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annotate dictation/audio.py @ 280:49e9e591c9bb

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Add persistent dictation, prewarmed WebRTC speech input, Copilot SDK routing, animated conversation lifecycle controls, parking, and architecture coverage.
author MrJuneJune <me@mrjunejune.com>
date Tue, 18 Aug 2026 19:14:53 -0700
parents 78699f810817
children
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1 from __future__ import annotations
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2
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3 from dataclasses import dataclass
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4 from enum import Enum
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5
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6 import numpy as np
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7
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8
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9 class AudioEventKind(str, Enum):
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10 SPEECH_STARTED = "speech_started"
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11 PARTIAL_READY = "partial_ready"
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12 FINAL_READY = "final_ready"
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13
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14
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15 @dataclass(frozen=True)
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16 class AudioEvent:
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17 kind: AudioEventKind
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18 samples: np.ndarray | None = None
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19
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20
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21 class AudioSegmenter:
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22 def __init__(
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23 self,
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24 *,
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25 sample_rate: int = 16000,
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26 speech_threshold: float = 0.012,
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27 silence_ms: int = 400,
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28 partial_interval_ms: int = 500,
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29 max_utterance_seconds: int = 30,
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30 pre_roll_ms: int = 200,
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31 ) -> None:
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32 self.sample_rate = sample_rate
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33 self.speech_threshold = speech_threshold
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34 self.silence_samples = sample_rate * silence_ms // 1000
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35 self.partial_samples = sample_rate * partial_interval_ms // 1000
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36 self.max_samples = sample_rate * max_utterance_seconds
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37 self.pre_roll_samples = sample_rate * pre_roll_ms // 1000
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38 self._pre_roll = np.empty(0, dtype=np.float32)
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39 self._utterance = np.empty(0, dtype=np.float32)
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40 self._silence_count = 0
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41 self._last_partial_size = 0
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42 self._speaking = False
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43
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44 @property
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45 def speaking(self) -> bool:
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46 return self._speaking
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47
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48 def feed(self, samples: np.ndarray) -> list[AudioEvent]:
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49 chunk = np.asarray(samples, dtype=np.float32).reshape(-1)
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50 if chunk.size == 0:
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51 return []
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52 rms = float(np.sqrt(np.mean(np.square(chunk), dtype=np.float64)))
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53 is_speech = rms >= self.speech_threshold
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54 events: list[AudioEvent] = []
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55
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56 if not self._speaking:
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57 if not is_speech:
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58 self._append_pre_roll(chunk)
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59 return events
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60 self._speaking = True
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61 self._utterance = np.concatenate((self._pre_roll, chunk))
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62 self._pre_roll = np.empty(0, dtype=np.float32)
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63 self._silence_count = 0
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64 self._last_partial_size = 0
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65 events.append(AudioEvent(AudioEventKind.SPEECH_STARTED))
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66 else:
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67 self._utterance = np.concatenate((self._utterance, chunk))
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68
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69 self._silence_count = 0 if is_speech else self._silence_count + chunk.size
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70 utterance_size = self._utterance.size
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71
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72 if (
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73 utterance_size - self._last_partial_size >= self.partial_samples
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74 and self._silence_count < self.silence_samples
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75 ):
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76 self._last_partial_size = utterance_size
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77 events.append(
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78 AudioEvent(
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79 AudioEventKind.PARTIAL_READY,
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80 self._utterance.copy(),
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81 )
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82 )
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83
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84 if (
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85 self._silence_count >= self.silence_samples
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86 or utterance_size >= self.max_samples
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87 ):
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88 final = self._utterance.copy()
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89 self._reset()
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90 events.append(AudioEvent(AudioEventKind.FINAL_READY, final))
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91
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92 return events
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93
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94 def flush(self) -> AudioEvent | None:
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95 if not self._speaking or self._utterance.size == 0:
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96 self._reset()
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97 return None
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98 final = self._utterance.copy()
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99 self._reset()
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100 return AudioEvent(AudioEventKind.FINAL_READY, final)
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101
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102 def _append_pre_roll(self, chunk: np.ndarray) -> None:
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103 self._pre_roll = np.concatenate((self._pre_roll, chunk))
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104 if self._pre_roll.size > self.pre_roll_samples:
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105 self._pre_roll = self._pre_roll[-self.pre_roll_samples :]
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106
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107 def _reset(self) -> None:
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108 self._pre_roll = np.empty(0, dtype=np.float32)
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109 self._utterance = np.empty(0, dtype=np.float32)
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110 self._silence_count = 0
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111 self._last_partial_size = 0
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112 self._speaking = False