ドキュメントを更新し、型注釈を追加してコードの可読性とメンテナンス性を向上。各モジュールの使用例や依存関係を明示化し、エラーハンドリングを改善。

src-python/docs/modules/transcription.md

@@ -1,3 +1,78 @@
+## 文字起こしモジュール (models.transcription)
+
+このドキュメントでは `models/transcription` に関する設計・セットアップ・使用例・テスト方針・トラブルシュートをまとめます。
+
+### 概要
+- `models/transcription` は音声入力をテキストに変換する機能を提供します。主に:
+  - `transcription_recorder.py` — マイクやスピーカからの音声取得ラッパー
+  - `transcription_transcriber.py` — 音声バッファを認識エンジンに渡して文字起こしを行うロジック
+  - `transcription_whisper.py` — faster-whisper（WhisperModel）周りのダウンロード/ロード補助
+  - `transcription_languages.py` — 各言語・国別のエンジン別コードマップ
+
+### 最近の変更点
+- 各モジュールに型注釈と docstring を追加しました。これによりメンテナンス性が向上します。
+- `transcription_whisper.py` にダウンロード進捗コールバックを明記した実装を追加しました。
+
+### 依存関係
+主要な依存:
+- `speech_recognition` — オーディオ録音と Google 音声認識のラッパー
+- `pyaudiowpatch` — クロスプラットフォームのオーディオ設定
+- `pydub` — 音声のチャンネル変換や処理
+- `faster_whisper`（オプショナル）— ローカルで Whisper を使う場合
+- `huggingface_hub`（オプショナル）— モデルアーティファクトのダウンロード
+
+注意: `pydub` は `ffmpeg` が必要です。環境に ffmpeg が無いとワーニングが出ます。
+
+推奨インストール（任意）:
+
+```powershell
+pip install speechrecognition pyaudiowpatch pydub faster-whisper huggingface-hub
+```
+
+テストでは多くの外部依存をモックするため、全てをインストールする必要はありません。
+
+### 初回セットアップ
+1. 必要に応じて `ffmpeg` をインストールしてください（pydub の動作に必要）。
+2. Whisper ローカルモデルを使う場合、`transcription_whisper.downloadWhisperWeight(root, weight_type, callback, end_callback)` を呼んでモデルを取得します。
+   - `callback(progress: float)` は 0.0〜1.0 の進捗通知です。
+   - 例:
+
+```python
+from models.transcription import transcription_whisper as tw
+tw.downloadWhisperWeight("./", "tiny", callback=lambda p: print(f"{p*100:.1f}%"), end_callback=lambda: print("done"))
+```
+
+### API 使用例
+簡単な `AudioTranscriber` の使い方:
+
+```python
+from models.transcription.transcription_transcriber import AudioTranscriber
+
+# source はライブラリが提供するオーディオソースオブジェクト
+tr = AudioTranscriber(speaker=False, source=source, phrase_timeout=3, max_phrases=10, transcription_engine="Google")
+# audio_queue は録音スレッドがプッシュするキュー
+tr.transcribeAudioQueue(audio_queue, languages=["English"], countries=["United States"]) 
+```
+
+戻り値やエラー処理のルールについては各関数の docstring を参照してください。
+
+### テスト方針
+- `AudioTranscriber` と `Whisper` ラッパーはユニットテストでモック化して検証します。
+- 推奨: `pytest` と `unittest.mock` を使い、以下のケースをカバーします:
+  - 正常系: Google/Whisper の成功パス（モックで期待テキストを返す）
+  - エッジ: 無音、低確信、複数言語
+  - フォールバック: Whisper が利用不可の場合のフォールバック動作
+
+### トラブルシュート
+- ffmpeg が見つからない: `pydub` がワーニングを出します。OS に合わせて ffmpeg をインストールしてください。
+- Whisper のロード時に VRAM エラー: `getWhisperModel` は VRAM 不足を検出して `ValueError("VRAM_OUT_OF_MEMORY", message)` を投げます。デバイス設定や compute_type を調整してください。
+- ハッシュ不一致やダウンロード失敗: キャッシュや weights ディレクトリを削除して再ダウンロードしてください。
+
+### 変更履歴
+- 2025-10-09: 型注釈と docstring を追加、ダウンロード/コールバック仕様を明記。
+
+---
+このドキュメントは簡潔な参照用です。さらに詳細な実行手順（ログ収集方法、ffmpeg のインストール手順例など）が必要であれば追記します。
 # transcription — 文字起こしモジュール
 概要: マイク/スピーカー音声の録音と Whisper/Google などのエンジンを使った文字起こしを提供するモジュール群です。主なクラスは録音用の Recorder と `AudioTranscriber` です。
 

