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()