Create app.py

app.py

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+import queue
+import threading
+
+import gradio as gr
+from dia.model import Dia
+from huggingface_hub import InferenceClient
+
+# Hardcoded podcast subject
+PODCAST_SUBJECT = "The future of AI and its impact on society"
+
+# Initialize the inference client
+client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct", provider="together")
+model = Dia.from_pretrained("nari-labs/Dia-1.6B", compute_dtype="float16")
+
+# Queue for audio streaming
+audio_queue = queue.Queue()
+stop_signal = threading.Event()
+
+
+def generate_podcast_text(subject):
+    prompt = f"""Generate a podcast told by 2 hosts about {subject}.
+The podcast should be an insightful discussion, with some amount of playful banter.
+Separate dialog as follows using [S1] for the male host and [S2] for the female host, for instance:
+[S1] Hello, how are you?
+[S2] I'm good, thank you. How are you?
+[S1] I'm good, thank you. (laughs)
+[S2] Great.
+Now go on, make 2 minutes of podcast.
+"""
+    response = client.chat_completion([{"role": "user", "content": prompt}], max_tokens=1000)
+    return response.choices[0].message.content
+
+
+def split_podcast_into_chunks(podcast_text, chunk_size=10):
+    lines = podcast_text.strip().split("\n")
+    chunks = []
+
+    for i in range(0, len(lines), chunk_size):
+        chunk = "\n".join(lines[i : i + chunk_size])
+        chunks.append(chunk)
+
+    return chunks
+
+
+def process_audio_chunks(podcast_text):
+    chunks = split_podcast_into_chunks(podcast_text)
+
+    for chunk in chunks:
+        if stop_signal.is_set():
+            break
+
+        audio_chunk = model.generate(chunk, use_torch_compile=True, verbose=False)
+        audio_queue.put(audio_chunk)
+
+    audio_queue.put(None)
+
+
+def stream_audio_generator(podcast_text):
+    """Creates a generator that yields audio chunks for streaming"""
+    stop_signal.clear()
+
+    # Start audio generation in a separate thread
+    gen_thread = threading.Thread(target=process_audio_chunks, args=(podcast_text,))
+    gen_thread.start()
+
+    sample_rate = 22050
+
+    try:
+        while True:
+            # Get next chunk from queue
+            chunk = audio_queue.get()
+
+            # None signals end of generation
+            if chunk is None:
+                break
+
+            # Yield the audio chunk with sample rate
+            yield (sample_rate, chunk)
+
+    except Exception as e:
+        print(f"Error in streaming: {e}")
+
+
+def stop_generation():
+    stop_signal.set()
+    return "Generation stopped"
+
+
+def generate_podcast():
+    podcast_text = generate_podcast_text(PODCAST_SUBJECT)
+    return podcast_text
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# NotebookLM Podcast Generator")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown(f"## Current Topic: {PODCAST_SUBJECT}")
+            gr.Markdown("This app generates a podcast discussion between two hosts about the specified topic.")
+
+            generate_btn = gr.Button("Generate Podcast Script", variant="primary")
+            podcast_output = gr.Textbox(label="Generated Podcast Script", lines=15)
+
+            gr.Markdown("## Audio Preview")
+            gr.Markdown("Click below to hear the podcast with realistic voices:")
+
+            with gr.Row():
+                start_audio_btn = gr.Button("▶️ Generate Podcast", variant="secondary")
+                stop_btn = gr.Button("⏹️ Stop", variant="stop")
+
+            audio_output = gr.Audio(label="Podcast Audio", streaming=True)
+            status_text = gr.Textbox(label="Status", visible=True)
+
+    generate_btn.click(fn=generate_podcast, outputs=podcast_output)
+
+    start_audio_btn.click(fn=stream_audio_generator, inputs=podcast_output, outputs=audio_output)
+    stop_btn.click(fn=stop_generation, outputs=status_text)
+
+if __name__ == "__main__":
+    demo.queue().launch()