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| author | Raviv Shalev <raviv.shalev@arm.com> | 2021-12-07 15:18:09 +0200 |
|---|---|---|
| committer | TeresaARM <teresa.charlinreyes@arm.com> | 2022-04-13 15:33:31 +0000 |
| commit | 97ddc06e52fbcabfd8ede7a00e9494c663186b92 (patch) | |
| tree | 43c84d352c3a67aa45d89760fba6c79b81c8f8dc /python/pyarmnn/examples/keyword_spotting | |
| parent | 2f0ddb67d8f9267ab600a8a26308cab32f9e16ac (diff) | |
| download | armnn-97ddc06e52fbcabfd8ede7a00e9494c663186b92.tar.gz | |
MLECO-2493 Add python OD example with TFLite delegate
Signed-off-by: Raviv Shalev <raviv.shalev@arm.com>
Change-Id: I25fcccbf912be0c5bd4fbfd2e97552341958af35
Diffstat (limited to 'python/pyarmnn/examples/keyword_spotting')
| -rw-r--r-- | python/pyarmnn/examples/keyword_spotting/README.MD | 2 | ||||
| -rw-r--r-- | python/pyarmnn/examples/keyword_spotting/run_audio_classification.py | 11 |
2 files changed, 8 insertions, 5 deletions
diff --git a/python/pyarmnn/examples/keyword_spotting/README.MD b/python/pyarmnn/examples/keyword_spotting/README.MD index d276c08f8e..dde8342e7f 100644 --- a/python/pyarmnn/examples/keyword_spotting/README.MD +++ b/python/pyarmnn/examples/keyword_spotting/README.MD @@ -166,7 +166,7 @@ mfcc_feats = np.dot(self._dct_matrix, log_mel_energy) # audio_utils.py # Quantize the input data and create input tensors with PyArmNN input_tensor = quantize_input(input_tensor, input_binding_info) -input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor]) +input_tensors = ann.make_input_tensors([input_binding_info], [input_data]) ``` Note: `ArmnnNetworkExecutor` has already created the output tensors for you. diff --git a/python/pyarmnn/examples/keyword_spotting/run_audio_classification.py b/python/pyarmnn/examples/keyword_spotting/run_audio_classification.py index 6dfa4cc806..50ad1a8a2e 100644 --- a/python/pyarmnn/examples/keyword_spotting/run_audio_classification.py +++ b/python/pyarmnn/examples/keyword_spotting/run_audio_classification.py @@ -14,7 +14,7 @@ script_dir = os.path.dirname(__file__) sys.path.insert(1, os.path.join(script_dir, '..', 'common')) from network_executor import ArmnnNetworkExecutor -from utils import prepare_input_tensors, dequantize_output +from utils import prepare_input_data, dequantize_output from mfcc import AudioPreprocessor, MFCC, MFCCParams from audio_utils import decode, display_text from audio_capture import AudioCaptureParams, CaptureAudioStream, capture_audio @@ -69,13 +69,16 @@ def parse_args(): def recognise_speech(audio_data, network, preprocessor, threshold): # 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]) dequantized_result = [] for index, ofm in enumerate(output_result): - dequantized_result.append(dequantize_output(ofm, network.output_binding_info[index])) + dequantized_result.append(dequantize_output(ofm, network.is_output_quantized(index), + network.get_output_quantization_scale(index), + network.get_output_quantization_offset(index))) # Decode the text and display result if above threshold decoded_result = decode(dequantized_result, labels) |