pipe処理を生成

requirements.txt

@@ -1 +1,6 @@
 librosalibrosa
+pyrnnoise
+resampy
+soundfile
+numpy
+modal

src/app_status.py

@@ -61,12 +61,59 @@ class AppStatus(Singleton):
         source_dir = f"{self.output_base_dir}/{self.request_id}/source"
         return source_dir
 
+    @property
+    def preprocess_dir(self)-> str:
+        """前処理ディレクトリのパス"""
+        preprocess_dir = f"{self.output_base_dir}/{self.request_id}/preprocess"
+        return preprocess_dir
+
+    @property
+    def preprocess_index(self)-> int:
+        """前処理のインデックス"""
+        return self.get_status('preprocess_index', default=0)
+
+    @preprocess_index.setter
+    def preprocess_index(self, value:int):
+        """前処理のインデックス"""
+        self.set_status('preprocess_index', value)
+        if value == 0:
+            self.set_status('preprocess_inputfile', self.unified_file)
+
+    @property
+    def preprocess_inputfile(self)-> str:
+        """前処理の入力ファイルのパス"""
+        return self.get_status('preprocess_inputfile')
+
+    @preprocess_inputfile.setter
+    def preprocess_inputfile(self, value:str):
+        """前処理の入力ファイルのパス"""
+        self.set_status('preprocess_inputfile', value)
+
+    @property
+    def preprocess_outputfile(self)-> str:
+        """前処理の出力ファイルのパス"""
+        index = self.preprocess_index
+        filename = f"preprocess_{index:02d}.wav"
+        output_file = f"{self.preprocess_dir}/{filename}"
+        return output_file
+
+
     @property
     def chunk_dir(self)-> str:
         """チャンクディレクトリのパス"""
         chunk_dir = f"{self.output_base_dir}/{self.request_id}/chunk"
         return chunk_dir
 
+    @property
+    def chunk_manifest(self)-> str:
+        """チャンクマニフェストのパス"""
+        return f"{self.chunk_dir}/chunks.manifest.json"
+
+    @property
+    def transcript_dir(self)-> str:
+        """文字起こし結果ディレクトリのパス"""
+        transcript_dir = f"{self.output_base_dir}/{self.request_id}/transcripts"
+        return transcript_dir
 
 
     @property
@@ -83,3 +130,9 @@ class AppStatus(Singleton):
     def unified_file(self)-> str:
         """統一ファイルのパス"""
         return f"{self.output_dir}/unified.wav"
+
+    @property
+    def diarization_file(self)-> str:
+        """話者分離のJSONファイルのパス"""
+        return f"{self.output_dir}/diarization.json"
+

