Update app.py

app.py

@@ -39,6 +39,7 @@ def clean_text(text: str) -> str:
     """
     Removes undesired characters (e.g., asterisks) that might not be recognized by the model's vocabulary.
     """
+    # Remove all asterisks. You can add more cleaning steps here as needed.
     return re.sub(r'\*', '', text)
 
 # ---------------------------------------------------------------------
@@ -50,6 +51,7 @@ def get_llama_pipeline(model_id: str, token: str):
     """
     if model_id in LLAMA_PIPELINES:
         return LLAMA_PIPELINES[model_id]
+
     tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token)
     model = AutoModelForCausalLM.from_pretrained(
         model_id,
@@ -62,6 +64,7 @@ def get_llama_pipeline(model_id: str, token: str):
     LLAMA_PIPELINES[model_id] = text_pipeline
     return text_pipeline
 
+
 def get_musicgen_model(model_key: str = "facebook/musicgen-large"):
     """
     Returns a cached MusicGen model if available; otherwise, loads it.
@@ -69,23 +72,28 @@ def get_musicgen_model(model_key: str = "facebook/musicgen-large"):
     """
     if model_key in MUSICGEN_MODELS:
         return MUSICGEN_MODELS[model_key]
+
     model = MusicgenForConditionalGeneration.from_pretrained(model_key)
     processor = AutoProcessor.from_pretrained(model_key)
+
     device = "cuda" if torch.cuda.is_available() else "cpu"
     model.to(device)
     MUSICGEN_MODELS[model_key] = (model, processor)
     return model, processor
 
+
 def get_tts_model(model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
     """
     Returns a cached TTS model if available; otherwise, loads it.
     """
     if model_name in TTS_MODELS:
         return TTS_MODELS[model_name]
+
     tts_model = TTS(model_name)
     TTS_MODELS[model_name] = tts_model
     return tts_model
 
+
 # ---------------------------------------------------------------------
 # Script Generation Function
 # ---------------------------------------------------------------------
@@ -93,10 +101,11 @@ def get_tts_model(model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
 def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
     """
     Generates a script, sound design suggestions, and music ideas from a user prompt.
-    Returns a tuple: (voice_script, sound_design, music_suggestions).
+    Returns a tuple of strings: (voice_script, sound_design, music_suggestions).
     """
     try:
         text_pipeline = get_llama_pipeline(model_id, token)
+
         system_prompt = (
             "You are an expert radio imaging producer specializing in sound design and music. "
             f"Based on the user's concept and the selected duration of {duration} seconds, produce the following: "
@@ -118,10 +127,12 @@ def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
         if "Output:" in generated_text:
             generated_text = generated_text.split("Output:")[-1].strip()
 
+        # Default placeholders
         voice_script = "No voice-over script found."
         sound_design = "No sound design suggestions found."
         music_suggestions = "No music suggestions found."
 
+        # Voice-Over Script
         if "Voice-Over Script:" in generated_text:
             parts = generated_text.split("Voice-Over Script:")
             voice_script_part = parts[1]
@@ -130,6 +141,7 @@ def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
             else:
                 voice_script = voice_script_part.strip()
 
+        # Sound Design
         if "Sound Design Suggestions:" in generated_text:
             parts = generated_text.split("Sound Design Suggestions:")
             sound_design_part = parts[1]
@@ -138,6 +150,7 @@ def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
             else:
                 sound_design = sound_design_part.strip()
 
+        # Music Suggestions
         if "Music Suggestions:" in generated_text:
             parts = generated_text.split("Music Suggestions:")
             music_suggestions = parts[1].strip()
@@ -147,26 +160,34 @@ def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
     except Exception as e:
         return f"Error generating script: {e}", "", ""
 
+
 # ---------------------------------------------------------------------
 # Voice-Over Generation Function
 # ---------------------------------------------------------------------
 @spaces.GPU(duration=100)
 def generate_voice(script: str, tts_model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
     """
-    Generates a voice-over from the provided script using Coqui TTS.
+    Generates a voice-over from the provided script using the Coqui TTS model.
     Returns the file path to the generated .wav file.
     """
     try:
         if not script.strip():
             return "Error: No script provided."
+
+        # Clean the script to remove special characters (e.g., asterisks) that may produce warnings
         cleaned_script = clean_text(script)
+
         tts_model = get_tts_model(tts_model_name)
+
+        # Generate and save voice
         output_path = os.path.join(tempfile.gettempdir(), "voice_over.wav")
         tts_model.tts_to_file(text=cleaned_script, file_path=output_path)
         return output_path
+
     except Exception as e:
         return f"Error generating voice: {e}"
 
