diff --git a/gigaam/decoding.py b/gigaam/decoding.py
index 6f9d239..150a166 100644
--- a/gigaam/decoding.py
+++ b/gigaam/decoding.py
@@ -61,17 +61,18 @@ def decode(self, head: CTCHead, encoded: Tensor, lengths: Tensor) -> List[str]:
         labels = log_probs.argmax(dim=-1, keepdim=False)
 
         skip_mask = labels != self.blank_id
-        skip_mask[:, 1:] = torch.logical_and(
-            skip_mask[:, 1:], labels[:, 1:] != labels[:, :-1]
-        )
+        skip_mask[:, 1:] &= (labels[:, 1:] != labels[:, :-1])
         for i, length in enumerate(lengths):
             skip_mask[i, length:] = 0
 
         pred_texts: List[str] = []
         for i in range(b):
-            pred_texts.append(
-                "".join(self.tokenizer.decode(labels[i][skip_mask[i]].cpu().tolist()))
-            )
+            valid_labels = labels[i][skip_mask[i]]
+            if len(valid_labels) > 0:
+                label_list = valid_labels.cpu().tolist()
+                pred_texts.append(self.tokenizer.decode(label_list))
+            else:
+                pred_texts.append("")
         return pred_texts
 
 
diff --git a/gigaam/model.py b/gigaam/model.py
index ac38c1d..9329e62 100644
--- a/gigaam/model.py
+++ b/gigaam/model.py
@@ -5,7 +5,7 @@
 import torch
 from torch import Tensor, nn
 
-from .preprocess import SAMPLE_RATE, load_audio
+from .preprocess import SAMPLE_RATE, load_audio, load_audio_from_bytes
 from .utils import onnx_converter
 
 LONGFORM_THRESHOLD = 25 * SAMPLE_RATE
@@ -101,6 +101,23 @@ def transcribe(self, wav_file: str) -> str:
         encoded, encoded_len = self.forward(wav, length)
         return self.decoding.decode(self.head, encoded, encoded_len)[0]
 
+    @torch.inference_mode()
+    def transcribe_bytes(self, audio_bytes: bytes) -> str:
+        """
+        Transcribes raw PCM16 audio bytes directly into text.
+        This is the fastest method for in-memory processing.
+        """
+
+        wav = load_audio_from_bytes(audio_bytes)
+        wav = wav.to(self._device).to(self._dtype).unsqueeze(0)
+        length = torch.full([1], wav.shape[-1], device=self._device)
+
+        if length.item() > LONGFORM_THRESHOLD:
+            raise ValueError("Too long audio, use 'transcribe_longform' method.")
+
+        encoded, encoded_len = self.forward(wav, length)
+        return self.decoding.decode(self.head, encoded, encoded_len)[0]
+
     def forward_for_export(self, features: Tensor, feature_lengths: Tensor) -> Tensor:
         """
         Encoder-decoder forward to save model entirely in onnx format.
diff --git a/gigaam/preprocess.py b/gigaam/preprocess.py
index fb6ebde..3be0e8f 100644
--- a/gigaam/preprocess.py
+++ b/gigaam/preprocess.py
@@ -39,6 +39,19 @@ def load_audio(audio_path: str, sample_rate: int = SAMPLE_RATE) -> Tensor:
         warnings.simplefilter("ignore", category=UserWarning)
         return torch.frombuffer(audio, dtype=torch.int16).float() / 32768.0
 
+def load_audio_from_bytes(audio_bytes: bytes, device: torch.device = None) -> Tensor:
+    """
+    Load audio directly from PCM16 bytes without any external tools.
+    This is the fastest method for in-memory audio processing.
+    """
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=UserWarning)
+        audio_tensor = torch.frombuffer(audio_bytes, dtype=torch.int16).float() / 32768.0
+
+        if device is not None:
+            audio_tensor = audio_tensor.to(device)
+
+        return audio_tensor
 
 class SpecScaler(nn.Module):
     """