src/jobs/job_chunk_with_diarization.py

@@ -0,0 +1,195 @@
+# jobs/job_chunk_files.py
+import os
+import json
+import subprocess
+from pathlib import Path
+from jobs.job_base import JobBase
+
+
+class JobChunkWithDiarization(JobBase):
+    """音声ファイルをチャンクに分割するジョブ
+    * 7分ターゲット(CHUNK_TARGET_SEC=420)で粗く位置決め → ±5秒の範囲で無音にスナップ
+    * 0.4秒オーバーラップ(CHUNK_OVERLAP_SEC=0.4)
+    * 最短1分(CHUNK_MIN_LEN_SEC=60)
+    * chunks.manifest.jsonl / chunks.manifest.json を保存して、絶対時刻(abs_start/abs_end)と切り出し実時間(cut_start/cut_end) を持たせます。
+    * 後段の ASR 結果(チャンク内時刻)に abs_start を足すだけで全体時刻に戻せます。
+    
+    """
+
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Chunk Audio Files Job"
+
+        # デフォルト設定(環境変数で上書き可)
+        self.target_len = float(os.getenv("CHUNK_TARGET_SEC", "420"))  # 7分
+        self.overlap = float(os.getenv("CHUNK_OVERLAP_SEC", "0.4"))    # 0.4s
+        self.snap_win = float(os.getenv("CHUNK_SNAP_WINDOW_SEC", "5.0"))   # 境界±5sで無音にスナップ
+        self.min_sil = float(os.getenv("CHUNK_MIN_SILENCE_SEC", "0.25"))   # 無音とみなす最小長
+        self.min_len = float(os.getenv("CHUNK_MIN_LEN_SEC", "60"))         # 最短1分
+        self.max_len = float(os.getenv("CHUNK_MAX_LEN_SEC", "480"))        # 最長8分(fallback)        
+
+    def _ffprobe_duration(self, src: str) -> float:
+        out = subprocess.check_output(
+            ["ffprobe", "-v", "error", "-show_entries", "format=duration",
+            "-of", "default=noprint_wrappers=1:nokey=1", src],
+            text=True
+        ).strip()
+        return float(out)
+
+    def _invert_vad_to_silences(self, vad: list[dict], total: float) -> list[tuple[float, float]]:
+        """VAD区間(発話)→ サイレンス区間に反転して [ (s,e), ... ] を返す"""
+        if not vad:
+            return [(0.0, total)]
+        vad_sorted = sorted([(float(v["start"]), float(v["end"])) for v in vad if v["end"] > v["start"]], key=lambda x: x[0])
+
+        silences = []
+        t = 0.0
+        for s, e in vad_sorted:
+            if s > t:
+                silences.append((t, s))
+            t = max(t, e)
+        if t < total:
+            silences.append((t, total))
+
+        # 短すぎるサイレンスは無視
+        silences = [(s, e) for (s, e) in silences if (e - s) >= self.min_sil]
+        return silences
+
+    def _nearest_silence_boundary(self, target_t: float, silences: list[tuple[float, float]], window: float) -> float | None:
+        """target_t の±window 内にあるサイレンスの “中点” または端点にスナップ(中点優先)"""
+        cand = []
+        lo, hi = target_t - window, target_t + window
+        for s, e in silences:
+            mid = 0.5 * (s + e)
+            for t in (mid, s, e):  # mid優先で評価
+                if lo <= t <= hi:
+                    cand.append((abs(t - target_t), t))
+        if not cand:
+            return None
+        cand.sort(key=lambda x: x[0])
+        return cand[0][1]
+
+    def _cut_wav(self, src: str, dst: str, start: float, end: float):
+        Path(dst).parent.mkdir(parents=True, exist_ok=True)
+        dur = max(0.01, end - start)
+        # ここで ASR 向けに 16k/mono PCM16 へ正規化出力
+        cmd = [
+            "ffmpeg", "-y",
+            "-i", src,
+            "-ss", f"{start:.3f}",
+            "-t", f"{dur:.3f}",
+            "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le",
+            dst
+        ]
+        subprocess.run(cmd, check=True)
+
+    def _build_chunks_with_vad_snap(self, src: str, out_dir: str, diar_json_path: str):
+        out = Path(out_dir)
+        out.mkdir(parents=True, exist_ok=True)
+
+        total = self._ffprobe_duration(src)
+
+        # ダイアリゼーション(VAD)読み込み
+        diar: dict = json.loads(Path(diar_json_path).read_text(encoding="utf-8"))
+        vad_list = diar.get("vad") or []
+        silences = self._invert_vad_to_silences(vad_list, total)
