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# Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
"""Utilities for speech recognition apps."""
import numpy as np
def decode(model_output: np.ndarray, labels: dict) -> str:
"""Decodes the integer encoded results from inference into a string.
Args:
model_output: Results from running inference.
labels: Dictionary of labels keyed on the classification index.
Returns:
Decoded string.
"""
top1_results = [labels[np.argmax(row)] for row in model_output]
return filter_characters(top1_results)
def filter_characters(results: list) -> str:
"""Filters unwanted and duplicate characters.
Args:
results: List of top 1 results from inference.
Returns:
Final output string to present to user.
"""
text = ""
for i in range(len(results)):
if results[i] == "$":
continue
elif i + 1 < len(results) and results[i] == results[i + 1]:
continue
else:
text += results[i]
return text
def display_text(text: str):
"""Presents the results on the console.
Args:
text: Results of performing ASR on the input audio data.
"""
print(text, sep="", end="", flush=True)
def decode_text(is_first_window, labels, output_result):
"""
Slices the text appropriately depending on the window, and decodes for wav2letter output.
* First run, take the left context, and inner context.
* Every other run, take the inner context.
Stores the current right context, and updates it for each inference. Will get used after last inference.
Args:
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
Returns:
current_r_context: the current right context
text: the current text string, with the latest output decoded and appended
"""
# For wav2letter with 148 output steps:
# Left context is index 0-48, inner context 49-99, right context 100-147
inner_context_start = 49
inner_context_end = 99
right_context_start = 100
if is_first_window:
# Since it's the first inference, keep the left context, and inner context, and decode
text = decode(output_result[0][0][0][0:inner_context_end], labels)
else:
# Only decode the inner context
text = decode(output_result[0][0][0][inner_context_start:inner_context_end], labels)
# 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
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