+
 # ---------------------------------------------------------------------
 # Music Generation Function
 # ---------------------------------------------------------------------
@@ -184,28 +205,33 @@ def generate_music(prompt: str, audio_length: int):
         musicgen_model, musicgen_processor = get_musicgen_model(model_key)
 
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt")
-        inputs = {k: v.to(device) for k, v in inputs.items()}
+        inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt").to(device)
 
         with torch.inference_mode():
             outputs = musicgen_model.generate(**inputs, max_new_tokens=audio_length)
 
         audio_data = outputs[0, 0].cpu().numpy()
         normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16")
+
         output_path = os.path.join(tempfile.gettempdir(), "musicgen_large_generated_music.wav")
         write(output_path, 44100, normalized_audio)
+
         return output_path
 
     except Exception as e:
         return f"Error generating music: {e}"
 
+
 # ---------------------------------------------------------------------
-# Audio Blending Function
+# Audio Blending with Duration Sync & Ducking
 # ---------------------------------------------------------------------
 @spaces.GPU(duration=100)
 def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int = 10):
     """
     Blends two audio files (voice and music).
+    1. If music < voice, loops the music until it meets/exceeds the voice duration.
+    2. If music > voice, trims music to the voice duration.
+    3. If ducking=True, the music is attenuated by 'duck_level' dB while the voice is playing.
     Returns the file path to the blended .wav file.
     """
     try:
@@ -214,19 +240,27 @@ def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int
 
         voice = AudioSegment.from_wav(voice_path)
         music = AudioSegment.from_wav(music_path)
-        voice_len = len(voice)
-        music_len = len(music)
 
+        voice_len = len(voice)  # in milliseconds
+        music_len = len(music)  # in milliseconds
+
+        # Loop music if it's shorter than the voice
         if music_len < voice_len:
             looped_music = AudioSegment.empty()
             while len(looped_music) < voice_len:
                 looped_music += music
             music = looped_music
 
+        # Trim music if it's longer than the voice
         if len(music) > voice_len:
             music = music[:voice_len]
 
-        final_audio = music.overlay(voice, gain_during_overlay=-duck_level) if ducking else music.overlay(voice)
+        if ducking:
+            ducked_music = music - duck_level
+            final_audio = ducked_music.overlay(voice)
+        else:
+            final_audio = music.overlay(voice)
+
         output_path = os.path.join(tempfile.gettempdir(), "blended_output.wav")
         final_audio.export(output_path, format="wav")
         return output_path
@@ -234,29 +268,12 @@ def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int
     except Exception as e:
         return f"Error blending audio: {e}"
 
-# ---------------------------------------------------------------------
-# Agent Function: Orchestrate the Full Workflow
-# ---------------------------------------------------------------------
-@spaces.GPU(duration=400)
-def run_agent(user_prompt: str, llama_model_id: str, duration: int, tts_model_name: str, music_length: int, ducking: bool, duck_level: int):
-    """
-    Runs the full workflow as an agent:
-      1. Generates a script (voice-over, sound design, and music suggestions).
-      2. Synthesizes a voice-over.
-      3. Generates a music track.
-      4. Blends the voice and music.
-    Returns all generated components.
-    """
-    voice_script, sound_design, music_suggestions = generate_script(user_prompt, llama_model_id, HF_TOKEN, duration)
-    voice_file = generate_voice(voice_script, tts_model_name)
-    music_file = generate_music(music_suggestions, music_length)
-    blended_file = blend_audio(voice_file, music_file, ducking, duck_level)
-    return voice_script, sound_design, music_suggestions, voice_file, music_file, blended_file
 
 # ---------------------------------------------------------------------
 # Gradio Interface with Enhanced UI
 # ---------------------------------------------------------------------
 with gr.Blocks(css="""
+    /* Global Styles */
     body {
         background: linear-gradient(135deg, #1d1f21, #3a3d41);
         color: #f0f0f0;
@@ -303,88 +320,109 @@ with gr.Blocks(css="""
 
     gr.Markdown("""
     Welcome to **AI Promo Studio**! This platform leverages state-of-the-art AI models to help you generate:
-    - A compelling voice-over script (with sound design and music suggestions),
-    - A natural-sounding voice-over,
-    - Custom music tracks,
-    - And a fully blended audio promo.
+    
+    - **Script**: Generate a compelling voice-over script with LLaMA.
+    - **Voice Synthesis**: Create natural-sounding voice-overs using Coqui TTS.
+    - **Music Production**: Produce custom music tracks with MusicGen.
+    - **Audio Blending**: Seamlessly blend voice and music with options for ducking.
     """)
 