src-python/models/transcription/transcription_languages.py

@@ -1,3 +1,8 @@
+"""Language table used by transcription components.
+
+Maps a display language and country to engine-specific language codes.
+"""
+
 transcription_lang = {
     "Afrikaans":{
         "South Africa":{

src-python/models/transcription/transcription_recorder.py

@@ -1,9 +1,18 @@
+"""Recorders that wrap speech_recognition microphone interfaces.
+
+These classes provide small adapters that push raw audio bytes into queues.
+They intentionally keep a thin API so the rest of the system can mock them
+in tests.
+"""
+
+from typing import Any
 from speech_recognition import Recognizer, Microphone
 from pyaudiowpatch import get_sample_size, paInt16
 from datetime import datetime
 
+
 class BaseRecorder:
-    def __init__(self, source, energy_threshold, dynamic_energy_threshold, record_timeout):
+    def __init__(self, source: Any, energy_threshold: int, dynamic_energy_threshold: bool, record_timeout: int) -> None:
         self.recorder = Recognizer()
         self.recorder.energy_threshold = energy_threshold
         self.recorder.dynamic_energy_threshold = dynamic_energy_threshold
@@ -15,18 +24,19 @@ class BaseRecorder:
 
         self.source = source
 
-    def adjustForNoise(self):
+    def adjustForNoise(self) -> None:
         with self.source:
             self.recorder.adjust_for_ambient_noise(self.source)
 
-    def recordIntoQueue(self, audio_queue):
+    def recordIntoQueue(self, audio_queue: Any) -> None:
         def record_callback(_, audio):
             audio_queue.put((audio.get_raw_data(), datetime.now()))
 
         self.stop, self.pause, self.resume = self.recorder.listen_in_background(self.source, record_callback, phrase_time_limit=self.record_timeout)
 
+
 class SelectedMicRecorder(BaseRecorder):
-    def __init__(self, device, energy_threshold, dynamic_energy_threshold, record_timeout):
+    def __init__(self, device: dict, energy_threshold: int, dynamic_energy_threshold: bool, record_timeout: int) -> None:
         source = Microphone(
             device_index=device['index'],
             sample_rate=int(device["defaultSampleRate"]),
@@ -34,8 +44,9 @@ class SelectedMicRecorder(BaseRecorder):
         super().__init__(source=source, energy_threshold=energy_threshold, dynamic_energy_threshold=dynamic_energy_threshold, record_timeout=record_timeout)
         # self.adjustForNoise()
 
+
 class SelectedSpeakerRecorder(BaseRecorder):
-    def __init__(self, device, energy_threshold, dynamic_energy_threshold, record_timeout):
+    def __init__(self, device: dict, energy_threshold: int, dynamic_energy_threshold: bool, record_timeout: int) -> None:
 
         source = Microphone(speaker=True,
             device_index= device["index"],
@@ -47,7 +58,7 @@ class SelectedSpeakerRecorder(BaseRecorder):
         # self.adjustForNoise()
 
 class BaseEnergyRecorder:
-    def __init__(self, source):
+    def __init__(self, source: Any) -> None:
         self.recorder = Recognizer()
         self.recorder.energy_threshold = 0
         self.recorder.dynamic_energy_threshold = False
@@ -59,18 +70,19 @@ class BaseEnergyRecorder:
 
         self.source = source
 
-    def adjustForNoise(self):
+    def adjustForNoise(self) -> None:
         with self.source:
             self.recorder.adjust_for_ambient_noise(self.source)
 
