Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+from torchaudio.transforms import Resample
+import moviepy.editor as mp
+import numpy as np
+from denoiser.pretrained import master64  # Import Facebook denoiser pre-trained model
+from denoiser.denoiser import Denoiser  # Denoising wrapper
+
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print("Device - ", DEVICE)
+
+# Load Facebook denoiser model
+model = master64()
+den = Denoiser(model).to(DEVICE)
+den.eval()
+
+def identity(video_path):
+    print(video_path)
+    
+    # Extract audio from the video
+    video = mp.VideoFileClip(video_path)
+    audio = video.audio
+    wav_file = "tmp.wav"
+    audio.write_audiofile(wav_file)
+    print("Wav stored.")
+
+    # Load audio
+    waveform, sr = torchaudio.load(wav_file)
+    waveform = waveform.to(DEVICE)
+
+    # Resample if necessary
+    target_sr = 48000
+    if sr != target_sr:
+        resampler = Resample(orig_freq=sr, new_freq=target_sr).to(DEVICE)
+        waveform = resampler(waveform)
+        sr = target_sr
+
+    # Process audio in chunks to avoid memory issues
+    chunk_duration = 10  # seconds
+    chunk_size = sr * chunk_duration
+    num_chunks = int(np.ceil(waveform.shape[1] / chunk_size))
+
+    enhanced_chunks = []
+    for i in range(num_chunks):
+        start = i * chunk_size
+        end = min((i + 1) * chunk_size, waveform.shape[1])
+        chunk = waveform[:, start:end]
+        enhanced_chunk = den(chunk.unsqueeze(0))
+        enhanced_chunks.append(enhanced_chunk.squeeze(0))
+
+    # Combine enhanced audio
+    enhanced_audio = torch.cat(enhanced_chunks, dim=1).cpu()
+
+    # Save enhanced audio
+    output_audio_path = "enhanced_aud.wav"
+    torchaudio.save(output_audio_path, enhanced_audio, sr)
+
+    # Replace audio in video
+    enhanced_audio_clip = mp.AudioFileClip(output_audio_path)
+    final_video = video.set_audio(enhanced_audio_clip)
+    output_video_path = "output_video.mp4"
+    final_video.write_videofile(output_video_path, 
+                                codec='libx264', 
+                                audio_codec='aac', 
+                                temp_audiofile='temp-audio.m4a', 
+                                remove_temp=True)
+    return output_video_path
+
+demo = gr.Interface(
+    fn=identity,
+    title="NoNoise - THE BEST AUDIO DENOISER",
+    description="NoNoise is the only platform you need for removing all kinds of background noise from your videos!!",
+    examples=[['audiopure_og.mov'], ['example.mp4']],
+    cache_examples=True,
+    inputs=gr.Video(label="Input Video", source="upload"),
+    outputs=gr.Video(label="Output Video"),
+)
+demo.launch()