0, value=1.3, step=0.05, label="CFG Scale")
-                        # NEW FEATURE: Silence removal checkbox
                         remove_silence_checkbox = gr.Checkbox(label="Trim Silence from Podcast", value=False,)
-
-            # Right column - Generation
             with gr.Column(scale=2):
                 with gr.Group():
                     gr.Markdown("### 📝 Script Input")
-                    script_input = gr.Textbox(label="Conversation Script", placeholder="Enter script here...", lines=10)
-
+                    script_input = gr.Textbox(
+                          label="Conversation Script",
+                          placeholder="Speaker 1: Hi everyone, I’m Alex, and welcome back.\nSpeaker 2: And I’m lisa. Thanks for tuning in.",
+                          lines=10
+                      )
                     with gr.Row():
                         random_example_btn = gr.Button("🎲 Random Example", scale=1)
                         generate_btn = gr.Button("🚀 Generate Podcast", variant="primary", scale=2)
-
                     stop_btn = gr.Button("🛑 Stop Generation", variant="stop", visible=False)
-
                     gr.Markdown("### 🎵 **Generated Output**")
                     audio_output = gr.Audio(label="Play Generated Podcast")
                     with gr.Accordion("📦 Download Files", open=False):
                       download_file = gr.File(label="Download Audio File (.wav)")
                       json_file_output = gr.File(label="Download Timestamps (.json)")
 
-        with gr.Accordion("💡 Usage Tips & Examples", open=True):
-            gr.Markdown("""
-            - **Upload Your Own Voices:** Create your own podcast with custom voice samples.  
-            - **Timestamps:** Useful if you want to generate a video using Wan2.2 or other tools. The timestamps let you automatically separate each speaker (splitting the long podcast into smaller chunks), pass the audio clips to your video generation model, and then merge the generated video clips into a full podcast video (e.g., using FFmpeg + any video generation model such as image+audio → video).
-            """)
+        with gr.Accordion("💡 Usage Tips & Examples", open=False):
+            gr.Markdown("""- **Upload Your Own Voices:** Create your own podcast with custom voice samples.  \n- **Timestamps:** Useful if you want to generate a video using Wan2.2 or other tools. The timestamps let you automatically separate each speaker (splitting the long podcast into smaller chunks), pass the audio clips to your video generation model, and then merge the generated video clips into a full podcast video (e.g., using FFmpeg + any video generation model such as image+audio → video).""")
             gr.Examples(examples=demo_instance.example_scripts, inputs=[num_speakers, script_input], label="Try these example scripts:")
 
-        # --- Backend Functions ---
         def process_and_refresh_voices(uploaded_files):
             if not uploaded_files: return [gr.update() for _ in speaker_selections] + [None]
             voices_dir = os.path.join(os.path.dirname(__file__), "voices")
@@ -448,29 +363,24 @@ def create_demo_interface(demo_instance: VibeVoiceDemo):
             return [gr.update(choices=new_choices) for _ in speaker_selections] + [None]
 
         def update_speaker_visibility(num):
-            return [gr.update(visible=(i < num)) for i in range(4)]
-
-        def handle_generate_click():
-            return gr.update(visible=False), gr.update(visible=True)
+            return [gr.update(visible=(i < int(num))) for i in range(4)]
 
         num_speakers.change(fn=update_speaker_visibility, inputs=num_speakers, outputs=speaker_selections)
         process_upload_btn.click(fn=process_and_refresh_voices, inputs=upload_audio, outputs=speaker_selections + [upload_audio])
 