+
+        chunks = []
+        manifest = []
+        i = 0
+        cur = 0.0
+
+        # 先頭チャンクの開始は0固定(頭に十分な無音があっても0開始にする)
+        while cur < total:
+            # 目標終端
+            target_end = cur + self.target_len
+
+            # 上限/下限ガード
+            hard_max = min(cur + self.max_len, total)
+            hard_min = min(cur + self.min_len, total)
+
+            # スナップ対象の目標(total超えるなら total)
+            soft_target = min(target_end, total)
+
+            # 近傍の無音にスナップ
+            snapped_end = self._nearest_silence_boundary(soft_target, silences, self.snap_win)
+
+            # 適用条件:
+            #  - スナップ後が hard_min 以上
+            #  - かつ hard_max 以下
+            if snapped_end is not None:
+                end = max(hard_min, min(snapped_end, hard_max))
+            else:
+                # 無音が見つからなければ、hard_max を超えない範囲で soft_target を採用
+                end = max(hard_min, min(soft_target, hard_max))
+
+            # オーバーラップ付与(右にだけ与える設計。左は前チャンクが担保)
+            cut_start = max(0.0, cur - self.overlap)
+            cut_end = min(total, end + self.overlap)
+
+            dst = out / f"{i:06d}.wav"
+            self._cut_wav(src, str(dst), cut_start, cut_end)
+
+            chunks.append({
+                "chunk_id": f"{i:06d}",
+                "abs_start": round(cur, 3),
+                "abs_end": round(end, 3),
+                "cut_start": round(cut_start, 3),
+                "cut_end": round(cut_end, 3),
+                "path": str(dst),
+            })
+            manifest.append({
+                "chunk_id": f"{i:06d}",
+                "abs_start": round(cur, 3),
+                "abs_end": round(end, 3),
+                "overlap_left": self.overlap if cur > 0.0 else 0.0,
+                "overlap_right": self.overlap if end < total else 0.0,
+                "path": str(dst),
+            })
+
+            i += 1
+            cur = end  # 次の開始は今回の終端
+
+        # マニフェスト保存(チャンクディレクトリ直下に)
+        (out / "chunks.manifest.jsonl").write_text(
+            "\n".join(json.dumps(m, ensure_ascii=False) for m in manifest),
+            encoding="utf-8"
+        )
+
+        # ついでに JSON でも
+        (out / "chunks.manifest.json").write_text(
+            json.dumps({"duration": total, "chunks": chunks}, ensure_ascii=False, indent=2),
+            encoding="utf-8"
+        )
+        return {"duration": total, "chunks": chunks}
+
+    def _has_existing_chunks(self, chunk_dir: str) -> bool:
+        p = Path(chunk_dir)
+        return p.exists() and any(p.glob("*.wav"))
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        # 既にチャンク済か軽く判定(ディレクトリ存在だけでなく *.wav があるか)
+        if self._has_existing_chunks(self.status.chunk_dir):
+            self.logger.info(f"Chunks already exist: {self.status.chunk_dir}")
+            return
+
+        # 入力音声(標準化/前処理後のファイルを想定)
+        src = self.status.unified_file  # ここはあなたのパイプラインに合わせて
+        diar_json = self.status.diarization_file  # 直前のジョブが保存した JSON
+        dst_dir = self.status.chunk_dir
+
+        if not src or not Path(src).exists():
+            raise FileNotFoundError(f"Input audio not found: {src}")
+        if not diar_json or not Path(diar_json).exists():
+            raise FileNotFoundError(f"Diarization JSON not found: {diar_json}")
+
+        self.logger.info(f"Chunk target ~{self.target_len}s, overlap {self.overlap}s, snap ±{self.snap_win}s, min_sil {self.min_sil}s")
+        meta = self._build_chunks_with_vad_snap(src, dst_dir, diar_json)
+
+        # 後工程用に状態に積む
+        self.status.set_status("chunks_manifest", str(Path(dst_dir) / "chunks.manifest.json"))
+        self.logger.info(f"Chunking done: {len(meta['chunks'])} files -> {dst_dir}")
+
+        return 

