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diff --git a/python/pyarmnn/examples/speech_recognition/run_audio_file.py b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
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+++ b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
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+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""Automatic speech recognition with PyArmNN demo for processing audio clips to text."""
+
+import sys
+import os
+from argparse import ArgumentParser
+
+script_dir = os.path.dirname(__file__)
+sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+
+from network_executor import ArmnnNetworkExecutor
+from utils import dict_labels
+from preprocess import MFCCParams, Preprocessor, MFCC
+from audio_capture import AudioCapture, ModelParams
+from audio_utils import decode_text, prepare_input_tensors, display_text
+
+
+def parse_args():
+    parser = ArgumentParser(description="ASR with PyArmNN")
+    parser.add_argument(
+        "--audio_file_path",
+        required=True,
+        type=str,
+        help="Path to the audio file to perform ASR",
+    )
+    parser.add_argument(
+        "--model_file_path",
+        required=True,
+        type=str,
+        help="Path to ASR model to use",
+    )
+    parser.add_argument(
+        "--labels_file_path",
+        required=True,
+        type=str,
+        help="Path to text file containing labels to map to model output",
+    )
+    parser.add_argument(
+        "--preferred_backends",
+        type=str,
+        nargs="+",
+        default=["CpuAcc", "CpuRef"],
+        help="""List of backends in order of preference for optimizing
+        subgraphs, falling back to the next backend in the list on unsupported
+        layers. Defaults to [CpuAcc, CpuRef]""",
+    )
+    return parser.parse_args()
+
+
+def main(args):
+    # Read command line args
+    audio_file = args.audio_file_path
+    model = ModelParams(args.model_file_path)
+    labels = dict_labels(args.labels_file_path)
+
+    # Create the ArmNN inference runner
+    network = ArmnnNetworkExecutor(model.path, args.preferred_backends)
+
+    audio_capture = AudioCapture(model)
+    buffer = audio_capture.from_audio_file(audio_file)
+
+    # Create the preprocessor
+    mfcc_params = MFCCParams(sampling_freq=16000, num_fbank_bins=128, mel_lo_freq=0, mel_hi_freq=8000,
+                                        num_mfcc_feats=13, frame_len=512, use_htk_method=False, n_FFT=512)
+    mfcc = MFCC(mfcc_params)
+    preprocessor = Preprocessor(mfcc, model_input_size=1044, stride=160)
+
+    text = ""
+    current_r_context = ""
+    is_first_window = True
+
+    print("Processing Audio Frames...")
+    for audio_data in buffer:
+        # Prepare the input Tensors
+        input_tensors = prepare_input_tensors(audio_data, network.input_binding_info, preprocessor)
+
+        # Run inference
+        output_result = network.run(input_tensors)
+
+        # Slice and Decode the text, and store the right context
+        current_r_context, text = decode_text(is_first_window, labels, output_result)
+
+        is_first_window = False
+
+        display_text(text)
+
+    print(current_r_context, flush=True)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)