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| # Standard library imports | |
| import math | |
| from typing import Annotated, List, Dict | |
| # Related third-party imports | |
| import numpy as np | |
| class SilenceStats: | |
| """ | |
| A class to compute and analyze statistics for silence durations | |
| between speech segments. | |
| This class provides methods to compute common statistical metrics | |
| (mean, median, standard deviation, interquartile range) and thresholds | |
| based on silence durations. | |
| Attributes | |
| ---------- | |
| silence_durations : List[float] | |
| A sorted list of silence durations. | |
| Methods | |
| ------- | |
| from_segments(segments) | |
| Class method to create a SilenceStats instance from speech segments. | |
| median() | |
| Compute the median silence duration. | |
| mean() | |
| Compute the mean silence duration. | |
| std() | |
| Compute the standard deviation of silence durations. | |
| iqr() | |
| Compute the interquartile range (IQR) of silence durations. | |
| threshold_std(factor=0.95) | |
| Compute threshold based on standard deviation. | |
| threshold_median_iqr(factor=1.5) | |
| Compute threshold based on median + IQR. | |
| total_silence_above_threshold(threshold) | |
| Compute total silence above a given threshold. | |
| """ | |
| def __init__(self, silence_durations: Annotated[List[float], "List of silence durations"]): | |
| """ | |
| Initialize the SilenceStats class with a list of silence durations. | |
| Parameters | |
| ---------- | |
| silence_durations : List[float] | |
| List of silence durations (non-negative values). | |
| """ | |
| if not all(isinstance(x, (int, float)) and x >= 0 for x in silence_durations): | |
| raise ValueError("silence_durations must be a list of non-negative numbers.") | |
| self.silence_durations = sorted(silence_durations) | |
| def from_segments(cls, segments: Annotated[List[Dict], "List of speech segments"]) -> "SilenceStats": | |
| """ | |
| Create a SilenceStats instance from a list of speech segments. | |
| Parameters | |
| ---------- | |
| segments : List[Dict] | |
| List of speech segments, where each segment contains 'start_time' | |
| and 'end_time' keys. | |
| Returns | |
| ------- | |
| SilenceStats | |
| A SilenceStats instance with computed silence durations. | |
| Examples | |
| -------- | |
| >>> segment = [{"start_time": 0, "end_time": 5}, {"start_time": 10, "end_time": 15}] | |
| >>> stat = SilenceStats.from_segments(segments) | |
| >>> stat.silence_durations | |
| [5] | |
| """ | |
| segments_sorted = sorted(segments, key=lambda x: x['start_time']) | |
| durations = [ | |
| segments_sorted[i + 1]['start_time'] - segments_sorted[i]['end_time'] | |
| for i in range(len(segments_sorted) - 1) | |
| if (segments_sorted[i + 1]['start_time'] - segments_sorted[i]['end_time']) > 0 | |
| ] | |
| return cls(durations) | |
| def median(self) -> Annotated[float, "Median of silence durations"]: | |
| """ | |
| Compute the median silence duration. | |
| Returns | |
| ------- | |
| float | |
| The median of the silence durations. | |
| """ | |
| n = len(self.silence_durations) | |
| if n == 0: | |
| return 0.0 | |
| mid = n // 2 | |
| if n % 2 == 0: | |
| return (self.silence_durations[mid - 1] + self.silence_durations[mid]) / 2 | |
| return self.silence_durations[mid] | |
| def mean(self) -> Annotated[float, "Mean of silence durations"]: | |
| """ | |
| Compute the mean silence duration. | |
| Returns | |
| ------- | |
| float | |
| The mean of the silence durations. | |
| """ | |
| return sum(self.silence_durations) / len(self.silence_durations) if self.silence_durations else 0.0 | |
| def std(self) -> Annotated[float, "Standard deviation of silence durations"]: | |
| """ | |
| Compute the standard deviation of silence durations. | |
| Returns | |
| ------- | |
| float | |
| The standard deviation of the silence durations. | |
| """ | |
| n = len(self.silence_durations) | |
| if n == 0: | |
| return 0.0 | |
| mu = self.mean() | |
| var = sum((x - mu) ** 2 for x in self.silence_durations) / n | |
| return math.sqrt(var) | |