src/jobs/job_diarization.py

@@ -0,0 +1,60 @@
+import os
+import json
+import time
+from pathlib import Path
+import modal
+from jobs.job_base import JobBase
+
+class JobDiarization(JobBase):
+    """話者分離を行うジョブ (pyannote.audio)"""
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Diarization Job"
+        # 環境変数で上書き可能に
+        self.modal_app_name  = os.getenv("MODAL_DIAR_APP",  "speaker-diarization-pyannote")
+        self.modal_func_name = os.getenv("MODAL_DIAR_FUNC", "diarize_audio")
+        # リトライ設定（ネットワーク/一時エラー対策）
+        self.max_retries = int(os.getenv("MODAL_DIAR_RETRIES", "2"))
+        self.retry_wait  = float(os.getenv("MODAL_DIAR_RETRY_WAIT", "2.0"))  # 秒
+
+    def _call_modal(self, audio_bytes: bytes, filename: str) -> dict:
+        """Modal の diarize 関数を呼ぶ（軽いリトライ付き）"""
+        func = modal.Function.from_name(self.modal_app_name, self.modal_func_name)
+        last_exc = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                return func.remote(audio_bytes, filename=filename)
+            except Exception as e:
+                last_exc = e
+                if attempt < self.max_retries:
+                    time.sleep(self.retry_wait * (attempt + 1))
+                else:
+                    raise
+        # 通らないが型のため
+        raise last_exc
+
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        # 出力先ファイルパス
+        diar_json_path = Path(self.status.diarization_file)
+
+        if diar_json_path.exists():
+            # すでに話者分離済み
+            self.logger.info(f"Diarization already done: {self.status.diarization_file}")
+            return
+
+        audio_path = Path(self.status.preprocess_inputfile)
+        audio_bytes = audio_path.read_bytes()
+
+        # Modalで話者分離を実行
+        self.logger.info(f"Calling Modal: app={self.modal_app_name}, func={self.modal_func_name}, file={audio_path.name}")
+        res = self._call_modal(audio_bytes, filename=audio_path.name)
+
+        # JSON 保存
+        diar_json_path.parent.mkdir(parents=True, exist_ok=True)
+        diar_json_path.write_text(json.dumps(res, ensure_ascii=False, indent=2), encoding="utf-8")
+
+        self.logger.info(f"Diarization result saved: {diar_json_path}")
+        return

src/jobs/job_enhancement_rnnoise.py

@@ -0,0 +1,62 @@
+import os
+import soundfile as sf
+import numpy as np
+from pathlib import Path
+from pyrnnoise import RNNoise
+from jobs.job_base import JobBase
+
+
+class JobEnhancementRnnoise(JobBase):
+    """音声ファイルの音声強調(RNNoise)を行うジョブ"""
+    
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Enhancement RNNoise Fmmpeg Job"
+
+    def _enhance_rnnoise(self, src: str, dst: str):
+        # 入力の実サンプルレートを取得して RNNoise に渡す
+        info = sf.info(src)
+        rn = RNNoise(sample_rate=info.samplerate)
+
+        tmp = str(Path(dst).with_suffix(".rnnoise.tmp.wav"))
+        try:
+            # ファイル→ファイルでRNNoiseを適用（内部で48k変換→戻しをやってくれる）
+            for _ in rn.denoise_wav(src, tmp):
+                pass
+        finally:
+            rn.reset()
+            del rn
+
+        # 任意: -3 dBFS の軽いピーク正規化
+        y, sr = sf.read(tmp, dtype="float32", always_2d=False)
+        peak = float(np.max(np.abs(y)) + 1e-12)
+        if peak > 0:
+            target_lin = 10 ** (-3 / 20)  # ≈ 0.707
+            y *= min(1.0, target_lin / peak)
+
+        sf.write(dst, y, sr, subtype="PCM_16")
+        try:
+            os.remove(tmp)
+        except OSError:
+            pass
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        if not os.path.exists(self.status.preprocess_dir):
+            self.status.preprocess_index = 0
+        elif os.path.exists(self.status.preprocess_outputfile):
+            # preprocess_dirが存在する場合かつ、 preprocess_outputfileが存在する場合
+            self.logger.info(f"Preprocess output file already exists: {self.status.preprocess_outputfile}")
+            self.status.preprocess_inputfile = self.status.preprocess_outputfile
+            self.status.preprocess_index += 1
+            return
+
+
+        input_file = self.status.preprocess_inputfile
+        output_file = self.status.preprocess_outputfile
+        self._enhance_rnnoise(input_file, output_file)
+
+        self.status.preprocess_inputfile = output_file
+        self.status.preprocess_index += 1
+        return

