add sample dia app file

dia_app_gradio.py

@@ -0,0 +1,378 @@
+import tempfile
+import time
+from pathlib import Path
+from typing import Optional, Tuple
+import spaces
+
+import gradio as gr
+import numpy as np
+import soundfile as sf
+import torch
+
+from dia.model import Dia
+
+
+# Load Nari model and config
+print("Loading Nari model...")
+try:
+    # Use the function from inference.py
+    model = Dia.from_pretrained("nari-labs/Dia-1.6B", compute_dtype="float32")
+except Exception as e:
+    print(f"Error loading Nari model: {e}")
+    raise
+
+
+@spaces.GPU
+def run_inference(
+    text_input: str,
+    audio_prompt_input: Optional[Tuple[int, np.ndarray]],
+    max_new_tokens: int,
+    cfg_scale: float,
+    temperature: float,
+    top_p: float,
+    cfg_filter_top_k: int,
+    speed_factor: float,
+):
+    """
+    Runs Nari inference using the globally loaded model and provided inputs.
+    Uses temporary files for text and audio prompt compatibility with inference.generate.
+    """
+    # global model, device  # Access global model, config, device
+
+    if not text_input or text_input.isspace():
+        raise gr.Error("Text input cannot be empty.")
+
+    temp_txt_file_path = None
+    temp_audio_prompt_path = None
+    output_audio = (44100, np.zeros(1, dtype=np.float32))
+
+    try:
+        prompt_path_for_generate = None
+        if audio_prompt_input is not None:
+            sr, audio_data = audio_prompt_input
+            # Check if audio_data is valid
+            if (
+                audio_data is None or audio_data.size == 0 or audio_data.max() == 0
+            ):  # Check for silence/empty
+                gr.Warning("Audio prompt seems empty or silent, ignoring prompt.")
+            else:
+                # Save prompt audio to a temporary WAV file
+                with tempfile.NamedTemporaryFile(
+                    mode="wb", suffix=".wav", delete=False
+                ) as f_audio:
+                    temp_audio_prompt_path = f_audio.name  # Store path for cleanup
+
+                    # Basic audio preprocessing for consistency
+                    # Convert to float32 in [-1, 1] range if integer type
+                    if np.issubdtype(audio_data.dtype, np.integer):
+                        max_val = np.iinfo(audio_data.dtype).max
+                        audio_data = audio_data.astype(np.float32) / max_val
+                    elif not np.issubdtype(audio_data.dtype, np.floating):
+                        gr.Warning(
+                            f"Unsupported audio prompt dtype {audio_data.dtype}, attempting conversion."
+                        )
+                        # Attempt conversion, might fail for complex types
+                        try:
+                            audio_data = audio_data.astype(np.float32)
+                        except Exception as conv_e:
+                            raise gr.Error(
+                                f"Failed to convert audio prompt to float32: {conv_e}"
+                            )
+
+                    # Ensure mono (average channels if stereo)
+                    if audio_data.ndim > 1:
+                        if audio_data.shape[0] == 2:  # Assume (2, N)
+                            audio_data = np.mean(audio_data, axis=0)
+                        elif audio_data.shape[1] == 2:  # Assume (N, 2)
+                            audio_data = np.mean(audio_data, axis=1)
+                        else:
+                            gr.Warning(
+                                f"Audio prompt has unexpected shape {audio_data.shape}, taking first channel/axis."
+                            )
+                            audio_data = (
+                                audio_data[0]
+                                if audio_data.shape[0] < audio_data.shape[1]
+                                else audio_data[:, 0]
+                            )
+                        audio_data = np.ascontiguousarray(
+                            audio_data
+                        )  # Ensure contiguous after slicing/mean
+
+                    # Write using soundfile
+                    try:
+                        sf.write(
+                            temp_audio_prompt_path, audio_data, sr, subtype="FLOAT"
+                        )  # Explicitly use FLOAT subtype
+                        prompt_path_for_generate = temp_audio_prompt_path
+                        print(
+                            f"Created temporary audio prompt file: {temp_audio_prompt_path} (orig sr: {sr})"
+                        )
+                    except Exception as write_e:
+                        print(f"Error writing temporary audio file: {write_e}")
+                        raise gr.Error(f"Failed to save audio prompt: {write_e}")
+
+        # 3. Run Generation
+
+        start_time = time.time()
+
+        # Use torch.inference_mode() context manager for the generation call
+        with torch.inference_mode():
+            output_audio_np = model.generate(
+                text_input,
+                max_tokens=max_new_tokens,
+                cfg_scale=cfg_scale,
+                temperature=temperature,
+                top_p=top_p,
+                cfg_filter_top_k=cfg_filter_top_k,  # Pass the value here
+                use_torch_compile=False,  # Keep False for Gradio stability
+                audio_prompt=prompt_path_for_generate,
+            )
+
+        end_time = time.time()
+        print(f"Generation finished in {end_time - start_time:.2f} seconds.")