-        gen_event = generate_btn.click(
-            fn=handle_generate_click,
-            outputs=[generate_btn, stop_btn]
-        ).then(
+        generate_btn.click(
             fn=demo_instance.generate_podcast_with_timestamps,
             inputs=[num_speakers, script_input] + speaker_selections + [cfg_scale, remove_silence_checkbox],
             outputs=[audio_output, download_file, json_file_output, generate_btn, stop_btn],
         )
-
-        stop_btn.click(fn=demo_instance.stop_audio_generation, cancels=[gen_event])
+        
+        stop_btn.click(
+            fn=demo_instance.stop_audio_generation
+        )
 
         def load_random_example():
             import random
             return random.choice(demo_instance.example_scripts) if demo_instance.example_scripts else (2, "Speaker 0: No examples loaded.")
-
         random_example_btn.click(fn=load_random_example, outputs=[num_speakers, script_input])
 
     return interface
@@ -478,7 +388,6 @@ def create_demo_interface(demo_instance: VibeVoiceDemo):
 
 
 
-
 def build_conversation_prompt(topic, *speaker_names):
     """
     Generates the final prompt. It takes the topic and a variable number of speaker names.
@@ -512,7 +421,6 @@ def build_conversation_prompt(topic, *speaker_names):
     prompt = f"""
 You are a professional podcast scriptwriter. 
 Write a natural, engaging conversation between {num_speakers} speakers on the topic: "{topic}".
-
 {speaker_mapping_str}
 Formatting Rules:
 - You MUST always format dialogue with {', '.join(speaker_labels)} ONLY. 
@@ -521,7 +429,6 @@ Formatting Rules:
 {introductions_str}
 - During the conversation, they may occasionally mention each other's names ({', '.join(names)}) naturally in the dialogue, but the labels must remain unchanged.
 - Do not add narration, descriptions, or any extra formatting.
-
 {example_str}
 """
     return prompt
@@ -600,57 +507,26 @@ def ui2():
     return demo
 
 
+
 import click
 @click.command()
-@click.option(
-    "--model_path",
-    default="microsoft/VibeVoice-1.5B",
-    help="Hugging Face Model Repo ID."
-)
-@click.option(
-    "--inference_steps",
-    default=10,
-    show_default=True,
-    type=int,
-    help="Number of inference steps for generation."
-)
-@click.option(
-    "--debug",
-    is_flag=True,
-    default=False,
-    help="Enable debug mode."
-)
-@click.option(
-    "--share",
-    is_flag=True,
-    default=False,
-    help="Enable sharing of the interface."
-)
+@click.option("--model_path", default="microsoft/VibeVoice-1.5B", help="Hugging Face Model Repo ID.")
+@click.option("--inference_steps", default=10, show_default=True, type=int, help="Number of inference steps for generation.")
+@click.option("--debug", is_flag=True, default=False, help="Enable debug mode.")
+@click.option("--share", is_flag=True, default=False, help="Enable sharing of the interface.")
 def main(model_path, inference_steps, debug, share):
-    # model_path = "microsoft/VibeVoice-1.5B"
     # model_folder = download_model(model_path, download_folder="./", redownload=False)
     model_folder=model_path
     device = "cuda" if torch.cuda.is_available() else "cpu"
     set_seed(42)
-    print("🎙️ Initializing VibeVoice Demo with Timestamp Support...")
-    demo_instance = VibeVoiceDemo(
-        model_path=model_folder,
-        device=device,
-        inference_steps=inference_steps
-    )
-    
-    custom_css = """
-      .gradio-container {
-          font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, sans-serif;
-      }"""
+    print("🎙️ Initializing VibeVoice ...")
+    demo_instance = VibeVoiceDemo(model_path=model_folder, device=device, inference_steps=inference_steps)
+    custom_css = """.gradio-container { font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, sans-serif; }"""
     demo1 = create_demo_interface(demo_instance)
-    demo2=ui2()
+    demo2 = ui2()
     demo = gr.TabbedInterface([demo1, demo2],["Vibe Podcasting","Generate Sample Podcast Script"],title="",theme=gr.themes.Soft(),css=custom_css)
-
     print("🚀 Launching Gradio Demo...")
     demo.queue().launch(debug=debug, share=share)
 
 if __name__ == "__main__":
-    main()
-
-# !python /content/VibeVoice/demo/colab.py --model_path microsoft/VibeVoice-1.5B --inference_steps 10 --debug --share    
+    main()