import argparse 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 # --- Global Setup --- parser = argparse.ArgumentParser(description="Gradio interface for Nari TTS") parser.add_argument( "--device", type=str, default=None, help="Force device (e.g., 'cuda', 'mps', 'cpu')" ) parser.add_argument("--share", action="store_true", help="Enable Gradio sharing") args = parser.parse_args() # Determine device if args.device: device = torch.device(args.device) elif torch.cuda.is_available(): device = torch.device("cuda") # Simplified MPS check for broader compatibility elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): # Basic check is usually sufficient, detailed check can be problematic device = torch.device("mps") else: device = torch.device("cpu") print(f"Using device: {device}") # 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") 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, use_cfg_filter=True, cfg_filter_top_k=cfg_filter_top_k, # Pass the value here use_torch_compile=False, # Keep False for Gradio stability audio_prompt_path=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}" ) 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...") demo.launch()