diff --git a/mteb/models/msclap_models.py b/mteb/models/msclap_models.py
index 6671cbcb2a..069058a426 100644
--- a/mteb/models/msclap_models.py
+++ b/mteb/models/msclap_models.py
@@ -21,7 +21,7 @@
 class MSClapWrapper:
     def __init__(
         self,
-        model_name: str = "microsoft/msclap",
+        model_name: str = "microsoft/msclap-2023",
         device: str = "cuda" if torch.cuda.is_available() else "cpu",
         **kwargs: Any,
     ):
@@ -188,7 +188,7 @@ def _process_audio_batch(self, batch) -> list[np.ndarray]:
             audio_tensor = audio_tensor.to(self.device)
             # Get embeddings using the internal audio encoder
             with torch.no_grad():
-                # Use the internal method
+                # Use the internal method: [0] the audio emebdding, [1] has output class probabilities
                 audio_features = self.model.clap.audio_encoder(audio_tensor)[0]
 
                 # Normalize embeddings
@@ -221,15 +221,13 @@ def get_text_embeddings(
 
     def encode(
         self,
-        inputs: AudioBatch | list[str],
+        inputs: list[str],
         *,
         task_name: str,
         prompt_type: PromptType | None = None,
         **kwargs: Any,
     ) -> np.ndarray:
-        if isinstance(inputs[0], str):
-            return self.get_text_embeddings(inputs)
-        return self.get_audio_embeddings(inputs)
+        return self.get_text_embeddings(inputs, **kwargs)
 
 
 # Microsoft CLAP Model metadata
diff --git a/mteb/models/muq_mulan_model.py b/mteb/models/muq_mulan_model.py
index 842cb38102..0c2196b77f 100644
--- a/mteb/models/muq_mulan_model.py
+++ b/mteb/models/muq_mulan_model.py
@@ -203,16 +203,14 @@ def get_text_embeddings(
 
     def encode(
         self,
-        inputs: AudioBatch | list[str],
+        inputs: list[str],
         *,
         task_name: str,
         prompt_type: PromptType | None = None,
         **kwargs: Any,
     ) -> np.ndarray:
         """Encode inputs (audio or text) into embeddings."""
-        if isinstance(inputs[0], str):
-            return self.get_text_embeddings(inputs)
-        return self.get_audio_embeddings(inputs)
+        return self.get_text_embeddings(inputs, **kwargs)
 
     def calc_similarity(
         self, audio_embeds: np.ndarray, text_embeds: np.ndarray
diff --git a/mteb/models/speecht5_models.py b/mteb/models/speecht5_models.py
index 0b5b1934e2..0693377f23 100644
--- a/mteb/models/speecht5_models.py
+++ b/mteb/models/speecht5_models.py
@@ -197,15 +197,13 @@ def get_text_embeddings(
 
     def encode(
         self,
-        inputs: AudioBatch,
+        inputs: list[str],
         *,
         task_name: str,
         prompt_type: PromptType | None = None,
         **kwargs: Any,
     ) -> np.ndarray:
-        if isinstance(inputs[0], str):
-            return self.get_text_embeddings(inputs).numpy()
-        return self.get_audio_embeddings(inputs).numpy()
+        return self.get_text_embeddings(inputs, **kwargs).numpy()
 
 
 # ASR model - Optimized for Speech Recognition tasks
diff --git a/mteb/models/wav2clip_model.py b/mteb/models/wav2clip_model.py
index 4d6cf991a1..5964a367b9 100644
--- a/mteb/models/wav2clip_model.py
+++ b/mteb/models/wav2clip_model.py
@@ -23,12 +23,12 @@ def __init__(
         **kwargs: Any,
     ):
         requires_package(self, "wav2clip", "pip install 'mteb[wav2clip]'")
-        import wav2clip
+        from wav2clip import embed_audio, get_model
 
-        self.wav2clip = wav2clip
+        self.embed_audio = embed_audio
         # audio side
         self.device = device
-        self.audio_model = self.wav2clip.get_model().to(device)
+        self.audio_model = get_model().to(device)
         self.sampling_rate = 16_000
 
         # text side (CLIP)
@@ -112,7 +112,7 @@ def get_audio_embeddings(
                 for wav in wavs:
                     # Process one audio at a time to avoid memory issues
                     wav_np = wav.unsqueeze(0).cpu().numpy()  # Add batch dimension
-                    embed = self.wav2clip.embed_audio(wav_np, self.audio_model)
+                    embed = self.embed_audio(wav_np, self.audio_model)
 
                     # Normalize
                     norm = np.linalg.norm(embed, axis=-1, keepdims=True)
@@ -153,15 +153,13 @@ def get_text_embeddings(
 
     def encode(
         self,
-        inputs: AudioBatch | list[str],
+        inputs: list[str],
         *,
         task_name: str,
         prompt_type: PromptType | None = None,
         **kwargs: Any,
     ) -> np.ndarray:
-        if isinstance(inputs[0], str):
-            return self.get_text_embeddings(inputs)
-        return self.get_audio_embeddings(inputs)
+        return self.get_text_embeddings(inputs, **kwargs)
 
 
 wav2clip_zero = ModelMeta(