src/jobs/job_pre_denoizing_fmmpeg.py

@@ -0,0 +1,46 @@
+import os
+from jobs.job_base import JobBase
+
+class JobPreDenoizingFmmpeg(JobBase):
+
+    """音声ファイルの前処理（ノイズ除去）を行うジョブ"""
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Pre Denoizing Fmmpeg Job"
+
+    def _denoise_ffmpeg(self, src, dst):
+        import subprocess, pathlib
+        pathlib.Path(dst).parent.mkdir(parents=True, exist_ok=True)
+        cmd = [
+            "ffmpeg", "-y", "-i", src,
+            "-af", (
+                "highpass=f=60,"         # 低域カット
+                "lowpass=f=9000,"        # 高域ノイズ軽減
+                "afftdn=nr=12:nf=-25,"   # 軽いノイズ除去（FFTベース）
+                "dynaudnorm=p=0.5"       # 軽い音量均し(強めたい場合は:m=15:s=3)
+            ),
+            "-ar", "16000", "-ac", "1", # STT用に16kHz/モノラル変換
+            dst
+        ]
+        subprocess.run(cmd, check=True)
+
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        if not os.path.exists(self.status.preprocess_dir):
+            self.status.preprocess_index = 0
+        elif os.path.exists(self.status.preprocess_outputfile):
+            # preprocess_dirが存在する場合かつ、 preprocess_outputfileが存在する場合
+            self.logger.info(f"Preprocess output file already exists: {self.status.preprocess_outputfile}")
+            self.status.preprocess_inputfile = self.status.preprocess_outputfile
+            self.status.preprocess_index += 1
+            return
+
+        input_file = self.status.preprocess_inputfile
+        output_file = self.status.preprocess_outputfile
+        self._denoise_ffmpeg(input_file, output_file)
+
+        self.status.preprocess_inputfile = output_file
+        self.status.preprocess_index += 1
+        return

src/jobs/job_pre_normalize_fmmpeg.py

@@ -0,0 +1,43 @@
+import os
+from jobs.job_base import JobBase
+
+class JobPreNormalizingFmmpeg(JobBase):
+    """音声ファイルの前処理(ラウドネス正規化)を行うジョブ"""
+
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Pre Normalizing Fmmpeg Job"
+
+    def _normalize_ffmpeg(self, src, dst):
+        import subprocess, pathlib
+        pathlib.Path(dst).parent.mkdir(parents=True, exist_ok=True)
+        # ffmpeg -i input.wav -af "loudnorm=I=-23:TP=-2:LRA=11" -ar 16000 -ac 1 output_norm.wav
+        cmd = [
+            "ffmpeg", "-y", "-i", src,
+            "-af", "loudnorm=I=-23:TP=-2:LRA=11",
+            "-ar", "16000", "-ac", "1", dst
+        ]
+        subprocess.run(cmd, check=True)
+
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        if not os.path.exists(self.status.preprocess_dir):
+            self.status.preprocess_index = 0
+        elif os.path.exists(self.status.preprocess_outputfile):
+            # preprocess_dirが存在する場合かつ、 preprocess_outputfileが存在する場合
+            self.logger.info(f"Preprocess output file already exists: {self.status.preprocess_outputfile}")
+            self.status.preprocess_inputfile = self.status.preprocess_outputfile
+            self.status.preprocess_index += 1
+            return
+
+
+
+        input_file = self.status.preprocess_inputfile
+        output_file = self.status.preprocess_outputfile
+        self._normalize_ffmpeg(input_file, output_file)
+
+        self.status.preprocess_inputfile = output_file
+        self.status.preprocess_index += 1
+        return

