diff --git a/speech_recognition/whisper/run.py b/speech_recognition/whisper/run.py
index fca19ccb..afd34abc 100644
--- a/speech_recognition/whisper/run.py
+++ b/speech_recognition/whisper/run.py
@@ -18,7 +18,7 @@
     sys.exit(1)
 
 
-def run_pytorch_fp32(model_name, num_runs, timeout, **kwargs):
+def run_pytorch(model_name, num_runs, timeout, use_torch_fp16=False):
     import os
     import sys
     import torch
@@ -32,6 +32,10 @@ def run_pytorch_fp32(model_name, num_runs, timeout, **kwargs):
     from speech_recognition.whisper.whisper.whisper.transcribe import transcribe
     model = load_model(model_name)
     model.eval()
+    if use_torch_fp16:
+        model = model.half()
+        model._encoder.half()
+        model._decoder.half()
 
     def single_pass_pytorch(_runner, _librispeech):
         array = _librispeech.get_input_array()
@@ -40,8 +44,10 @@ def single_pass_pytorch(_runner, _librispeech):
             _runner.run(batch_size * array.shape[0], audio)["text"].lstrip().replace(".", "").upper()
         )
 
+    decode_options = {"fp16": use_torch_fp16}
+
     def transcribe_wrapper(audio):
-        return transcribe(model, audio, no_speech_threshold=1.0, verbose=None)
+        return transcribe(model, audio, no_speech_threshold=1.0, verbose=None, **decode_options)
 
     runner = PyTorchRunnerV2(transcribe_wrapper, throughput_only=True)
     librispeech = LibriSpeech()
@@ -50,6 +56,14 @@ def transcribe_wrapper(audio):
     return run_model(single_pass_pytorch, runner, librispeech, batch_size, num_runs, timeout)
 
 
+def run_pytorch_fp32(model_name, num_runs, timeout):
+    return run_pytorch(model_name, num_runs, timeout, use_torch_fp16=False)
+
+
+def run_pytorch_fp16(model_name, num_runs, timeout):
+    return run_pytorch(model_name, num_runs, timeout, use_torch_fp16=True)
+
+
 def run_pytorch_cuda(model_name, num_runs, timeout, **kwargs):
     import os
     import sys
@@ -89,8 +103,12 @@ def transcribe_wrapper(audio):
     whisper_variants = whisper_variants + [f"{name}.en" for name in whisper_variants[:4]]
     parser = DefaultArgParser(["pytorch"])
     parser.require_model_name(whisper_variants)
+    parser.add_argument("-p", "--precision", type=str, choices=["fp32", "fp16"], required=True)
 
+    args = vars(parser.parse())
     if torch.cuda.is_available():
-        run_pytorch_cuda(**vars(parser.parse()))
-    else:
-        run_pytorch_fp32(**vars(parser.parse()))
+        run_pytorch_cuda(**args)
+    elif args["precision"] == "fp32":
+        run_pytorch_fp32(**args)
+    elif args["precision"] == "fp16":
+        run_pytorch_fp16(**args)