diff --git a/modules/diarize/audio_loader.py b/modules/diarize/audio_loader.py
index 6db3cc64..2efd4216 100644
--- a/modules/diarize/audio_loader.py
+++ b/modules/diarize/audio_loader.py
@@ -2,6 +2,8 @@
 import subprocess
 from functools import lru_cache
 from typing import Optional, Union
+from scipy.io.wavfile import write
+import tempfile
 
 import numpy as np
 import torch
@@ -24,32 +26,43 @@ def exact_div(x, y):
 TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
 
 
-def load_audio(file: str, sr: int = SAMPLE_RATE):
+def load_audio(file: Union[str, np.ndarray], sr: int = SAMPLE_RATE) -> np.ndarray:
     """
-    Open an audio file and read as mono waveform, resampling as necessary
+    Open an audio file or process a numpy array containing audio data as mono waveform, resampling as necessary.
 
     Parameters
     ----------
-    file: str
-        The audio file to open
+    file: Union[str, np.ndarray]
+        The audio file to open or a numpy array containing the audio data.
 
     sr: int
-        The sample rate to resample the audio if necessary
+        The sample rate to resample the audio if necessary.
 
     Returns
     -------
     A NumPy array containing the audio waveform, in float32 dtype.
     """
+    if isinstance(file, np.ndarray):
+        if file.dtype != np.float32:
+            file = file.astype(np.float32)
+        if file.ndim > 1:
+            file = np.mean(file, axis=1)
+
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+        write(temp_file.name, SAMPLE_RATE, (file * 32768).astype(np.int16))
+        temp_file_path = temp_file.name
+        temp_file.close()
+    else:
+        temp_file_path = file
+
     try:
-        # Launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI to be installed.
         cmd = [
             "ffmpeg",
             "-nostdin",
             "-threads",
             "0",
             "-i",
-            file,
+            temp_file_path,
             "-f",
             "s16le",
             "-ac",
@@ -63,6 +76,9 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
         out = subprocess.run(cmd, capture_output=True, check=True).stdout
     except subprocess.CalledProcessError as e:
         raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+    finally:
+        if isinstance(file, np.ndarray):
+            os.remove(temp_file_path)
 
     return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
 
diff --git a/modules/diarize/diarizer.py b/modules/diarize/diarizer.py
index c12c2357..592e1bf7 100644
--- a/modules/diarize/diarizer.py
+++ b/modules/diarize/diarizer.py
@@ -1,6 +1,7 @@
 import os
 import torch
-from typing import List
+from typing import List, Union, BinaryIO
+import numpy as np
 import time
 import logging
 
@@ -20,7 +21,7 @@ def __init__(self,
         self.pipe = None
 
     def run(self,
-            audio: str,
+            audio: Union[str, BinaryIO, np.ndarray],
             transcribed_result: List[dict],
             use_auth_token: str,
             device: str