src/jobs/job_transcribe_chk_modal.py

@@ -0,0 +1,159 @@
+import os
+import json
+import time
+import requests
+from typing import Any, Dict, List, Optional
+from pathlib import Path
+from jobs.job_base import JobBase
+
+class JobTranscribeChunkOpenAI(JobBase):
+    """
+    チャンク群をOpenAIで逐次文字起こしするジョブ
+
+        - 入力: chunks.manifest.json (JobChunkFilesで作成)
+        - 出力: チャンクごとの *.transcript.json と、統合 all.transcripts.json
+        - タイムスタンプを abs_start で全体時刻に補正
+    """
+
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Transcribe Chunks via OpenAI"
+
+        # 設定（環境変数で上書き可能）
+        # self.model = os.getenv("ASR_MODEL", "gpt-4o-transcribe")
+        self.model = os.getenv("ASR_MODEL", "whisper-1")
+        self.lang = os.getenv("ASR_LANG", "ja")
+        self.response_format = os.getenv("ASR_RESP_FMT", "json")
+        self.word_ts = os.getenv("ASR_WORD_TS", "1") in ("1", "true", "True")
+        self.req_timeout = int(os.getenv("ASR_TIMEOUT_SEC", "600"))
+        self.sleep_between = float(os.getenv("ASR_SLEEP_SEC", "0.3"))
+        self.max_retries = int(os.getenv("ASR_MAX_RETRIES", "2"))
+        self.retry_backoff = float(os.getenv("ASR_RETRY_BACKOFF", "1.5"))  # 乗算
+
+        # OpenAI
+        self.api_key = os.getenv("OPENAI_API_KEY")
+        self.api_url = os.getenv("OPENAI_TRANSCRIBE_URL", "https://api.openai.com/v1/audio/transcriptions")
+
+
+    def _manifest_path(self) -> Path:
+        return Path(self.status.chunk_manifest)
+
+    def _out_dir(self) -> Path:
+        return Path(self.status.transcript_dir)
+
+
+    def _transcribe_file(self, wav_path: str) -> Dict[str, Any]:
+        if not self.api_key:
+            raise RuntimeError("OPENAI_API_KEY が未設定です。環境変数に設定してください。")
+
+        if "transcribe" in self.model and self.response_format == "json":
+            response_format = "verbose_json"  
+        elif "whisper" in self.model and self.response_format == "json":
+            response_format = "verbose_json"
+        else:   
+            response_format = self.response_format
+
+        # self.model = os.getenv("ASR_MODEL", "gpt-4o-transcribe")
+        self.model = os.getenv("ASR_MODEL", "whisper-1")
+
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+        data = {
+            "model": self.model,
+            "response_format": response_format,
+            "language": self.lang,
+        }
+        # 単語タイムスタンプが欲しければ指定
+        if self.word_ts:
+            # OpenAIのフォームは配列っぽいキー名を要求
+            data["timestamp_granularities[]"] = "word"
+
+        # 軽いリトライ
+        wait = 1.0
+        last_err: Optional[Exception] = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                with open(wav_path, "rb") as f:
+                    files = {"file": (Path(wav_path).name, f, "audio/wav")}
+                    r = requests.post(self.api_url, headers=headers, data=data, files=files, timeout=self.req_timeout)
+                if r.status_code == 429 or 500 <= r.status_code < 600:
+                    raise requests.HTTPError(f"HTTP {r.status_code}: {r.text}")
