fix app.py

create upload tool

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 from pathlib import Path
 from denoisers.SpectralGating import SpectralGating
 
-
+model = SpectralGating()
 
 def denoising_transform(audio):
     src_path = Path(__file__).parent.resolve() / Path("cache_wav/original/{}.wav".format(str(uuid.uuid4())))
@@ -32,6 +32,5 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    model = SpectralGating()
     demo.launch()
 

train.py

@@ -34,7 +34,8 @@ def init_wandb(cfg):
 def train(cfg: DictConfig):
     device = torch.device(f'cuda:{cfg.gpu}' if torch.cuda.is_available() else 'cpu')
     init_wandb(cfg)
-    checkpoint_saver = CheckpointSaver(dirpath=cfg['training']['model_save_path'], run_name=wandb.run.name)
+    checkpoint_saver = CheckpointSaver(dirpath=cfg['training']['model_save_path'], run_name=wandb.run.name,
+                                       decreasing=False)
     metrics = Metrics(source_rate=cfg['dataloader']['sample_rate']).to(device)
 
     model = get_model(cfg['model']).to(device)

upload_to_HF.py

@@ -0,0 +1,56 @@
+import os
+import wandb
+from huggingface_hub import HfApi
+from pathlib import Path
+import huggingface_hub
+import ssl
+import os
+os.environ['CURL_CA_BUNDLE'] = ''
+
+ssl._create_default_https_context = ssl._create_unverified_context
+
+class Uploader:
+    def __init__(self, entity, project, run_name, repo_id, username):
+        self.entity = entity
+        self.project = project
+        self.run_name = run_name
+        self.hf_api = HfApi()
+        self.wandb_api = wandb.Api()
+        self.repo_id = repo_id
+        self.username = username
+        huggingface_hub.login(os.environ.get('HUGGINGFACE_TOKEN'))
+
+    def get_model_from_wandb_run(self):
+        runs = self.wandb_api.runs(f"{self.entity}/{self.project}",
+                        # order='+summary_metrics.train_pesq'
+                        )
+        run = [run for run in runs if run.name == self.run_name][0]
+        artifacts = run.logged_artifacts()
+        best_model = [artifact for artifact in artifacts if artifact.type == 'model'][0]
+        artifact_dir = best_model.download()
+        model_path = list(Path(artifact_dir).glob("*.pt"))[0].absolute().as_posix()
+        print(f"Model validation score = {best_model.metadata['Validation score']}")
+        return model_path
+
+    def upload_to_HF(self):
+        model_path = self.get_model_from_wandb_run()
+        self.hf_api.upload_file(
+            path_or_fileobj=model_path,
+            path_in_repo=Path(model_path).name,
+            repo_id=f'{self.username}/{self.repo_id}',
+        )
+
+    def create_repo(self):
+        self.hf_api.create_repo(repo_id=self.repo_id, exist_ok=True)
+
+
+
+if __name__ == '__main__':
+    uploader = Uploader(entity='borisovmaksim',
+                        project='denoising',
+                        run_name='wav_normalization',
+                        repo_id='demucs',
+                        username='BorisovMaksim')
+    uploader.create_repo()
+    uploader.upload_to_HF()
+