     with gr.Tabs():
-        # Tab 1: Script Generation
+        # Step 1: Generate Script
         with gr.Tab("📝 Script Generation"):
             with gr.Row():
-                user_prompt = gr.Textbox(label="Promo Idea", placeholder="E.g., A 30-second promo for a morning show...", lines=2)
+                user_prompt = gr.Textbox(
+                    label="Promo Idea", 
+                    placeholder="E.g., A 30-second promo for a morning show...",
+                    lines=2
+                )
             with gr.Row():
-                llama_model_id = gr.Textbox(label="LLaMA Model ID", value="meta-llama/Meta-Llama-3-8B-Instruct", placeholder="Enter a valid Hugging Face model ID")
-                duration = gr.Slider(label="Promo Duration (seconds)", minimum=15, maximum=60, step=15, value=30)
+                llama_model_id = gr.Textbox(
+                    label="LLaMA Model ID", 
+                    value="meta-llama/Meta-Llama-3-8B-Instruct", 
+                    placeholder="Enter a valid Hugging Face model ID"
+                )
+                duration = gr.Slider(
+                    label="Desired Promo Duration (seconds)",
+                    minimum=15, 
+                    maximum=60, 
+                    step=15, 
+                    value=30
+                )
             generate_script_button = gr.Button("Generate Script", variant="primary")
-            script_output = gr.Textbox(label="Voice-Over Script", lines=5, interactive=False)
+            script_output = gr.Textbox(label="Generated Voice-Over Script", lines=5, interactive=False)
             sound_design_output = gr.Textbox(label="Sound Design Suggestions", lines=3, interactive=False)
             music_suggestion_output = gr.Textbox(label="Music Suggestions", lines=3, interactive=False)
-            generate_script_button.click(fn=lambda prompt, model, dur: generate_script(prompt, model, HF_TOKEN, dur),
-                                          inputs=[user_prompt, llama_model_id, duration],
-                                          outputs=[script_output, sound_design_output, music_suggestion_output])
 
-        # Tab 2: Voice Synthesis
+            generate_script_button.click(
+                fn=lambda user_prompt, model_id, dur: generate_script(user_prompt, model_id, HF_TOKEN, dur),
+                inputs=[user_prompt, llama_model_id, duration],
+                outputs=[script_output, sound_design_output, music_suggestion_output],
+            )
+
+        # Step 2: Generate Voice
         with gr.Tab("🎤 Voice Synthesis"):
             gr.Markdown("Generate a natural-sounding voice-over using Coqui TTS.")
-            selected_tts_model = gr.Dropdown(label="TTS Model",
-                                             choices=["tts_models/en/ljspeech/tacotron2-DDC", "tts_models/en/ljspeech/vits", "tts_models/en/sam/tacotron-DDC"],
-                                             value="tts_models/en/ljspeech/tacotron2-DDC", multiselect=False)
+            selected_tts_model = gr.Dropdown(
+                label="TTS Model",
+                choices=[
+                    "tts_models/en/ljspeech/tacotron2-DDC",  
+                    "tts_models/en/ljspeech/vits", 
+                    "tts_models/en/sam/tacotron-DDC", 
+                ],
+                value="tts_models/en/ljspeech/tacotron2-DDC",
+                multiselect=False
+            )
             generate_voice_button = gr.Button("Generate Voice-Over", variant="primary")
             voice_audio_output = gr.Audio(label="Voice-Over (WAV)", type="filepath")
-            generate_voice_button.click(fn=lambda script, tts: generate_voice(script, tts),
-                                        inputs=[script_output, selected_tts_model],
-                                        outputs=voice_audio_output)
 
-        # Tab 3: Music Production
+            generate_voice_button.click(
+                fn=lambda script, tts_model: generate_voice(script, tts_model),
+                inputs=[script_output, selected_tts_model],
+                outputs=voice_audio_output,
+            )
+
+        # Step 3: Generate Music
         with gr.Tab("🎶 Music Production"):
-            gr.Markdown("Generate a custom music track using the MusicGen Large model.")
-            audio_length = gr.Slider(label="Music Length (tokens)", minimum=128, maximum=1024, step=64, value=512, info="Increase tokens for longer audio (inference time may vary).")
+            gr.Markdown("Generate a custom music track using the **MusicGen Large** model.")
+            audio_length = gr.Slider(
+                label="Music Length (tokens)",
+                minimum=128, 
+                maximum=1024, 
+                step=64, 
+                value=512,
+                info="Increase tokens for longer audio (inference time may vary)."
+            )
             generate_music_button = gr.Button("Generate Music", variant="primary")
             music_output = gr.Audio(label="Generated Music (WAV)", type="filepath")
-            generate_music_button.click(fn=lambda sugg, length: generate_music(sugg, length),
-                                        inputs=[music_suggestion_output, audio_length],
-                                        outputs=[music_output])
 
