change in model config

.gitattributes

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 model.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
 vocals.wav filter=lfs diff=lfs merge=lfs -text
+pretrained_models/2stems/model.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1 +1,2 @@
-env/
+env/
+__pycache__/

custom_model.py

@@ -25,7 +25,7 @@ class SpleeterModel(PreTrainedModel):
         Returns:
             dict: Separated stems.
         """
-        return self.separator.separate(audio_path)
+        return self.separator.separate_to_file(audio_path, "separated_audio")
 
 
 AutoConfig.register("spleeter", SpleeterConfig)

pretrained_models/2stems/.probe

@@ -0,0 +1 @@
+OK

pretrained_models/2stems/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "model"
+all_model_checkpoint_paths: "model"

pretrained_models/2stems/model.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7747f9fd2c782306dbec1504360fbb645a097a48446f23486c3ff9c89bc11788
+size 78614080

test.py

@@ -1,21 +1,6 @@
-# from transformers import AutoConfig, AutoModel
-
-# config = AutoConfig.from_pretrained("niral-env/youtube_spleeter")
-# print(config)
-# model = AutoModel.from_pretrained("niral-env/youtube_spleeter", config=config)
-# print(model)
-
-
-# Load model directly
-# from transformers import AutoModel
-# model = AutoModel.from_pretrained("niral-env/youtube_spleeter")
-
-# print(model)
-
 from transformers import AutoConfig, AutoModel
 from custom_model import SpleeterModel
 
-# Load configuration and model
 config = AutoConfig.from_pretrained("niral-env/youtube_spleeter")
 print("----"*30)
 print(config)
@@ -23,12 +8,15 @@ print("----"*30)
 
 model = SpleeterModel(config)
 
-# Process an audio file
-# result = model.forward("example.wav")
-
-# Output separated stems
 print(model)
 result = model.forward("vocals.wav")
 
-# Output separated stems
-print(result)
+print(result)
+
+
+# from transformers import AutoModel
+# model = AutoModel.from_pretrained("niral-env/youtube_spleeter")
+# print(model)
+# result = model.forward("vocals.wav")
+
+# print(result)