| def iqr(self) -> Annotated[float, "Interquartile range (IQR) of silence durations"]: | |
| """ | |
| Compute the Interquartile Range (IQR). | |
| Returns | |
| ------- | |
| float | |
| The IQR of the silence durations. | |
| """ | |
| if not self.silence_durations: | |
| return 0.0 | |
| q1 = np.percentile(self.silence_durations, 25) | |
| q3 = np.percentile(self.silence_durations, 75) | |
| return q3 - q1 | |
| def threshold_std(self, factor: Annotated[float, "Scaling factor for std threshold"] = 0.95) -> float: | |
| """ | |
| Compute the threshold based on standard deviation. | |
| Parameters | |
| ---------- | |
| factor : float, optional | |
| A scaling factor for the standard deviation, by default 0.95. | |
| Returns | |
| ------- | |
| float | |
| Threshold based on standard deviation. | |
| """ | |
| return self.std() * factor | |
| def threshold_median_iqr(self, factor: Annotated[float, "Scaling factor for IQR"] = 1.5) -> float: | |
| """ | |
| Compute the threshold based on median and IQR. | |
| Parameters | |
| ---------- | |
| factor : float, optional | |
| A scaling factor for the IQR, by default 1.5. | |
| Returns | |
| ------- | |
| float | |
| Threshold based on median and IQR. | |
| """ | |
| return self.median() + (self.iqr() * factor) | |
| def total_silence_above_threshold( | |
| self, threshold: Annotated[float, "Threshold value for silence"] | |
| ) -> Annotated[float, "Total silence above the threshold"]: | |
| """ | |
| Compute the total silence above the given threshold. | |
| Parameters | |
| ---------- | |
| threshold : float | |
| The threshold value to compare silence durations. | |
| Returns | |
| ------- | |
| float | |
| Total silence duration above the threshold. | |
| """ | |
| return sum(s for s in self.silence_durations if s >= threshold) | |
| if __name__ == "__main__": | |
| final_ssm = { | |
| 'ssm': [ | |
| {'speaker': 'Customer', 'start_time': 8500, 'end_time': 9760, 'text': 'Hey, G-Chance, this is Jennifer. ', | |
| 'index': 0, 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 10660, 'end_time': 11560, 'text': 'Yes, hi, Jennifer. ', 'index': 1, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 11620, 'end_time': 12380, 'text': "Good afternoon, ma'am. ", 'index': 2, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 83880, 'end_time': 85460, 'text': 'Okay. ', 'index': 24, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 85500, 'end_time': 85620, 'text': 'Yeah. ', 'index': 25, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 86400, 'end_time': 90320, | |
| 'text': "So I'll be sending this shipping documents right after this call. ", 'index': 26, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'CSR', 'start_time': 90400, 'end_time': 91160, 'text': 'Thank you so much. ', 'index': 27, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'Customer', 'start_time': 92060, 'end_time': 92680, 'text': 'Okay, thank you. ', 'index': 28, | |
| 'sentiment': 'Neutral', 'profane': False}, | |
| {'speaker': 'Customer', 'start_time': 93880, 'end_time': 98220, 'text': 'All right, bye-bye. ', 'index': 29, | |
| 'sentiment': 'Neutral', 'profane': False} | |
| ], | |
| 'summary': 'Gabby from Transplace AP Team called Jennifer to request copies of a carrier invoice, bill of ' | |
| 'lading, and proof of delivery, and Jennifer provided her email for Gabby to send the shipping ' | |
| 'documents.', | |
| 'conflict': False, | |
| 'topic': 'Invoice and Shipping Documents Request' | |
| } | |
| stats = SilenceStats.from_segments(final_ssm['ssm']) | |
| print("Mean:", stats.mean()) | |
| print("Median:", stats.median()) | |
| print("Std Dev:", stats.std()) | |
| print("IQR:", stats.iqr()) | |
| t_std = stats.threshold_std(factor=0.97) | |
| t_median_iqr = stats.threshold_median_iqr(factor=1.49) | |
| print("Threshold (std-based):", t_std) | |
| print("Threshold (median+IQR):", t_median_iqr) | |
| print("Total silence (std-based):", stats.total_silence_above_threshold(t_std)) | |
| print("Total silence (median+IQR-based):", stats.total_silence_above_threshold(t_median_iqr)) | |
| final_ssm["silence"] = t_std | |
| print(final_ssm) | |