init

model_speaker_embedding.py

@@ -11,12 +11,13 @@ from transformers import AutoModel, AutoFeatureExtractor
 # W2V BERT #
 ############
 class W2VBERTEmbedding:
-    def __init__(self, ckpt: str = "facebook/w2v-bert-2.0"):
+    def __init__(self, ckpt: str = "facebook/w2v-bert-2.0", mean_pool: bool = True):
         self.processor = AutoFeatureExtractor.from_pretrained(ckpt)
         self.model = AutoModel.from_pretrained(ckpt)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)
         self.model.eval()
+        self.mean_pool = mean_pool
 
     def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
         # audio file is decoded on the fly
@@ -25,7 +26,9 @@ class W2VBERTEmbedding:
         inputs = self.processor(wav, sampling_rate=self.processor.sampling_rate, return_tensors="pt")
         with torch.no_grad():
             outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()})
-        return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
+        if self.mean_pool:
+            return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
+        return outputs.last_hidden_state.cpu().numpy()[0]
 
 
 ##########