+
+        # 4. Convert Codes to Audio
+        if output_audio_np is not None:
+            # Get sample rate from the loaded DAC model
+            output_sr = 44100
+
+            # --- Slow down audio ---
+            original_len = len(output_audio_np)
+            # Ensure speed_factor is positive and not excessively small/large to avoid issues
+            speed_factor = max(0.1, min(speed_factor, 5.0))
+            target_len = int(
+                original_len / speed_factor
+            )  # Target length based on speed_factor
+            if (
+                target_len != original_len and target_len > 0
+            ):  # Only interpolate if length changes and is valid
+                x_original = np.arange(original_len)
+                x_resampled = np.linspace(0, original_len - 1, target_len)
+                resampled_audio_np = np.interp(x_resampled, x_original, output_audio_np)
+                output_audio = (
+                    output_sr,
+                    resampled_audio_np.astype(np.float32),
+                )  # Use resampled audio
+                print(
+                    f"Resampled audio from {original_len} to {target_len} samples for {speed_factor:.2f}x speed."
+                )
+            else:
+                output_audio = (
+                    output_sr,
+                    output_audio_np,
+                )  # Keep original if calculation fails or no change
+                print(f"Skipping audio speed adjustment (factor: {speed_factor:.2f}).")
+            # --- End slowdown ---
+
+            print(
+                f"Audio conversion successful. Final shape: {output_audio[1].shape}, Sample Rate: {output_sr}"
+            )
+
+            # Explicitly convert to int16 to prevent Gradio warning
+            if (
+                output_audio[1].dtype == np.float32
+                or output_audio[1].dtype == np.float64
+            ):
+                audio_for_gradio = np.clip(output_audio[1], -1.0, 1.0)
+                audio_for_gradio = (audio_for_gradio * 32767).astype(np.int16)
+                output_audio = (output_sr, audio_for_gradio)
+                print("Converted audio to int16 for Gradio output.")
+
+        else:
+            print("\nGeneration finished, but no valid tokens were produced.")
+            # Return default silence
+            gr.Warning("Generation produced no output.")
+
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        import traceback
+
+        traceback.print_exc()
+        # Re-raise as Gradio error to display nicely in the UI
+        raise gr.Error(f"Inference failed: {e}")
+
+    finally:
+        # 5. Cleanup Temporary Files defensively
+        if temp_txt_file_path and Path(temp_txt_file_path).exists():
+            try:
+                Path(temp_txt_file_path).unlink()
+                print(f"Deleted temporary text file: {temp_txt_file_path}")
+            except OSError as e:
+                print(
+                    f"Warning: Error deleting temporary text file {temp_txt_file_path}: {e}"
+                )
+        if temp_audio_prompt_path and Path(temp_audio_prompt_path).exists():
+            try:
+                Path(temp_audio_prompt_path).unlink()
+                print(f"Deleted temporary audio prompt file: {temp_audio_prompt_path}")
+            except OSError as e:
+                print(
+                    f"Warning: Error deleting temporary audio prompt file {temp_audio_prompt_path}: {e}"
+                )
+
+    return output_audio
+
+
+# --- Create Gradio Interface ---
+css = """
+#col-container {max-width: 90%; margin-left: auto; margin-right: auto;}
+"""
+# Attempt to load default text from example.txt
+default_text = "[S1] Dia is an open weights text to dialogue model. \n[S2] You get full control over scripts and voices. \n[S1] Wow. Amazing. (laughs) \n[S2] Try it now on Git hub or Hugging Face."
+example_txt_path = Path("./example.txt")
+if example_txt_path.exists():
+    try:
+        default_text = example_txt_path.read_text(encoding="utf-8").strip()
+        if not default_text:  # Handle empty example file
+            default_text = "Example text file was empty."
