MLECO-2079 Adding the python KWS example
Signed-off-by: Eanna O Cathain <eanna.ocathain@arm.com>
Change-Id: Ie1463aaeb5e3cade22df8f560ae99a8e1c4a9c17
diff --git a/python/pyarmnn/examples/speech_recognition/audio_utils.py b/python/pyarmnn/examples/speech_recognition/audio_utils.py
index f03d2e1..1ac78e8 100644
--- a/python/pyarmnn/examples/speech_recognition/audio_utils.py
+++ b/python/pyarmnn/examples/speech_recognition/audio_utils.py
@@ -1,10 +1,9 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
"""Utilities for speech recognition apps."""
import numpy as np
-import pyarmnn as ann
def decode(model_output: np.ndarray, labels: dict) -> str:
@@ -50,33 +49,6 @@
print(text, sep="", end="", flush=True)
-def quantize_input(data, input_binding_info):
- """Quantize the float input to (u)int8 ready for inputting to model."""
- if data.ndim != 2:
- raise RuntimeError("Audio data must have 2 dimensions for quantization")
-
- quant_scale = input_binding_info[1].GetQuantizationScale()
- quant_offset = input_binding_info[1].GetQuantizationOffset()
- data_type = input_binding_info[1].GetDataType()
-
- if data_type == ann.DataType_QAsymmS8:
- data_type = np.int8
- elif data_type == ann.DataType_QAsymmU8:
- data_type = np.uint8
- else:
- raise ValueError("Could not quantize data to required data type")
-
- d_min = np.iinfo(data_type).min
- d_max = np.iinfo(data_type).max
-
- for row in range(data.shape[0]):
- for col in range(data.shape[1]):
- data[row, col] = (data[row, col] / quant_scale) + quant_offset
- data[row, col] = np.clip(data[row, col], d_min, d_max)
- data = data.astype(data_type)
- return data
-
-
def decode_text(is_first_window, labels, output_result):
"""
Slices the text appropriately depending on the window, and decodes for wav2letter output.
@@ -88,7 +60,6 @@
is_first_window: Boolean to show if it is the first window we are running inference on
labels: the label set
output_result: the output from the inference
- text: the current text string, to be displayed at the end
Returns:
current_r_context: the current right context
text: the current text string, with the latest output decoded and appended
@@ -109,25 +80,3 @@
# Store the right context, we will need it after the last inference
current_r_context = decode(output_result[0][0][0][right_context_start:], labels)
return current_r_context, text
-
-
-def prepare_input_tensors(audio_data, input_binding_info, mfcc_preprocessor):
- """
- Takes a block of audio data, extracts the MFCC features, quantizes the array, and uses ArmNN to create the
- input tensors.
-
- Args:
- audio_data: The audio data to process
- mfcc_instance: the mfcc class instance
- input_binding_info: the model input binding info
- mfcc_preprocessor: the mfcc preprocessor instance
- Returns:
- input_tensors: the prepared input tensors, ready to be consumed by the ArmNN NetworkExecutor
- """
-
- data_type = input_binding_info[1].GetDataType()
- input_tensor = mfcc_preprocessor.extract_features(audio_data)
- if data_type != ann.DataType_Float32:
- input_tensor = quantize_input(input_tensor, input_binding_info)
- input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor])
- return input_tensors