-    def recordIntoQueue(self, energy_queue):
+    def recordIntoQueue(self, energy_queue: Any) -> None:
         def recordCallback(_, energy):
             energy_queue.put(energy)
 
         self.stop, self.pause, self.resume = self.recorder.listen_energy_in_background(self.source, recordCallback)
 
+
 class SelectedMicEnergyRecorder(BaseEnergyRecorder):
-    def __init__(self, device):
+    def __init__(self, device: dict) -> None:
         source = Microphone(
             device_index=device['index'],
             sample_rate=int(device["defaultSampleRate"]),
@@ -78,8 +90,9 @@ class SelectedMicEnergyRecorder(BaseEnergyRecorder):
         super().__init__(source=source)
         # self.adjustForNoise()
 
+
 class SelectedSpeakerEnergyRecorder(BaseEnergyRecorder):
-    def __init__(self, device):
+    def __init__(self, device: dict) -> None:
 
         source = Microphone(speaker=True,
             device_index= device["index"],
@@ -90,7 +103,15 @@ class SelectedSpeakerEnergyRecorder(BaseEnergyRecorder):
         # self.adjustForNoise()
 
 class BaseEnergyAndAudioRecorder:
-    def __init__(self, source, energy_threshold, dynamic_energy_threshold, phrase_time_limit, phrase_timeout, record_timeout):
+    def __init__(
+        self,
+        source: Any,
+        energy_threshold: int,
+        dynamic_energy_threshold: bool,
+        phrase_time_limit: int,
+        phrase_timeout: int,
+        record_timeout: int,
+    ) -> None:
         self.recorder = Recognizer()
         self.recorder.energy_threshold = energy_threshold
         self.recorder.dynamic_energy_threshold = dynamic_energy_threshold
@@ -104,11 +125,11 @@ class BaseEnergyAndAudioRecorder:
 
         self.source = source
 
-    def adjustForNoise(self):
+    def adjustForNoise(self) -> None:
         with self.source:
             self.recorder.adjust_for_ambient_noise(self.source)
 
-    def recordIntoQueue(self, audio_queue, energy_queue=None):
+    def recordIntoQueue(self, audio_queue: Any, energy_queue: Any = None) -> None:
         def audioRecordCallback(_, audio):
             audio_queue.put((audio.get_raw_data(), datetime.now()))
 
@@ -121,10 +142,20 @@ class BaseEnergyAndAudioRecorder:
             phrase_time_limit=self.phrase_time_limit,
             callback_energy=energyRecordCallback if energy_queue is not None else None,
             phrase_timeout=self.phrase_timeout,
-            record_timeout=self.record_timeout)
+            record_timeout=self.record_timeout,
+        )
+
 
 class SelectedMicEnergyAndAudioRecorder(BaseEnergyAndAudioRecorder):
-    def __init__(self, device, energy_threshold, dynamic_energy_threshold, phrase_time_limit, phrase_timeout:int=1, record_timeout:int=5):
+    def __init__(
+        self,
+        device: dict,
+        energy_threshold: int,
+        dynamic_energy_threshold: bool,
+        phrase_time_limit: int,
+        phrase_timeout: int = 1,
+        record_timeout: int = 5,
+    ) -> None:
         source = Microphone(
             device_index=device['index'],
             sample_rate=int(device["defaultSampleRate"]),
@@ -139,14 +170,23 @@ class SelectedMicEnergyAndAudioRecorder(BaseEnergyAndAudioRecorder):
         )
         # self.adjustForNoise()
 
+
 class SelectedSpeakerEnergyAndAudioRecorder(BaseEnergyAndAudioRecorder):
-    def __init__(self, device, energy_threshold, dynamic_energy_threshold, phrase_time_limit, phrase_timeout:int=1, record_timeout:int=5):
+    def __init__(
+        self,
+        device: dict,
+        energy_threshold: int,
+        dynamic_energy_threshold: bool,
+        phrase_time_limit: int,
+        phrase_timeout: int = 1,
+        record_timeout: int = 5,
+    ) -> None:
 
         source = Microphone(speaker=True,
             device_index= device["index"],
             sample_rate=int(device["defaultSampleRate"]),
             chunk_size=get_sample_size(paInt16),
-            channels=device["maxInputChannels"]
+            channels=device["maxInputChannels"],
         )
         super().__init__(
             source=source,

