Create app.py

app.py

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+import os
+import tempfile
+from pathlib import Path  
+
+import gradio as gr
+import numpy as np
+import soundfile as sf
+import librosa
+import torch
+
+from pyharp import ModelCard, build_endpoint
+from df import enhance, init_df
+
+# -----------------------------
+# Model metadata for HARP
+# -----------------------------
+model_card = ModelCard(
+    name="Background Noise Remover (DeepFilterNet3)",
+    description=(
+        "Background noise suppression / speech enhancement using DeepFilterNet3. "
+        "Input is converted to mono 48kHz. Slider controls strength through wet/dry blend."
+    ),
+    author="Derek Llanes",
+    tags=["denoise", "speech enhancement", "deepfilternet", "v3"],
+)
+
+# -----------------------------
+# Device & Model Initialization
+# -----------------------------
+# Auto-detect GPU for Hugging Face deployment
+DEVICE_STR = "cuda" if torch.cuda.is_available() else "cpu"
+DEVICE = torch.device(DEVICE_STR)
+
+# Load model 1 time into global memory
+MODEL, DF_STATE, _ = init_df()
+MODEL = MODEL.to(DEVICE)
+
+
+def load_audio_mono_48k(path: str):
+    # Load audio from filepath, convert to mono float32, resample to 48kHz.
+    try:
+        audio, sr = sf.read(path, always_2d=False)
+    except Exception:
+        audio, sr = librosa.load(path, sr=None, mono=False)
+
+    audio = np.asarray(audio)
+
+    # stereo to mono
+    if audio.ndim == 2:
+        audio = audio.mean(axis=1)
+
+    audio = audio.astype(np.float32)
+
+    # resample to 48k
+    if sr != 48000:
+        audio = librosa.resample(audio, orig_sr=sr, target_sr=48000)
+        sr = 48000
+
+    return audio, sr
+
+
+def apply_attenuation_db(noisy: np.ndarray, enhanced: np.ndarray, noise_atten_db: float, max_db: float = 30.0):
+    """
+    Map Noise Attenuation (dB) slider to a stable strength in [0,1]
+    and crossfade between original and enhanced audio.
+    """
+    # convert dB slider into [0,1]
+    s = float(noise_atten_db) / float(max_db)
+    s = max(0.0, min(1.0, s))
+
+    # make same length
+    n = min(len(noisy), len(enhanced))
+    noisy = noisy[:n]
+    enhanced = enhanced[:n]
+
+    out = (1.0 - s) * noisy + s * enhanced
+    return out.astype(np.float32)
+
+
+@torch.inference_mode()
+def process_fn(input_audio_path: str, noise_atten_db: float) -> str:
+    if not input_audio_path:
+        raise ValueError("No input audio provided.")
+
+    # Load and normalize
+    noisy, sr = load_audio_mono_48k(input_audio_path)
+
+    # numpy to torch, add channel dim [T] to [1, T]
+    noisy_t = torch.from_numpy(noisy).float().unsqueeze(0).to(DEVICE)
+
+    # Denoise, then remove added channel and back to numpy
+    enhanced_t = enhance(MODEL, DF_STATE, noisy_t)
+    enhanced = enhanced_t.squeeze(0).detach().cpu().numpy()
+
+    # Slider strength
+    out = apply_attenuation_db(noisy, enhanced, noise_atten_db, max_db=30.0)
+
+    # Save output WAV and return path
+    out_dir = Path(tempfile.gettempdir()) / "pyharp_dfnet_outputs"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_path = out_dir / "denoised.wav"
+
+    sf.write(str(out_path), out, sr)
+    return str(out_path)
+
+
+# -----------------------------
+# Gradio endpoint
+# -----------------------------
+with gr.Blocks() as demo:
+    input_components = [
+        gr.Audio(type="filepath", label="Input Audio").harp_required(True),
+        gr.Slider(
+            minimum=0,
+            maximum=30,
+            step=1,
+            value=12,
+            label="Noise Attenuation (dB)",
+            info="0 = no change, 30 = strongest. Implemented as wet/dry strength.",
+        ),
+    ]
+
+    output_components = [
+        gr.Audio(type="filepath", label="Output Audio")
+        .set_info("Denoised audio output."),
+    ]
+
+    app = build_endpoint(
+        model_card=model_card,
+        input_components=input_components,
+        output_components=output_components,
+        process_fn=process_fn,
+    )
+
+demo.queue().launch(show_error=False, pwa=True)