Create app.py

app.py

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+import noisereduce as nr
+import scipy.io.wavfile as wavfile
+import numpy as np
+import gradio as gr
+import os
+import tempfile
+import shutil
+
+def denoise_audio_file(input_path, output_path):
+    rate, data = wavfile.read(input_path)
+    
+    if len(data.shape) > 1:
+        reduced_noise = np.zeros_like(data, dtype=np.float32)
+        for channel in range(data.shape[1]):
+            reduced_noise[:, channel] = nr.reduce_noise(y=data[:, channel], sr=rate)
+    else:
+        reduced_noise = nr.reduce_noise(y=data, sr=rate)
+    
+    wavfile.write(output_path, rate, reduced_noise.astype(data.dtype))
+    return output_path
+
+def process_single_file(file):
+    if not file.name.endswith('.wav'):
+        raise gr.Error("Please upload a WAV file")
+    
+    # Use the original filename for the denoised file, but in a temp dir
+    name, ext = os.path.splitext(os.path.basename(file.name))
+    base_filename = f"{name}_denoised{ext}"
+    temp_dir = tempfile.mkdtemp()
+    output_path = os.path.join(temp_dir, base_filename)
+    denoise_audio_file(file.name, output_path)
+    return output_path
+
+def process_batch_files(files):
+    output_files = []
+    temp_dir = tempfile.mkdtemp()
+    for file in files:
+        if file.name.endswith('.wav'):
+            name, ext = os.path.splitext(os.path.basename(file.name))
+            base_filename = f"{name}_denoised{ext}"
+            output_path = os.path.join(temp_dir, base_filename)
+            denoise_audio_file(file.name, output_path)
+            output_files.append(output_path)
+    return output_files
+
+with gr.Blocks(title="Audio Noise Reducer") as demo:
+    gr.Markdown("# 🎧 Audio Noise Reduction")
+    gr.Markdown("Upload WAV files to remove background noise using AI-powered processing.")
+    
+    with gr.Tab("Single File Processing"):
+        with gr.Row():
+            with gr.Column():
+                single_file = gr.File(label="Upload WAV File", file_types=[".wav"])
+                single_btn = gr.Button("Process File")
+            with gr.Column():
+                single_output = gr.File(label="Download Denoised File")
+                single_status = gr.Textbox(label="Processing Status", interactive=False)
+                
+        single_btn.click(
+            fn=process_single_file,
+            inputs=single_file,
+            outputs=single_output,
+            api_name="process_single"
+        )
+
+    with gr.Tab("Batch Processing"):
+        with gr.Row():
+            with gr.Column():
+                batch_files = gr.File(label="Upload WAV Files", file_count="multiple", file_types=[".wav"])
+                batch_btn = gr.Button("Process Files")
+            with gr.Column():
+                batch_output = gr.Files(label="Download Denoised Files")
+                batch_status = gr.Textbox(label="Processing Status", interactive=False)
+                
+        batch_btn.click(
+            fn=process_batch_files,
+            inputs=batch_files,
+            outputs=batch_output,
+            api_name="process_batch"
+        )
+
+    demo.queue()
+    
+if __name__ == "__main__":
+    demo.launch()