Create app.py

app.py

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+import gradio as gr
+
+import torch
+from torchaudio.backend.common import AudioMetaData
+from df.enhance import enhance, load_audio, save_audio
+from df.io import resample
+from libdf import DF
+from df.model import ModelParams
+from df import config
+import moviepy.editor as mp
+import numpy as np
+
+try:
+    config.load('config.ini')
+except Exception as e:
+    print(e)
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+p = ModelParams()
+df = DF(
+    sr=p.sr,
+    fft_size=p.fft_size,
+    hop_size=p.hop_size,
+    nb_bands=p.nb_erb,
+    min_nb_erb_freqs=p.min_nb_freqs,
+)
+
+
+print("Device - ", DEVICE)
+model = torch.load(("model.pth"), map_location=torch.device('cpu'))
+model.to(DEVICE)
+model.eval()
+
+def identity(x):
+    print(x)
+    # audio = mp.AudioFileClip(x)
+    wav_file = x
+    # audio.write_audiofile(wav_file)
+    print("Wav stored.")
+    meta = AudioMetaData(-1, -1, -1, -1, "")
+    sr = config("sr", 48000, int, section="df")
+    sample, meta = load_audio(wav_file, sr)
+    len_audio = (meta.num_frames/meta.sample_rate)/60
+    max_min = 1
+    if len_audio  % max_min < 0.1:
+        num_chunks = len_audio // max_min
+    else:
+        num_chunks = len_audio // max_min + 1
+    print(f"Total length of audio = {len_audio} chunks = {num_chunks}")
+    estimate = []
+    split_tensors = torch.tensor_split(sample, int(num_chunks), dim = 1)
+    for i in range(len(split_tensors)):
+        enhanced = enhance(model, df, split_tensors[i])
+        enhanced = enhance(model, df, enhanced)
+        lim = torch.linspace(0.0, 1.0, int(sr * 0.15)).unsqueeze(0)
+        lim = torch.cat((lim, torch.ones(1, enhanced.shape[1] - lim.shape[1])), dim=1)
+        enhanced = enhanced * lim
+        enhanced = resample(enhanced, sr, meta.sample_rate)
+        estimate.append(enhanced)
+    estimate = tuple(estimate)
+    enhanced = torch.cat(estimate, dim = -1)
+    sr = meta.sample_rate
+    save_audio("enhanced_aud.wav", enhanced, sr)
+    return "enhanced_aud.wav"
+
+demo = gr.Interface(
+    fn=identity,
+    title="Audio Denoiser using DeepFilterNet V3",
+    description="Implemented audio denoising using DeepFilterNet V3, enabled processing of larger files even on cpu, by splitting up the audio file into chunks of 1 minute each.",
+    inputs=gr.Audio(type='filepath'),
+    outputs=gr.Audio(label="Output Audio"),
+)
+demo.launch()