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