diff --git a/README.md b/README.md
index cba6e1f..031ccf5 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,17 @@ All the supported options with their help are shown.
 
 On top of the OpenAI Whisper command line options, there are some specific options provided by CTranslate2 or whiper-ctranslate2.
 
+## Batched inference
+
+Batched inference transcribes each segment in-dependently which can provide an additional 2x-4x speed increase:
+
+    whisper-ctranslate2 inaguracio2011.mp3 --batched True
+    
+You can additionally use the --batch_size to specify the maximum number of parallel requests to model for decoding.
+
+Batched inference uses Voice Activity Detection (VAD) filter and ignores the following paramters: compression_ratio_threshold, logprob_threshold,
+no_speech_threshold, condition_on_previous_text, prompt_reset_on_temperature, prefix, hallucination_silence_threshold.
+
 ## Quantization
 
 `--compute_type` option which accepts _default,auto,int8,int8_float16,int16,float16,float32_ values indicates the type of [quantization](https://opennmt.net/CTranslate2/quantization.html) to use. On CPU _int8_ will give the best performance:
@@ -115,14 +126,14 @@ https://user-images.githubusercontent.com/309265/231533784-e58c4b92-e9fb-4256-b4
 
 ## Diarization (speaker identification)
 
-There is experimental diarization support using [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) to identify speakers. At the moment, the support is a segment level.
+There is experimental diarization support using [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) to identify speakers. At the moment, the support is at segment level.
 
 To enable diarization you need to follow these steps:
 
 1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) with `pip install pyannote.audio`
 2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions
 3. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions
-4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).
+4. Create an access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).
 
 And then execute passing the HuggingFace API token as parameter to enable diarization:
 
diff --git a/src/whisper_ctranslate2/commandline.py b/src/whisper_ctranslate2/commandline.py
index 6fa6efc..10f10e4 100644
--- a/src/whisper_ctranslate2/commandline.py
+++ b/src/whisper_ctranslate2/commandline.py
@@ -354,7 +354,14 @@ def read_command_line():
             "--batched",
             type=CommandLine._str2bool,
             default=False,
-            help="Uses Batched transcription which can provide an additional 2x-3x speed increase",
+            help="Uses Batched transcription which can provide an additional 2x-4x speed increase",
+        )
+
+        algorithm_args.add_argument(
+            "--batch_size",
+            type=CommandLine._optional_int,
+            default=None,
+            help="When using Batched transcription the maximum number of parallel requests to model for decoding.",
         )
 
         vad_args = parser.add_argument_group("VAD filter arguments")
diff --git a/src/whisper_ctranslate2/transcribe.py b/src/whisper_ctranslate2/transcribe.py
index 781f3a8..b8a4c24 100644
--- a/src/whisper_ctranslate2/transcribe.py
+++ b/src/whisper_ctranslate2/transcribe.py
@@ -111,6 +111,7 @@ def __init__(
         cache_directory: str,
         local_files_only: bool,
         batched: bool,
+        batch_size: int = None,
     ):
         self.model = WhisperModel(
             model_path,
@@ -121,6 +122,8 @@ def __init__(
             download_root=cache_directory,
             local_files_only=local_files_only,
         )
+
+        self.batch_size = batch_size
         if batched:
             self.batched_model = BatchedInferencePipeline(model=self.model)
         else:
@@ -144,6 +147,10 @@ def inference(
             model = self.model
             vad = options.vad_filter
 
+        batch_size = (
+            {"batch_size": self.batch_size} if self.batch_size is not None else {}
+        )
+
         segments, info = model.transcribe(
             audio=audio,
             language=language,
@@ -171,6 +178,7 @@ def inference(
             hallucination_silence_threshold=options.hallucination_silence_threshold,
             vad_filter=vad,
             vad_parameters=vad_parameters,
+            **batch_size,
         )
 
         language_name = LANGUAGES[info.language].title()
diff --git a/src/whisper_ctranslate2/version.py b/src/whisper_ctranslate2/version.py
index 574c066..3d18726 100644
--- a/src/whisper_ctranslate2/version.py
+++ b/src/whisper_ctranslate2/version.py
@@ -1 +1 @@
-__version__ = "0.4.9"
+__version__ = "0.5.0"
diff --git a/src/whisper_ctranslate2/whisper_ctranslate2.py b/src/whisper_ctranslate2/whisper_ctranslate2.py
index 0567d50..9cc2473 100644
--- a/src/whisper_ctranslate2/whisper_ctranslate2.py
+++ b/src/whisper_ctranslate2/whisper_ctranslate2.py
@@ -118,6 +118,7 @@ def main():
     hf_token = args.pop("hf_token")
     speaker_name = args.pop("speaker_name")
     batched = args.pop("batched")
+    batch_size = args.pop("batch_size")
 
     language = get_language(language, model_directory, model)
     options = get_transcription_options(args)
@@ -147,6 +148,10 @@ def main():
         )
         return
 
+    if batch_size and not batched:
+        sys.stderr.write("--batched_size can only be used if-- batched is True")
+        return
+
     if args["max_line_count"] and not args["max_line_width"]:
         warnings.warn("--max_line_count has no effect without --max_line_width")
 
@@ -218,6 +223,7 @@ def main():
         cache_directory,
         local_files_only,
         batched,
+        batch_size,
     )
 
     diarization = len(hf_token) > 0