-        # Tab 4: Audio Blending
+            generate_music_button.click(
+                fn=lambda music_suggestion, length: generate_music(music_suggestion, length),
+                inputs=[music_suggestion_output, audio_length],
+                outputs=[music_output],
+            )
+
+        # Step 4: Blend Audio
         with gr.Tab("🎚️ Audio Blending"):
-            gr.Markdown("Blend your voice-over and music track. Enable ducking to lower the music during voice segments.")
+            gr.Markdown("Blend your voice-over and music track. Music will be looped/truncated to match the voice duration. Enable ducking to lower the music during voice segments.")
             ducking_checkbox = gr.Checkbox(label="Enable Ducking?", value=True)
-            duck_level_slider = gr.Slider(label="Ducking Level (dB attenuation)", minimum=0, maximum=20, step=1, value=10)
+            duck_level_slider = gr.Slider(
+                label="Ducking Level (dB attenuation)", 
+                minimum=0, 
+                maximum=20, 
+                step=1, 
+                value=10
+            )
             blend_button = gr.Button("Blend Voice + Music", variant="primary")
             blended_output = gr.Audio(label="Final Blended Output (WAV)", type="filepath")
-            blend_button.click(fn=blend_audio,
-                               inputs=[voice_audio_output, music_output, ducking_checkbox, duck_level_slider],
-                               outputs=blended_output)
 
-        # Tab 5: Agent – Full Workflow
-        with gr.Tab("🤖 Agent"):
-            gr.Markdown("Let the agent handle everything in one go: generate script, synthesize voice, produce music, and blend the final ad.")
-            with gr.Row():
-                agent_prompt = gr.Textbox(label="Ad Promo Idea", placeholder="Enter your ad promo concept...", lines=2)
-            with gr.Row():
-                agent_llama_model_id = gr.Textbox(label="LLaMA Model ID", value="meta-llama/Meta-Llama-3-8B-Instruct", placeholder="Enter a valid Hugging Face model ID")
-                agent_duration = gr.Slider(label="Promo Duration (seconds)", minimum=15, maximum=60, step=15, value=30)
-            with gr.Row():
-                agent_tts_model = gr.Dropdown(label="TTS Model",
-                                              choices=["tts_models/en/ljspeech/tacotron2-DDC", "tts_models/en/ljspeech/vits", "tts_models/en/sam/tacotron-DDC"],
-                                              value="tts_models/en/ljspeech/tacotron2-DDC", multiselect=False)
-                agent_music_length = gr.Slider(label="Music Length (tokens)", minimum=128, maximum=1024, step=64, value=512)
-            with gr.Row():
-                agent_ducking = gr.Checkbox(label="Enable Ducking?", value=True)
-                agent_duck_level = gr.Slider(label="Ducking Level (dB attenuation)", minimum=0, maximum=20, step=1, value=10)
-            agent_run_button = gr.Button("Run Agent", variant="primary")
-            agent_script_output = gr.Textbox(label="Voice-Over Script", lines=5, interactive=False)
-            agent_sound_output = gr.Textbox(label="Sound Design Suggestions", lines=3, interactive=False)
-            agent_music_suggestions_output = gr.Textbox(label="Music Suggestions", lines=3, interactive=False)
-            agent_voice_audio = gr.Audio(label="Voice-Over (WAV)", type="filepath")
-            agent_music_audio = gr.Audio(label="Generated Music (WAV)", type="filepath")
-            agent_blended_audio = gr.Audio(label="Final Blended Output (WAV)", type="filepath")
-            agent_run_button.click(fn=run_agent,
-                                   inputs=[agent_prompt, agent_llama_model_id, agent_duration, agent_tts_model, agent_music_length, agent_ducking, agent_duck_level],
-                                   outputs=[agent_script_output, agent_sound_output, agent_music_suggestions_output, agent_voice_audio, agent_music_audio, agent_blended_audio])
+            blend_button.click(
+                fn=blend_audio,
+                inputs=[voice_audio_output, music_output, ducking_checkbox, duck_level_slider],
+                outputs=blended_output
+            )
 
+    # Footer
     gr.Markdown("""
     <div class="footer">
         <hr>
@@ -394,6 +432,7 @@ with gr.Blocks(css="""
     </div>
     """)
     
+    # Visitor Badge
     gr.HTML("""
     <div style="text-align: center; margin-top: 1rem;">
         <a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold">