[81] - add worker for media clustering

src/core/models/media_factory.py

@@ -83,7 +83,7 @@ def make_from_url(video_url):
             try:
                 print("Downloading video from URL")
                 wget.download(video_url, out=file_path)
-                print("Video downloaded")
+                print("\nVideo downloaded")
             except Exception as e:
                 print("Error downloading video:", e)
                 raise Exception("Error Downloading Video")
@@ -95,7 +95,7 @@ def make_from_url(video_url):
             try:
                 print("Downloading video from S3")
                 AWSS3Utils.download_file_from_s3(bucket_name, file_key, file_path)
-                print("Video downloaded")
+                print("\nVideo downloaded")
             except Exception as e:
                 print("Error downloading video from S3:", e)
                 raise Exception("Error Downloading Video")
@@ -126,7 +126,7 @@ def make_from_url(audio_url):
             try:
                 print("Downloading audio from URL")
                 wget.download(audio_url, out=file_path)
-                print("Audio downloaded")
+                print("\nAudio downloaded")
             except Exception as e:
                 print("Error downloading audio:", e)
                 raise Exception("Error Downloading audio")
@@ -138,13 +138,13 @@ def make_from_url(audio_url):
             try:
                 print("Downloading audio from S3")
                 AWSS3Utils.download_file_from_s3(bucket_name, file_key, file_path)
-                print("Audio downloaded")
+                print("\nAudio downloaded")
             except Exception as e:
                 print("Error downloading audio from S3:", e)
                 raise Exception("Error Downloading audio")
 
         return {"path": file_path}
-    
+
     @staticmethod
     def make_from_url_to_wav(audio_url):
         temp_dir = tempfile.gettempdir()
@@ -156,7 +156,7 @@ def make_from_url_to_wav(audio_url):
             print("Downloading audio from URL")
             wget.download(audio_url, out=audio_file)
             print("\naudio downloaded")
-            
+
             _, file_extension = os.path.splitext(file_name)
             if file_extension != '.wav':
                 audio = AudioSegment.from_file(audio_file, format=file_extension[1:])
@@ -172,7 +172,7 @@ def make_from_url_to_wav(audio_url):
     @staticmethod
     def make_from_file_on_disk(audio_path):
         return {"path": audio_path}
-    
+
 
 
 media_factory = {

src/core/operators/audio_vec_embedding_clap.py

@@ -1,53 +1,68 @@
-"""Operator to get audio representation using LAION-CLAP - https://github.com/LAION-AI/CLAP. """
+"""
+Operator to get audio representation using LAION-CLAP - https://huggingface.co/laion/larger_clap_general
+"""
 
 def initialize(param):
     """
     Initializes the operator.
     
     Args: 
-        param (dict): A dict to initialize and load the the model.
+        param (dict): A dict to initialize and load the model.
     
     """
-    global model
-    global librosa
-    global np
-    global contextmanager
-    global os
+    global model, processor, librosa, contextmanager, os, torch, device
 
-    import numpy as np
     import librosa
     from contextlib import contextmanager
     import os
-    import laion_clap
+    from transformers import ClapModel, ClapProcessor
+    import torch
+
+    # Load the model and processor
+    model = ClapModel.from_pretrained("laion/larger_clap_general")
+    processor = ClapProcessor.from_pretrained("laion/larger_clap_general")
 
-    model = laion_clap.CLAP_Module()
-    model.load_ckpt() # load the best checkpoint (HTSAT model) in the paper.
-    print("model successfully downloaded")
+    # Set the device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    print("audio CLAP Model successfully initialized and loaded onto", device)
 
 
 def run(audio_file):
     """
-    Runs the operator and compute inference on the audio file.
+    Runs the operator and computes inference on the audio file.
 
     Args:
         audio_file (dict): `AudioFactory` file object.
 
     Returns:
-        audio_emb (numpy.ndarray): A 512-length vector embedding representing the audio. 
+        audio_emb (list): A 512-length vector embedding representing the audio. 
 
     """
     audio = audio_file["path"]
 
     @contextmanager
     def audio_load(fname):
+        """
+        Loads audio and removes the file after use.
+
+        Args:
+            fname (str): Path to the audio file.
+        
+        Yields:
+            numpy.ndarray: Loaded audio data.
+        """
         a, _ = librosa.load(fname, sr=48000)
         try:
             yield a
         finally:
             os.remove(fname)
 
     with audio_load(audio) as audio_var:
-        query_audio = audio_var.reshape(1, -1)
-        audio_emb = model.get_audio_embedding_from_data(x = query_audio, use_tensor=False)
-        audio_emb = audio_emb.reshape(-1)
+        inputs = processor(audios=audio_var, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()} 
+        with torch.no_grad():
+            audio_emb = model.get_audio_features(**inputs)
+        audio_emb = audio_emb.squeeze(0).tolist()
         return audio_emb

src/core/operators/audio_vec_embedding_clap_requirements.in

@@ -1,3 +1,3 @@
-laion-clap==1.1.6
 librosa==0.10.2.post1
-torchvision==0.19.0
+transformers==4.44.0
+torch==2.4.0