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annotate dictation/transcriber.py @ 279:b3b547563ec7

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Add Google connector service and agent wiki Implement the C/Seobeo Google Drive and Gmail connector with encrypted OAuth storage, Zenbu authentication, browser testing, AI tool discovery, chunked HTTP decoding, and Bazel coverage. Consolidate repository guidance into progressive wiki documentation and enforce arena-first allocation for new first-party C code. Co-authored-by: Copilot <[email protected]> Copilot-Session: 84c338fd-0939-4bb3-b7f3-1062eb213e5d
author MrJuneJune <me@mrjunejune.com>
date Mon, 17 Aug 2026 22:22:36 -0700
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1 from __future__ import annotations
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2
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3 import asyncio
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4 from concurrent.futures import ThreadPoolExecutor
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5 from dataclasses import dataclass
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6 from pathlib import Path
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7 from typing import Protocol
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8
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9 import numpy as np
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10
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11
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12 @dataclass(frozen=True)
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13 class Transcript:
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14 text: str
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15 language: str
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16 probability: float
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17
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18
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19 class Transcriber(Protocol):
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20 async def warmup(self) -> None:
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21 ...
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22
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23 async def transcribe(
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24 self,
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25 samples: np.ndarray,
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26 *,
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27 final: bool,
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28 ) -> Transcript:
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29 ...
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30
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31
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32 class FasterWhisperTranscriber:
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33 def __init__(self, model_dir: Path, compute_type: str) -> None:
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34 self._model_dir = model_dir
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35 self._compute_type = compute_type
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36 self._executor = ThreadPoolExecutor(
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37 max_workers=1,
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38 thread_name_prefix="dictation",
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39 )
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40 self._model = None
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41 self._load_lock = asyncio.Lock()
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42
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43 async def warmup(self) -> None:
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44 if self._model is not None:
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45 return
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46 async with self._load_lock:
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47 if self._model is not None:
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48 return
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49 loop = asyncio.get_running_loop()
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50 self._model = await loop.run_in_executor(
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51 self._executor,
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52 self._load,
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53 )
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54
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55 def _load(self):
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56 from faster_whisper import WhisperModel
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57
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58 if not (self._model_dir / "model.bin").is_file():
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59 raise FileNotFoundError(
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60 f"Model not found at {self._model_dir}. "
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61 "Run: bazel run //dictation:download_model"
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62 )
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63 return WhisperModel(
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64 str(self._model_dir),
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65 device="cuda",
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66 compute_type=self._compute_type,
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67 local_files_only=True,
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68 )
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69
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70 async def transcribe(
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71 self,
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72 samples: np.ndarray,
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73 *,
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74 final: bool,
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75 ) -> Transcript:
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76 await self.warmup()
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77 loop = asyncio.get_running_loop()
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78 return await loop.run_in_executor(
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79 self._executor,
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80 self._transcribe_sync,
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81 np.asarray(samples, dtype=np.float32),
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82 final,
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83 )
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84
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85 def _transcribe_sync(
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86 self,
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87 samples: np.ndarray,
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88 final: bool,
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89 ) -> Transcript:
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90 segments, info = self._model.transcribe(
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91 samples,
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92 beam_size=5 if final else 1,
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93 best_of=5 if final else 1,
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94 condition_on_previous_text=False,
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95 vad_filter=False,
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96 )
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97 text = "".join(segment.text for segment in segments).strip()
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98 return Transcript(
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99 text=text,
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100 language=info.language or "",
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101 probability=float(info.language_probability or 0.0),
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102 )
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103
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104 def close(self) -> None:
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105 self._executor.shutdown(wait=False, cancel_futures=True)