MLECO-2493 Add python OD example with TFLite delegate
Signed-off-by: Raviv Shalev <raviv.shalev@arm.com>
Change-Id: I25fcccbf912be0c5bd4fbfd2e97552341958af35
diff --git a/python/pyarmnn/examples/speech_recognition/run_audio_file.py b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
index 0430f68..ddf6cb7 100644
--- a/python/pyarmnn/examples/speech_recognition/run_audio_file.py
+++ b/python/pyarmnn/examples/speech_recognition/run_audio_file.py
@@ -12,7 +12,7 @@
from argparse import ArgumentParser
from network_executor import ArmnnNetworkExecutor
-from utils import prepare_input_tensors
+from utils import prepare_input_data
from audio_capture import AudioCaptureParams, capture_audio
from audio_utils import decode_text, display_text
from wav2letter_mfcc import Wav2LetterMFCC, W2LAudioPreprocessor
@@ -78,10 +78,11 @@
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)
+ input_data = prepare_input_data(audio_data, network.get_data_type(), network.get_input_quantization_scale(0),
+ network.get_input_quantization_offset(0), preprocessor)
# Run inference
- output_result = network.run(input_tensors)
+ output_result = network.run([input_data])
# Slice and Decode the text, and store the right context
current_r_context, text = decode_text(is_first_window, labels, output_result)