src-python/models/transcription/transcription_transcriber.py

@@ -1,7 +1,14 @@
+"""Runtime transcriber that wraps Google SpeechRecognition and faster-whisper.
+
+This class focuses on converting incoming raw audio buffers into text using
+either the Google web recognizer (online) or a local Whisper model (offline).
+"""
+
 import time
 from io import BytesIO
 from threading import Event
 import wave
+from typing import Any, Callable, Dict, List, Optional, Tuple
 from speech_recognition import Recognizer, AudioData, AudioFile
 from speech_recognition.exceptions import UnknownValueError
 from datetime import timedelta
@@ -20,38 +27,71 @@ warnings.simplefilter('ignore', RuntimeWarning)
 PHRASE_TIMEOUT = 3
 MAX_PHRASES = 10
 
+
 class AudioTranscriber:
-    def __init__(self, speaker, source, phrase_timeout, max_phrases, transcription_engine, root=None, whisper_weight_type=None, device="cpu", device_index=0, compute_type="auto"):
+    """Convert queued audio buffers into transcripts.
+
+    Public attributes set by the constructor:
+    - speaker: bool
+    - phrase_timeout: int
+    - max_phrases: int
+
+    Methods are intentionally permissive about input types to match the
+    existing codebase; this wrapper adds typing for clarity.
+    """
+
+    def __init__(
+        self,
+        speaker: bool,
+        source: Any,
+        phrase_timeout: int,
+        max_phrases: int,
+        transcription_engine: str,
+        root: Optional[str] = None,
+        whisper_weight_type: Optional[str] = None,
+        device: str = "cpu",
+        device_index: int = 0,
+        compute_type: str = "auto",
+    ) -> None:
         self.speaker = speaker
         self.phrase_timeout = phrase_timeout
         self.max_phrases = max_phrases
-        self.transcript_data = []
+        self.transcript_data: List[Dict[str, Any]] = []
         self.transcript_changed_event = Event()
         self.audio_recognizer = Recognizer()
         self.transcription_engine = "Google"
         self.whisper_model = None
-        self.audio_sources = {
+        self.audio_sources: Dict[str, Any] = {
             "sample_rate": source.SAMPLE_RATE,
             "sample_width": source.SAMPLE_WIDTH,
             "channels": source.channels,
             "last_sample": bytes(),
             "last_spoken": None,
             "new_phrase": True,
-                "process_data_func": self.processSpeakerData if speaker else self.processSpeakerData
+            "process_data_func": self.processSpeakerData if speaker else self.processSpeakerData,
         }
 
         if transcription_engine == "Whisper" and checkWhisperWeight(root, whisper_weight_type) is True:
-            self.whisper_model = getWhisperModel(root, whisper_weight_type, device=device, device_index=device_index, compute_type=compute_type)
+            self.whisper_model = getWhisperModel(
+                root, whisper_weight_type, device=device, device_index=device_index, compute_type=compute_type
+            )
             self.transcription_engine = "Whisper"
 
-    def transcribeAudioQueue(self, audio_queue, languages, countries, avg_logprob=-0.8, no_speech_prob=0.6):
+    def transcribeAudioQueue(
+        self,
+        audio_queue: Any,
+        languages: List[str],
+        countries: List[str],
+        avg_logprob: float = -0.8,
+        no_speech_prob: float = 0.6,
+    ) -> bool:
         if audio_queue.empty():
             time.sleep(0.01)
             return False
         audio, time_spoken = audio_queue.get()
         self.updateLastSampleAndPhraseStatus(audio, time_spoken)
 
