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annotate dictation/transcriber.py @ 275:78699f810817

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Add Qwen3-VL and WebRTC dictation services Add Bazel targets for the CUDA-backed Qwen3-VL server and a local WebRTC faster-whisper dictation service. Co-authored-by: Copilot <[email protected]> Copilot-Session: e3d8cb06-6c95-4ae0-9757-651d3796ab00
author MrJuneJune <me@mrjunejune.com>
date Mon, 17 Aug 2026 10:58:47 -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)