+                r.raise_for_status()
+                return r.json()
+            except Exception as e:
+                last_err = e
+                if attempt < self.max_retries:
+                    self.logger.warning(f"ASR retry ({attempt+1}/{self.max_retries}) for {wav_path}: {e}")
+                    time.sleep(wait)
+                    wait *= self.retry_backoff
+                    continue
+                break
+        # リトライ尽きた
+        raise last_err if last_err else RuntimeError("Unknown ASR error")
+
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        if os.path.exists(self.status.transcript_dir):
+            # すでに変換済み
+            self.logger.info(f"Transcription already done: {self.status.transcript_dir}")
+            return
+
+        manifest_path = self._manifest_path()
+        if not manifest_path.exists():
+            raise FileNotFoundError(f"chunks manifest not found: {manifest_path}")
+
+        out_dir = self._out_dir()
+        out_dir.mkdir(parents=True, exist_ok=True)
+
+        meta = json.loads(manifest_path.read_text(encoding="utf-8"))
+        chunks: List[Dict[str, Any]] = meta.get("chunks") or []
+
+        if not chunks:
+            self.logger.warning("No chunks found in manifest. Skipping.")
+            return
+
+        results: List[Dict[str, Any]] = []
+
+        for ch in chunks:
+            wav = ch["path"]
+            per_chunk_out = out_dir / f"{Path(wav).stem}.transcript.json"
+
+            # レジューム対応：既に保存済みならスキップ
+            if per_chunk_out.exists():
+                res: dict = json.loads(per_chunk_out.read_text(encoding="utf-8"))
+            else:
+                self.logger.info(f"ASR: {wav}")
+                res: dict = self._transcribe_file(wav)
+                per_chunk_out.write_text(json.dumps(res, ensure_ascii=False, indent=2), encoding="utf-8")
+                time.sleep(self.sleep_between)
+
+            # チャンク内→全体時刻に補正
+            offset = float(ch["abs_start"])
+            words = res.get("words") or []
+            for w in words:
+                if "start" in w:
+                    w["start"] = float(w["start"]) + offset
+                if "end" in w:
+                    w["end"] = float(w["end"]) + offset
+            segments = res.get("segments") or []
+            for s in segments:
+                if "start" in s:
+                    s["start"] = float(s["start"]) + offset
+                if "end" in s:
+                    s["end"] = float(s["end"]) + offset
+
+            results.append({
+                "chunk_id": ch["chunk_id"],
+                "abs_start": ch["abs_start"],
+                "abs_end": ch["abs_end"],
+                "text": res.get("text", ""),
+                "segments": segments,
+                "words": words,
+            })
+
+        # 統合保存
+        all_out = out_dir / "all.transcripts.json"
+        all_out.write_text(json.dumps({"transcripts": results}, ensure_ascii=False, indent=2), encoding="utf-8")
+        self.logger.info(f"Transcription merged: {all_out}")
+