-        confidences = [{"confidence": 0, "text": "", "language": None}]
+        confidences: List[Dict[str, Any]] = [{"confidence": 0, "text": "", "language": None}]
         try:
             audio_data = self.audio_sources["process_data_func"]()
             match self.transcription_engine:
@@ -67,13 +107,19 @@ class AudioTranscriber:
                         except Exception:
                             pass
                 case "Whisper":
-                    audio_data = np.frombuffer(audio_data.get_raw_data(convert_rate=16000, convert_width=2), np.int16).flatten().astype(np.float32) / 32768.0
+                    audio_data = np.frombuffer(
+                        audio_data.get_raw_data(convert_rate=16000, convert_width=2), np.int16
+                    ).flatten().astype(np.float32) / 32768.0
                     if isinstance(audio_data, torch.Tensor):
                         audio_data = audio_data.detach().numpy()
 
                     for language, country in zip(languages, countries):
                         text = ""
-                        source_language = transcription_lang[language][country][self.transcription_engine] if len(languages) == 1 else None
+                        source_language = (
+                            transcription_lang[language][country][self.transcription_engine]
+                            if len(languages) == 1
+                            else None
+                        )
                         segments, info = self.whisper_model.transcribe(
                             audio_data,
                             beam_size=5,
@@ -91,7 +137,9 @@ class AudioTranscriber:
                                 continue
                             text += s.text
                         confidences.append({"confidence": info.language_probability, "text": text, "language": language})
-                        if (len(languages) == 1) or (transcription_lang[language][country][self.transcription_engine] == info.language):
+                        if (len(languages) == 1) or (
+                            transcription_lang[language][country][self.transcription_engine] == info.language
+                        ):
                             break
 
         except UnknownValueError:
@@ -106,7 +154,7 @@ class AudioTranscriber:
             self.updateTranscript(result)
         return True
 
-    def updateLastSampleAndPhraseStatus(self, data, time_spoken):
+    def updateLastSampleAndPhraseStatus(self, data: bytes, time_spoken) -> None:
         source_info = self.audio_sources
         if source_info["last_spoken"] and time_spoken - source_info["last_spoken"] > timedelta(seconds=self.phrase_timeout):
             source_info["last_sample"] = bytes()
@@ -117,11 +165,13 @@ class AudioTranscriber:
         source_info["last_sample"] += data
         source_info["last_spoken"] = time_spoken
 
-    def processMicData(self):
-        audio_data = AudioData(self.audio_sources["last_sample"], self.audio_sources["sample_rate"], self.audio_sources["sample_width"])
+    def processMicData(self) -> AudioData:
+        audio_data = AudioData(
+            self.audio_sources["last_sample"], self.audio_sources["sample_rate"], self.audio_sources["sample_width"]
+        )
         return audio_data
 
-    def processSpeakerData(self):
+    def processSpeakerData(self) -> AudioData:
         temp_file = BytesIO()
         with wave.open(temp_file, 'wb') as wf:
             wf.setnchannels(self.audio_sources["channels"])
@@ -141,7 +191,7 @@ class AudioTranscriber:
             audio = self.audio_recognizer.record(source)
         return audio
 
-    def updateTranscript(self, result):
+    def updateTranscript(self, result: dict) -> None:
         source_info = self.audio_sources
         transcript = self.transcript_data
 
@@ -152,14 +202,14 @@ class AudioTranscriber:
         else:
             transcript[0] = result
 
-    def getTranscript(self):
+    def getTranscript(self) -> dict:
         if len(self.transcript_data) > 0:
             result = self.transcript_data.pop(-1)
         else:
             result = {"confidence": 0, "text": "", "language": None}
         return result
 
-    def clearTranscriptData(self):
+    def clearTranscriptData(self) -> None:
         self.transcript_data.clear()
         self.audio_sources["last_sample"] = bytes()
         self.audio_sources["new_phrase"] = True

src-python/models/transcription/transcription_whisper.py

@@ -1,6 +1,17 @@
+"""Helpers for downloading and loading Whisper (faster-whisper) models.
+
+This module exposes small utilities used by the transcription subsystem:
+- downloadFile: stream-download a file with optional progress callback
+- checkWhisperWeight: quick local availability check
+- downloadWhisperWeight: download model artifacts from HF hub
+- getWhisperModel: construct and return a WhisperModel instance
+
+The functions are defensive: failures are caught and reported by the caller.
+"""
+
 from os import path as os_path, makedirs as os_makedirs
 from requests import get as requests_get
-from typing import Callable
+from typing import Callable, Optional
 import huggingface_hub
 from faster_whisper import WhisperModel
 import logging
@@ -30,7 +41,14 @@ _FILENAMES = [
     "vocabulary.json",
 ]
 
