diff --git a/.gitignore b/.gitignore
index 7dbdfee6a..3076b7d65 100644
--- a/.gitignore
+++ b/.gitignore
@@ -168,4 +168,7 @@ cython_debug/
 #.idea/
 
 # PyPI configuration file
-.pypirc
\ No newline at end of file
+.pypirc
+
+# Models files added for offline diarization
+models/*.bin
diff --git a/README.md b/README.md
index 08eb0757e..0f009330f 100644
--- a/README.md
+++ b/README.md
@@ -54,6 +54,7 @@ This repository provides fast automatic speech recognition (70x realtime with la
 
 <h2 align="left", id="highlights">New🚨</h2>
 
+- Offline diarization support added! Use local models without Hugging Face API access.
 - 1st place at [Ego4d transcription challenge](https://eval.ai/web/challenges/challenge-page/1637/leaderboard/3931/WER)  🏆
 - _WhisperX_ accepted at INTERSPEECH 2023 
 - v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization
@@ -110,6 +111,19 @@ To **enable Speaker Diarization**, include your Hugging Face access token (read)
 > **Note**<br>
 > As of Oct 11, 2023, there is a known issue regarding slow performance with pyannote/Speaker-Diarization-3.0 in whisperX. It is due to dependency conflicts between faster-whisper and pyannote-audio 3.0.0. Please see [this issue](https://github.com/m-bain/whisperX/issues/499) for more details and potential workarounds.
 
+#### Offline Diarization
+
+You can now use offline diarization without needing a Hugging Face token. This requires:
+
+1. Download the necessary model files to a local `models` directory
+2. Configure the diarization pipeline using the provided `models/pyannote_diarization_config.yaml` file
+
+To use offline diarization with the command line:
+
+```bash
+whisperx path/to/audio.wav --model large-v2 --diarize --diarize_offline --diarize_config models/pyannote_diarization_config.yaml
+```
+
 <h2 align="left" id="example">Usage 💬 (command line)</h2>
 
 ### English
@@ -137,6 +151,10 @@ To label the transcript with speaker ID's (set number of speakers if known e.g.
 
     whisperx path/to/audio.wav --model large-v2 --diarize --highlight_words True
 
+To use offline diarization mode:
+
+    whisperx path/to/audio.wav --model large-v2 --diarize --diarize_offline --diarize_config models/pyannote_diarization_config.yaml
+
 To run on CPU instead of GPU (and for running on Mac OS X):
 
     whisperx path/to/audio.wav --compute_type int8
@@ -192,8 +210,15 @@ print(result["segments"]) # after alignment
 # import gc; gc.collect(); torch.cuda.empty_cache(); del model_a
 
 # 3. Assign speaker labels
+# Option 1: HF-token based diarization (online)
 diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)
 
+# Option 2: Local/offline diarization
+# diarize_model = whisperx.OfflineDiarizationPipeline(
+#    config_path="models/pyannote_diarization_config.yaml",
+#    device=device
+# )
+
 # add min/max number of speakers if known
 diarize_segments = diarize_model(audio)
 # diarize_model(audio, min_speakers=min_speakers, max_speakers=max_speakers)
@@ -267,6 +292,8 @@ Bug finding and pull requests are also highly appreciated to keep this project g
 
 * [x] Allow silero-vad as alternative VAD option
 
+* [x] Add offline diarization support
+
 * [ ] Improve diarization (word level). *Harder than first thought...*
 
