[ADD] config file for filepaths

CONFIG.yaml

@@ -0,0 +1,8 @@
+######################################
+# PATHS FOR THE DIFFERENT COMPONENTS #
+######################################
+
+# List of paths to initialise the model
+path_tokenizer_file: "./assets/bpe_simple_vocab_16e6.txt.gz" 
+path_audioclip: './assets/AudioCLIP-Full-Training.pt' 
+path_snowmobile_model: "./assets/snowmobile_model.pth"

analyze.sh

@@ -12,8 +12,9 @@ exec docker run \
     --gpus all \
     -v ./logs:/app/logs \
     -v "$FOLDER_TO_EXPOSE":/data \
-    ghcr.io/ninanor/snowmobile_analyzer:main \
+    snowmobile \
     --input /data/"$FILENAME"
 
+#ghcr.io/ninanor/snowmobile_analyzer:main 
 
 

audioclip/model/custom_model.py

@@ -6,11 +6,17 @@
 
 from model import AudioCLIP
 
+# Open the config file
+import yaml
+from yaml import FullLoader
+with open("./CONFIG.yaml") as f:
+    cfg = yaml.load(f, Loader=FullLoader)
+
 class CustomAudioCLIP(pl.LightningModule):
     def __init__(self, num_target_classes, model_arguments=None):
         super().__init__()
         model_arguments = {} if model_arguments is None else model_arguments
-        self.aclp = AudioCLIP(pretrained=f'/app/assets/AudioCLIP-Full-Training.pt', **model_arguments)
+        self.aclp = AudioCLIP(pretrained=cfg["path_audioclip"], **model_arguments)
         self.num_target_classes = num_target_classes
         self._build_model()
 

audioclip/utils/simple_tokenizer.py

@@ -8,10 +8,16 @@
 import ftfy
 import regex as re
 
+# Open the config file
+import yaml
+from yaml import FullLoader
+with open("./CONFIG.yaml") as f:
+    cfg = yaml.load(f, Loader=FullLoader)
 
 @lru_cache()
 def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'assets', 'bpe_simple_vocab_16e6.txt.gz')
+    return cfg["path_tokenizer_file"]
+    #return os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'assets', 'bpe_simple_vocab_16e6.txt.gz')
 
 
 @lru_cache()

example/SNOWMOBILE_RESULTS/example_audio_ANALYZED.csv

@@ -0,0 +1,19 @@
+start_detection,end_detection,label,confidence,hr
+0,3,1,0.97691464,0.19687336119166438
+3,6,1,0.9611957,0.16774687365839228
+6,9,1,0.9939383,0.36043088267512735
+9,12,1,0.99498135,0.47373295662640913
+12,15,1,0.99076325,0.3646539816725187
+15,18,1,0.99004626,0.17752296324623074
+21,24,1,0.999651,0.29822320508183836
+24,27,1,0.99951804,0.3069427548594896
+27,30,1,0.99987257,0.32149264560634827
+30,33,1,0.9999827,0.3938179195958813
+33,36,1,0.9997253,0.38632521474145276
+36,39,1,0.9927994,0.685510328529914
+39,42,1,0.9832097,0.5133540409044806
+42,45,1,0.9991579,0.42189265222346845
+45,48,1,0.99997604,0.17324206247276597
+48,51,1,0.9999901,0.2518335613798428
+51,54,1,0.9902189,0.15691590619107337
+54,57,1,0.98673064,0.1867643074938098

src/predict.py

@@ -13,6 +13,12 @@
 import traceback
 import logging
 
+# Open the config file
+import yaml
+from yaml import FullLoader
+with open("./CONFIG.yaml") as f:
+    cfg = yaml.load(f, Loader=FullLoader)
+
 logging.basicConfig(filename='logs/logfile.log', level=logging.INFO,
                     format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -193,7 +199,7 @@ def analyzeFile(
 
     # Initiate model
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model_path = "/app/assets/snowmobile_model.pth"
+    model_path = cfg["path_snowmobile_model"]
 
     model = initModel(model_path=model_path, device=device)