+    except Exception as e:
+        print(f"Warning: Could not read example.txt: {e}")
+
+
+# Build Gradio UI
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# Nari Text-to-Speech Synthesis")
+
+    with gr.Row(equal_height=False):
+        with gr.Column(scale=1):
+            text_input = gr.Textbox(
+                label="Input Text",
+                placeholder="Enter text here...",
+                value=default_text,
+                lines=5,  # Increased lines
+            )
+            audio_prompt_input = gr.Audio(
+                label="Audio Prompt (Optional)",
+                show_label=True,
+                sources=["upload", "microphone"],
+                type="numpy",
+            )
+            with gr.Accordion("Generation Parameters", open=False):
+                max_new_tokens = gr.Slider(
+                    label="Max New Tokens (Audio Length)",
+                    minimum=860,
+                    maximum=3072,
+                    value=model.config.data.audio_length,  # Use config default if available, else fallback
+                    step=50,
+                    info="Controls the maximum length of the generated audio (more tokens = longer audio).",
+                )
+                cfg_scale = gr.Slider(
+                    label="CFG Scale (Guidance Strength)",
+                    minimum=1.0,
+                    maximum=5.0,
+                    value=3.0,  # Default from inference.py
+                    step=0.1,
+                    info="Higher values increase adherence to the text prompt.",
+                )
+                temperature = gr.Slider(
+                    label="Temperature (Randomness)",
+                    minimum=1.0,
+                    maximum=1.5,
+                    value=1.3,  # Default from inference.py
+                    step=0.05,
+                    info="Lower values make the output more deterministic, higher values increase randomness.",
+                )
+                top_p = gr.Slider(
+                    label="Top P (Nucleus Sampling)",
+                    minimum=0.80,
+                    maximum=1.0,
+                    value=0.95,  # Default from inference.py
+                    step=0.01,
+                    info="Filters vocabulary to the most likely tokens cumulatively reaching probability P.",
+                )
+                cfg_filter_top_k = gr.Slider(
+                    label="CFG Filter Top K",
+                    minimum=15,
+                    maximum=50,
+                    value=30,
+                    step=1,
+                    info="Top k filter for CFG guidance.",
+                )
+                speed_factor_slider = gr.Slider(
+                    label="Speed Factor",
+                    minimum=0.8,
+                    maximum=1.0,
+                    value=0.94,
+                    step=0.02,
+                    info="Adjusts the speed of the generated audio (1.0 = original speed).",
+                )
+
+            run_button = gr.Button("Generate Audio", variant="primary")
+
+        with gr.Column(scale=1):
+            audio_output = gr.Audio(
+                label="Generated Audio",
+                type="numpy",
+                autoplay=False,
+            )
+
+    # Link button click to function
+    run_button.click(
+        fn=run_inference,
+        inputs=[
+            text_input,
+            audio_prompt_input,
+            max_new_tokens,
+            cfg_scale,
+            temperature,
+            top_p,
+            cfg_filter_top_k,
+            speed_factor_slider,
+        ],
+        outputs=[audio_output],  # Add status_output here if using it
+        api_name="generate_audio",
+    )
+
+    # Add examples (ensure the prompt path is correct or remove it if example file doesn't exist)
+    example_prompt_path = "./example_prompt.mp3"  # Adjust if needed
+    examples_list = [
+        [
+            "[S1] Oh fire! Oh my goodness! What's the procedure? What to we do people? The smoke could be coming through an air duct! \n[S2] Oh my god! Okay.. it's happening. Everybody stay calm! \n[S1] What's the procedure... \n[S2] Everybody stay fucking calm!!!... Everybody fucking calm down!!!!! \n[S1] No! No! If you touch the handle, if its hot there might be a fire down the hallway! ",
+            None,
+            3072,
+            3.0,
+            1.3,
+            0.95,
+            35,
+            0.94,
+        ],
+        [
+            "[S1] Open weights text to dialogue model. \n[S2] You get full control over scripts and voices. \n[S1] I'm biased, but I think we clearly won. \n[S2] Hard to disagree. (laughs) \n[S1] Thanks for listening to this demo. \n[S2] Try it now on Git hub and Hugging Face. \n[S1] If you liked our model, please give us a star and share to your friends. \n[S2] This was Nari Labs.",
+            example_prompt_path if Path(example_prompt_path).exists() else None,
+            3072,
+            3.0,
+            1.3,
+            0.95,
+            35,
+            0.94,
+        ],
+    ]
+
+    if examples_list:
+        gr.Examples(
+            examples=examples_list,
+            inputs=[
+                text_input,
+                audio_prompt_input,
+                max_new_tokens,
+                cfg_scale,
+                temperature,
+                top_p,
+                cfg_filter_top_k,
+                speed_factor_slider,
+            ],
+            outputs=[audio_output],
+            fn=run_inference,
+            cache_examples=False,
+            label="Examples (Click to Run)",
+        )
+    else:
+        gr.Markdown("_(No examples configured or example prompt file missing)_")
+
+# --- Launch the App ---
+if __name__ == "__main__":
+    print("Launching Gradio interface...")
+
+    # set `GRADIO_SERVER_NAME`, `GRADIO_SERVER_PORT` env vars to override default values
+    # use `GRADIO_SERVER_NAME=0.0.0.0` for Docker
+    demo.launch()