-def downloadFile(url, path, func=None):
+def downloadFile(url: str, path: str, func: Optional[Callable[[float], None]] = None) -> None:
+    """Download a file from `url` to `path`.
+
+    Args:
+        url: remote URL to download from
+        path: local filepath to write
+        func: optional callback(progress: float) called with a 0.0-1.0 progress
+    """
     try:
         res = requests_get(url, stream=True)
         res.raise_for_status()
@@ -39,15 +57,20 @@ def downloadFile(url, path, func=None):
         with open(os_path.join(path), 'wb') as file:
             for chunk in res.iter_content(chunk_size=1024 * 2000):
                 file.write(chunk)
-                if isinstance(func, Callable):
+                if callable(func) and file_size:
                     total_chunk += len(chunk)
                     func(total_chunk / file_size)
     except Exception:
+        # Silent failure here; caller may re-check or log
         pass
 
-def checkWhisperWeight(root, weight_type):
+def checkWhisperWeight(root: str, weight_type: str) -> bool:
+    """Return True if a Whisper model for `weight_type` is loadable from disk.
+
+    This attempts to construct a local `WhisperModel` with local_files_only=True
+    to verify required files exist and are compatible.
+    """
     path = os_path.join(root, "weights", "whisper", weight_type)
-    result = False
     try:
         WhisperModel(
             path,
@@ -58,23 +81,47 @@ def checkWhisperWeight(root, weight_type):
             num_workers=1,
             local_files_only=True,
         )
-        result = True
+        return True
     except Exception:
-        pass
-    return result
+        return False
 
-def downloadWhisperWeight(root, weight_type, callback=None, end_callback=None):
+def downloadWhisperWeight(
+    root: str,
+    weight_type: str,
+    callback: Optional[Callable[[float], None]] = None,
+    end_callback: Optional[Callable[[], None]] = None,
+) -> None:
+    """Ensure Whisper weight files are present locally; download them if missing.
+
+    Args:
+        root: project root where `weights/whisper` lives
+        weight_type: key from `_MODELS` (eg. "tiny", "base")
+        callback: progress callback for the main model file
+        end_callback: called when download completes
+    """
     path = os_path.join(root, "weights", "whisper", weight_type)
     os_makedirs(path, exist_ok=True)
-    if checkWhisperWeight(root, weight_type) is False:
+    if not checkWhisperWeight(root, weight_type):
         for filename in _FILENAMES:
             file_path = os_path.join(path, filename)
             url = huggingface_hub.hf_hub_url(_MODELS[weight_type], filename)
             downloadFile(url, file_path, func=callback if filename == "model.bin" else None)
-    if isinstance(end_callback, Callable):
+    if callable(end_callback):
         end_callback()
 
-def getWhisperModel(root, weight_type, device="cpu", device_index=0, compute_type="auto"):
+def getWhisperModel(
+    root: str,
+    weight_type: str,
+    device: str = "cpu",
+    device_index: int = 0,
+    compute_type: str = "auto",
+) -> WhisperModel:
+    """Return a `WhisperModel` instance loaded from local weights.
+
+    Raises:
+        ValueError: when VRAM shortage is detected (wrapped from RuntimeError)
+        Exception: other loading errors are propagated.
+    """
     path = os_path.join(root, "weights", "whisper", weight_type)
     if compute_type == "auto":
         compute_type = getBestComputeType(device, device_index)
@@ -90,11 +137,10 @@ def getWhisperModel(root, weight_type, device="cpu", device_index=0, compute_typ
         )
         return model
     except RuntimeError as e:
-        # VRAM不足エラーの検出
+        # Detect VRAM out-of-memory-like errors and raise a clear ValueError
         error_message = str(e)
         if "CUDA out of memory" in error_message or "CUBLAS_STATUS_ALLOC_FAILED" in error_message:
             raise ValueError("VRAM_OUT_OF_MEMORY", error_message)
-        # その他のエラーは通常通り再送出
         raise
 
 if __name__ == "__main__":