use logger

.flake8

@@ -0,0 +1,17 @@
+[flake8]
+ignore = E203, E266, E501, W503
+max-line-length = 100
+import-order-style = google
+application-import-names = flake8
+select = B,C,E,F,W,T4,B9
+exclude =
+  .tox,
+  .git,
+  __pycache__,
+  docs,
+  sbatch,
+  .venv,
+  *.pyc,
+  *.egg-info,
+  .cache,
+  .eggs

app.py

@@ -8,6 +8,8 @@ import markdown
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
+from loguru import logger
+
 from df import config
 from df.enhance import enhance, init_df, load_audio, save_audio
 from df.utils import resample
@@ -55,14 +57,13 @@ def mix_at_snr(clean, noise, snr, eps=1e-10):
 
 def mix_and_denoise(speech_rec, speech_upl, noise, snr):
     sr = config("sr", 48000, int, section="df")
-    print(speech_rec, speech_upl, noise, snr)
+    logger.info(f"Got parameters speech_rec: {speech_rec}, speech_upl: {speech_upl}, noise: {noise}, snr: {snr}")
     if noise is None:
         noise = "samples/dkitchen.wav"
     sp_kwargs = {}
     if speech_rec is None or "none" in speech_rec:
         speech_file = "samples/p232_013_clean.wav"
         if speech_upl is not None and "none" not in speech_upl:
-            print("using speech_upl")
             speech_file = speech_upl
     else:
         speech_file = speech_rec
@@ -70,7 +71,7 @@ def mix_and_denoise(speech_rec, speech_upl, noise, snr):
     try:
         speech, meta = load_audio(speech_file, sr, **sp_kwargs)
     except RuntimeError as e:
-        print("Could not load audio:", e)
+        logger.error("Could not load audio: " + str(e))
         import os
 
         print(os.path.getsize(speech_file))
@@ -78,16 +79,16 @@ def mix_and_denoise(speech_rec, speech_upl, noise, snr):
         print(os.path.getctime(speech_file))
         raise e
 
-    print(f"Loaded speech with shape {speech.shape}")
+    logger.info(f"Loaded speech with shape {speech.shape}")
     noise, _ = load_audio(noise, sr)
     if meta.sample_rate != sr:
         # Low pass filter by resampling
         noise = resample(resample(noise, sr, meta.sample_rate), meta.sample_rate, sr)
-    print(f"Loaded noise with shape {noise.shape}")
+    logger.info(f"Loaded noise with shape {noise.shape}")
     speech, noise, noisy = mix_at_snr(speech, noise, snr)
-    print("Start denoising audio")
+    logger.info("Start denoising audio")
     enhanced = enhance(model, df, noisy)
-    print("Denoising finished")
+    logger.info("Denoising finished")
     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
@@ -99,7 +100,7 @@ def mix_and_denoise(speech_rec, speech_upl, noise, snr):
     save_audio(noisy_fn, noisy, sr)
     enhanced_fn = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
     save_audio(enhanced_fn, enhanced, sr)
-    print("saved audios", noisy_fn, enhanced_fn)
+    logger.info(f"saved audios: {noisy_fn}, {enhanced_fn}")
     return (
         noisy_fn,
         spec_figure(noisy, sr=sr),
@@ -198,10 +199,7 @@ def spec_figure(
         ckwargs = {}
         if "ax" in kwargs:
             if colorbar_format is None:
-                if (
-                    kwargs.get("vmin", None) is not None
-                    or kwargs.get("vmax", None) is not None
-                ):
+                if kwargs.get("vmin", None) is not None or kwargs.get("vmax", None) is not None:
                     colorbar_format = "%+2.0f dB"
             ckwargs = {"ax": kwargs["ax"]}
         plt.colorbar(im, format=colorbar_format, **ckwargs)
@@ -248,9 +246,7 @@ outputs = [
     gradio.outputs.Audio(label="Enhanced"),
     gradio.outputs.Image(type="plot"),
 ]
-description = (
-    "This demo denoises audio files using DeepFilterNet. Try it with your own voice!"
-)
+description = "This demo denoises audio files using DeepFilterNet. Try it with your own voice!"
 iface = gradio.Interface(
     fn=mix_and_denoise,
     title="DeepFilterNet Demo",

pyproject.toml

@@ -0,0 +1,10 @@
+[tool.black]
+line-length = 100
+target-version = ["py37", "py38", "py39", "py310"]
+include = '\.pyi?$'
+
+[tool.isort]
+profile = "black"
+line_length = 100
+skip_gitignore = true
+known_first_party = ["df", "libdf", "libdfdata"]