added demucs to app

app.py

@@ -3,34 +3,47 @@ import ffmpeg
 import gradio as gr
 from pathlib import Path
 from denoisers.SpectralGating import SpectralGating
+from huggingface_hub import hf_hub_download
+from denoisers.demucs import Demucs
+import hydra
+from omegaconf import DictConfig
+import torch
 
-model = SpectralGating()
 
-def denoising_transform(audio):
-    src_path = Path(__file__).parent.resolve() / Path("cache_wav/original/{}.wav".format(str(uuid.uuid4())))
-    tgt_path = Path(__file__).parent.resolve() / Path("cache_wav/denoised/{}.wav".format(str(uuid.uuid4())))
-    src_path.parent.mkdir(exist_ok=True, parents=True)
-    tgt_path.parent.mkdir(exist_ok=True, parents=True)
-    (ffmpeg.input(audio)
-            .output(src_path.as_posix(), acodec='pcm_s16le', ac=1, ar=22050)
-            .run()
+
+@hydra.main(version_base=None, config_path="conf", config_name="config")
+def run_app(cfg: DictConfig):
+    model = Demucs(cfg['model'])
+    model_path = hf_hub_download(repo_id="BorisovMaksim/demucs", filename="Demucs_original_sr_epoch3.pt")
+    checkpoint = torch.load(model_path)
+    model.load_state_dict(checkpoint['model_state_dict'])
+
+    def denoising_transform(audio):
+        src_path = Path(__file__).parent.resolve() / Path("cache_wav/original/{}.wav".format(str(uuid.uuid4())))
+        tgt_path = Path(__file__).parent.resolve() / Path("cache_wav/denoised/{}.wav".format(str(uuid.uuid4())))
+        src_path.parent.mkdir(exist_ok=True, parents=True)
+        tgt_path.parent.mkdir(exist_ok=True, parents=True)
+        (ffmpeg.input(audio)
+         .output(src_path.as_posix(), acodec='pcm_s16le', ac=1, ar=22050)
+         .run()
+         )
+        model.predict(audio, tgt_path)
+        return tgt_path
+
+    demo = gr.Interface(
+        fn=denoising_transform,
+        inputs=gr.Audio(label="Source Audio", source="microphone", type='filepath'),
+        outputs=gr.Audio(label="Target Audio", type='filepath'),
+        examples=[
+            ["testing/wavs/p232_071.wav"],
+            ["testing/wavs/p232_284.wav"],
+        ],
+        title="Denoising"
     )
-    model.predict(audio, tgt_path)
-    return tgt_path
-
-
-demo = gr.Interface(
-    fn=denoising_transform,
-    inputs=gr.Audio(label="Source Audio", source="microphone", type='filepath'),
-    outputs=gr.Audio(label="Target Audio", type='filepath'),
-    examples=[
-        ["testing/wavs/p232_071.wav"],
-        ["testing/wavs/p232_284.wav"],
-    ],
-    title="Denoising",
-    interpretation="default",
-)
 
-if __name__ == "__main__":
+
     demo.launch()
 
+if __name__ == "__main__":
+    run_app()
+

requirements.txt

@@ -1,4 +1,6 @@
 ffmpeg-python
 noisereduce
 torch
-torchaudio
+torchaudio
+hydra
+omegaconf