MLECO-1253 Adding ASR sample application using the PyArmNN api
Change-Id: I450b23800ca316a5bfd4608c8559cf4f11271c21
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
diff --git a/python/pyarmnn/examples/speech_recognition/run_audio_file.py b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
new file mode 100644
index 0000000..c7e4c6b
--- /dev/null
+++ b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
@@ -0,0 +1,94 @@
+# 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)