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| import numpy as np | |
| def IoU(diarized_segments: np.ndarray, asr_segments: np.ndarray) -> np.ndarray: | |
| """ | |
| Calculates the Intersection over Union (IoU) between diarized_segments and asr_segments. | |
| Args: | |
| ----------- | |
| - diarized_segments (np.ndarray): An array representing N segments with shape (M, 2), where each row | |
| contains the start and end times of a diarized segment. | |
| - asr_segments (np.ndarray): An array representing M segments with shape (N, 2), where each row contains | |
| the start and end times of an asr segment. | |
| Returns: | |
| -------- | |
| - np.ndarray: A 2D array of shape (N, M) representing the IoU between each pair of diarized and. | |
| The value at position (i, j) in the array corresponds to the IoU between the asr segment i and the diarized segment j. | |
| Values are in the range [0, 1], where 0 indicates no intersection and 1 indicates perfect overlap. | |
| Note: | |
| - The IoU is calculated as the ratio of the intersection over the union of the time intervals. | |
| - Segments with no overlap result in an IoU value of 0. | |
| - Segments with overlap but no intersection (e.g., one segment completely contained within another) can | |
| have an IoU greater than 0. | |
| Example: | |
| ```python | |
| diarized_segments = np.array([[0, 5], [3, 8], [6, 10]]) | |
| asr_segments = np.array([[2, 6], [1, 4]]) | |
| IoU_values = IoU(diarized_segments, asr_segments) | |
| print(IoU_values) | |
| # Output | |
| # [[0.5 0.5 0.] | |
| # [0.6 0.14285714 0.]] | |
| ``` | |
| """ | |
| # We measure intersection between each of the N asr_segments [Nx2] and each M of diarize_ segments [Mx2] | |
| # The result is a NxM matrix. intersection <= 0 mean no intersection. | |
| starts = np.maximum(asr_segments[:, 0, np.newaxis], diarized_segments[:, 0]) | |
| ends = np.minimum(asr_segments[:, 1, np.newaxis], diarized_segments[:, 1]) | |
| intersections = np.maximum(ends - starts, 0) | |
| # Union for segments without overlap will lead to invalid results but it does not matters | |
| # as we opt them out eventually. | |
| union = np.maximum(asr_segments[:, 1, np.newaxis], diarized_segments[:, 1]) - np.minimum( | |
| asr_segments[:, 0, np.newaxis], diarized_segments[:, 0] | |
| ) | |
| # Negative results are zeroed as they are invalid. | |
| intersection_over_union = np.maximum(intersections / union, 0) | |
| return intersection_over_union | |
| def match_segments( | |
| diarized_segments: np.ndarray, | |
| diarized_labels: list[str], | |
| asr_segments: np.ndarray, | |
| threshold: float = 0.0, | |
| no_match_label: str = "NO_SPEAKER", | |
| ) -> np.ndarray: | |
| """ | |
| Perform segment matching between diarized segments and ASR (Automatic Speech Recognition) segments. | |
| Args: | |
| ----- | |
| - diarized_segments (np.ndarray): Array representing diarized speaker segments. | |
| - diarized_labels (list[str]): List of labels corresponding to diarized_segments. | |
| - asr_segments (np.ndarray): Array representing ASR speaker segments. | |
| - threshold (float, optional): IoU (Intersection over Union) threshold for matching. Default is 0.0. | |
| - no_match_label (str, optional): Label assigned when no matching segment is found. Default is "NO_SPEAKER". | |
| Returns: | |
| -------- | |
| - np.ndarray: Array of labels corresponding to the best-matched ASR segments for each diarized segment. | |
| Notes: | |
| - The function calculates IoU between diarized segments and ASR segments and considers only segments with IoU above the threshold. | |
| - If no matching segment is found, the specified `no_match_label` is assigned. | |
| - The returned array represents the labels of the best-matched ASR segments for each diarized segment. | |
| """ | |
| iou_results = IoU(diarized_segments, asr_segments) | |
| # Zero out iou below threshold. | |
| iou_results[iou_results <= threshold] = 0.0 | |
| # We create a no match label which value will be threshold | |
| diarized_labels = [no_match_label] + diarized_labels | |
| # If there is nothing above threshold, no_match_label will be assigned. | |
| iou_results = np.hstack([threshold * np.ones((iou_results.shape[0], 1)), iou_results]) | |
| # Will find argument with highest iou (if all zeroes, will assign first (no_match_label)). | |
| best_match_idx = np.argmax(iou_results, axis=1) | |
| assigned_labels = np.take(diarized_labels, best_match_idx) | |
| return assigned_labels |