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AI Audio Denoiser – Remove Background Noise Instantly
This is a commercial-grade, proprietary audio denoising algorithm designed to clean noisy audio clips using AI and DSP.

🎯 Ideal for:

Podcasters, YouTubers, and streamers

Call center recordings and voice notes

SaaS platforms with noisy audio inputs

🧠 Built with [ConvTasNet / FFT / Custom DSP]
⚙️ Available as API, SDK, or on-premises solution


👉 Contact us for integration or licensing: itsdevansh57@gmail.com
🔐 This demo is for evaluation only. All rights reserved.

README.md

@@ -1,14 +1,7 @@
----
-title: Audio Denoiser
-emoji: 🌖
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 5.32.0
-app_file: app.py
-pinned: false
-license: other
-short_description: 'AI Audio Denoiser – Removes Background Noise '
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎧 ConvTasNet AI Audio Denoiser
+
+Upload noisy audio clips to get clean, denoised audio using a state-of-the-art ConvTasNet model.
+
+> ⚠️ This demo is for evaluation only. Commercial use is prohibited without permission.
+
+Contact: itsdevansh57@gmail.com

app.py

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+import torch
+import torchaudio
+import soundfile as sf
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+from asteroid.models import BaseModel
+import gradio as gr
+import os
+import uuid
+
+# Load pretrained ConvTasNet model
+print("Loading model...")
+model = BaseModel.from_pretrained("JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device).eval()
+print("Model loaded successfully ✅")
+
+def denoise_and_visualize(audio_path):
+    if audio_path is None:
+        return "Please upload an audio file.", None, None, None
+
+    try:
+        # Unique ID to avoid overwriting files
+        uid = str(uuid.uuid4())
+        output_dir = "outputs"
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Load & resample input to 16kHz mono
+        wav, sr = torchaudio.load(audio_path)
+        if sr != 16000:
+            wav = torchaudio.functional.resample(wav, sr, 16000)
+        wav = wav.mean(dim=0, keepdim=True).to(device)
+
+        # Model inference
+        with torch.no_grad():
+            est_sources = model.separate(wav)
+        clean_audio = est_sources[:, 0, :].cpu().squeeze().numpy()
+
+        # Save output audio
+        audio_output = os.path.join(output_dir, f"cleaned_{uid}.wav")
+        sf.write(audio_output, clean_audio, 16000)
+
+        # Create spectrograms
+        orig, _ = librosa.load(audio_path, sr=sr)
+        den, _ = librosa.load(audio_output, sr=16000)
+
+        plt.figure(figsize=(12, 5))
+        plt.subplot(1, 2, 1)
+        D_orig = librosa.amplitude_to_db(np.abs(librosa.stft(orig)), ref=np.max)
+        librosa.display.specshow(D_orig, sr=sr, y_axis='log', x_axis='time')
+        plt.title("Original Noisy")
+        plt.colorbar(format='%+2.0f dB')
+
+        plt.subplot(1, 2, 2)
+        D_clean = librosa.amplitude_to_db(np.abs(librosa.stft(den)), ref=np.max)
+        librosa.display.specshow(D_clean, sr=16000, y_axis='log', x_axis='time')
+        plt.title("Denoised Output")
+        plt.colorbar(format='%+2.0f dB')
+
+        plt.tight_layout()
+        spectrogram_output = os.path.join(output_dir, f"spectrogram_{uid}.png")
+        plt.savefig(spectrogram_output)
+        plt.close()
+
+        return "✅ Denoising complete!", audio_output, spectrogram_output, (16000, clean_audio)
+
+    except Exception as e:
+        return f"Error processing audio: {e}", None, None, None
+
+# Gradio UI
+iface = gr.Interface(
+    fn=denoise_and_visualize,
+    inputs=gr.Audio(type="filepath", label="Upload Noisy Audio"),
+    outputs=[
+        gr.Textbox(label="Status"),
+        gr.Audio(label="Denoised Audio"),
+        gr.Image(label="Spectrogram Comparison"),
+        gr.Audio(label="Denoised Audio (16kHz)"),
+    ],
+    title="ConvTasNet AI Audio Denoiser",
+    description="Upload a noisy audio file. This app removes background noise using ConvTasNet. Spectrograms show before & after.",
+)
+
+iface.launch()

requirements.txt

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+torch
+torchaudio
+gradio
+librosa
+matplotlib
+soundfile
+asteroid