 
diff --git a/models/pyannote_diarization_config.yaml b/models/pyannote_diarization_config.yaml
new file mode 100644
index 000000000..d54512b4e
--- /dev/null
+++ b/models/pyannote_diarization_config.yaml
@@ -0,0 +1,19 @@
+version: 3.1.0
+
+pipeline:
+  name: pyannote.audio.pipelines.SpeakerDiarization
+  params:
+    clustering: AgglomerativeClustering
+    embedding: models/pyannote_model_wespeaker-voxceleb-resnet34-LM.bin
+    embedding_batch_size: 32
+    embedding_exclude_overlap: true
+    segmentation: models/pyannote_model_segmentation-3.0.bin
+    segmentation_batch_size: 32
+
+params:
+  clustering:
+    method: centroid
+    min_cluster_size: 12
+    threshold: 0.7045654963945799
+  segmentation:
+    min_duration_off: 0.0
diff --git a/whisperx/offline_diarize.py b/whisperx/offline_diarize.py
new file mode 100644
index 000000000..cd6d201f4
--- /dev/null
+++ b/whisperx/offline_diarize.py
@@ -0,0 +1,65 @@
+import os
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import torch
+from pyannote.audio import Pipeline
+
+from .audio import load_audio, SAMPLE_RATE
+
+class OfflineDiarizationPipeline:
+    def __init__(
+        self,
+        config_path,
+        device="cpu",
+    ):
+        if isinstance(device, str):
+            device = torch.device(device)
+            
+        # Load the pipeline with local config
+        self.model = self._load_pipeline_from_pretrained(config_path).to(device)
+
+    def _load_pipeline_from_pretrained(self, path_to_config):
+        path_to_config = Path(path_to_config)
+        
+        if not path_to_config.exists():
+            raise FileNotFoundError(f"Config file not found: {path_to_config}")
+        
+        print(f"Loading pyannote pipeline from {path_to_config}...")
+        # the paths in the config are relative to the current working directory
+        # so we need to change the working directory to the model path
+        # and then change it back
+        
+        cwd = Path.cwd().resolve()  # store current working directory
+        
+        # first .parent is the folder of the config, second .parent is the folder containing the 'models' folder
+        cd_to = path_to_config.parent.parent.resolve()
+        
+        print(f"Changing working directory to {cd_to}")
+        os.chdir(cd_to)
+        
+        pipeline = Pipeline.from_pretrained(path_to_config)
+        
+        print(f"Changing working directory back to {cwd}")
+        os.chdir(cwd)
+        
+        return pipeline
+
+    def __call__(
+        self,
+        audio,
+        num_speakers=None,
+        min_speakers=None,
+        max_speakers=None,
+    ):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio_data = {
+            'waveform': torch.from_numpy(audio[None, :]),
+            'sample_rate': SAMPLE_RATE
+        }
+        segments = self.model(audio_data, num_speakers=num_speakers, min_speakers=min_speakers, max_speakers=max_speakers)
+        diarize_df = pd.DataFrame(segments.itertracks(yield_label=True), columns=['segment', 'label', 'speaker'])
+        diarize_df['start'] = diarize_df['segment'].apply(lambda x: x.start)
+        diarize_df['end'] = diarize_df['segment'].apply(lambda x: x.end)
+        return diarize_df
diff --git a/whisperx/transcribe.py b/whisperx/transcribe.py
index a67787c1b..f256a9843 100644
--- a/whisperx/transcribe.py
+++ b/whisperx/transcribe.py
@@ -10,6 +10,11 @@
 from whisperx.asr import load_model
 from whisperx.audio import load_audio
 from whisperx.diarize import DiarizationPipeline, assign_word_speakers
+# Import the offline diarization pipeline
+try:
+    from .offline_diarize import OfflineDiarizationPipeline
+except ImportError:
+    OfflineDiarizationPipeline = None
 from whisperx.types import AlignedTranscriptionResult, TranscriptionResult
 from whisperx.utils import (
     LANGUAGES,
@@ -56,6 +61,9 @@ def cli():
     parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
     parser.add_argument("--min_speakers", default=None, type=int, help="Minimum number of speakers to in audio file")
     parser.add_argument("--max_speakers", default=None, type=int, help="Maximum number of speakers to in audio file")
+    # Add offline diarization params
+    parser.add_argument("--diarize_offline", action="store_true", help="Use offline diarization models instead of downloading from HF")
+    parser.add_argument("--diarize_config", type=str, default="models/pyannote_diarization_config.yaml", help="Path to the diarization config file for offline mode")
 
     parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
     parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
@@ -120,6 +128,8 @@ def cli():
     chunk_size: int = args.pop("chunk_size")
 
     diarize: bool = args.pop("diarize")
+    diarize_offline: bool = args.pop("diarize_offline")
+    diarize_config: str = args.pop("diarize_config")
     min_speakers: int = args.pop("min_speakers")
     max_speakers: int = args.pop("max_speakers")
     print_progress: bool = args.pop("print_progress")
@@ -238,12 +248,24 @@ def cli():
 
     # >> Diarize
     if diarize:
-        if hf_token is None:
-            print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
         tmp_results = results
         print(">>Performing diarization...")
         results = []
-        diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
+        
+        if diarize_offline:
+            if OfflineDiarizationPipeline is None:
+                raise ImportError("offline_diarize.py must be in the same directory as transcribe.py. Please ensure it's properly installed.")
+                
+            print(f"Using offline diarization with config: {diarize_config}")
+            diarize_model = OfflineDiarizationPipeline(
+                config_path=diarize_config,
+                device=device
+            )
+        else:
+            if hf_token is None:
+                print("Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model...")
+            diarize_model = DiarizationPipeline(use_auth_token=hf_token, device=device)
+            
         for result, input_audio_path in tmp_results:
             diarize_segments = diarize_model(input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers)
             result = assign_word_speakers(diarize_segments, result)