src/jobs/job_transcribe_chk_openai.py

@@ -0,0 +1,159 @@
+import os
+import json
+import time
+import requests
+from typing import Any, Dict, List, Optional
+from pathlib import Path
+from jobs.job_base import JobBase
+
+class JobTranscribeChunkOpenAI(JobBase):
+    """
+    チャンク群をOpenAIで逐次文字起こしするジョブ
+
+        - 入力: chunks.manifest.json (JobChunkFilesで作成)
+        - 出力: チャンクごとの *.transcript.json と、統合 all.transcripts.json
+        - タイムスタンプを abs_start で全体時刻に補正
+    """
+
+    def __init__(self):
+        super().__init__(name=self.__class__.__name__)
+        self.description = "Transcribe Chunks via OpenAI"
+
+        # 設定（環境変数で上書き可能）
+        # self.model = os.getenv("ASR_MODEL", "gpt-4o-transcribe")
+        self.model = os.getenv("ASR_MODEL", "whisper-1")
+        self.lang = os.getenv("ASR_LANG", "ja")
+        self.response_format = os.getenv("ASR_RESP_FMT", "json")
+        self.word_ts = os.getenv("ASR_WORD_TS", "1") in ("1", "true", "True")
+        self.req_timeout = int(os.getenv("ASR_TIMEOUT_SEC", "600"))
+        self.sleep_between = float(os.getenv("ASR_SLEEP_SEC", "0.3"))
+        self.max_retries = int(os.getenv("ASR_MAX_RETRIES", "2"))
+        self.retry_backoff = float(os.getenv("ASR_RETRY_BACKOFF", "1.5"))  # 乗算
+
+        # OpenAI
+        self.api_key = os.getenv("OPENAI_API_KEY")
+        self.api_url = os.getenv("OPENAI_TRANSCRIBE_URL", "https://api.openai.com/v1/audio/transcriptions")
+
+
+    def _manifest_path(self) -> Path:
+        return Path(self.status.chunk_manifest)
+
+    def _out_dir(self) -> Path:
+        return Path(self.status.transcript_dir)
+
+
+    def _transcribe_file(self, wav_path: str) -> Dict[str, Any]:
+        if not self.api_key:
+            raise RuntimeError("OPENAI_API_KEY が未設定です。環境変数に設定してください。")
+
+        if "transcribe" in self.model and self.response_format == "json":
+            response_format = "verbose_json"  
+        elif "whisper" in self.model and self.response_format == "json":
+            response_format = "verbose_json"
+        else:   
+            response_format = self.response_format
+
+        # self.model = os.getenv("ASR_MODEL", "gpt-4o-transcribe")
+        self.model = os.getenv("ASR_MODEL", "whisper-1")
+
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+        data = {
+            "model": self.model,
+            "response_format": response_format,
+            "language": self.lang,
+        }
+        # 単語タイムスタンプが欲しければ指定
+        if self.word_ts:
+            # OpenAIのフォームは配列っぽいキー名を要求
+            data["timestamp_granularities[]"] = "word"
+
+        # 軽いリトライ
+        wait = 1.0
+        last_err: Optional[Exception] = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                with open(wav_path, "rb") as f:
+                    files = {"file": (Path(wav_path).name, f, "audio/wav")}
+                    r = requests.post(self.api_url, headers=headers, data=data, files=files, timeout=self.req_timeout)
+                if r.status_code == 429 or 500 <= r.status_code < 600:
+                    raise requests.HTTPError(f"HTTP {r.status_code}: {r.text}")
+                r.raise_for_status()
+                return r.json()
+            except Exception as e:
+                last_err = e
+                if attempt < self.max_retries:
+                    self.logger.warning(f"ASR retry ({attempt+1}/{self.max_retries}) for {wav_path}: {e}")
+                    time.sleep(wait)
+                    wait *= self.retry_backoff
+                    continue
+                break
+        # リトライ尽きた
+        raise last_err if last_err else RuntimeError("Unknown ASR error")
+
+
+    def execute(self):
+        self.logger.info(f"{self.name} execute started")
+
+        if os.path.exists(self.status.transcript_dir):
+            # すでに変換済み
+            self.logger.info(f"Transcription already done: {self.status.transcript_dir}")
+            return
+
+        manifest_path = self._manifest_path()
+        if not manifest_path.exists():
+            raise FileNotFoundError(f"chunks manifest not found: {manifest_path}")
+
+        out_dir = self._out_dir()
+        out_dir.mkdir(parents=True, exist_ok=True)
+
+        meta = json.loads(manifest_path.read_text(encoding="utf-8"))
+        chunks: List[Dict[str, Any]] = meta.get("chunks") or []
+
+        if not chunks:
+            self.logger.warning("No chunks found in manifest. Skipping.")
+            return
+
+        results: List[Dict[str, Any]] = []
+
+        for ch in chunks:
+            wav = ch["path"]
+            per_chunk_out = out_dir / f"{Path(wav).stem}.transcript.json"
+
+            # レジューム対応：既に保存済みならスキップ
+            if per_chunk_out.exists():
+                res: dict = json.loads(per_chunk_out.read_text(encoding="utf-8"))
+            else:
+                self.logger.info(f"ASR: {wav}")
+                res: dict = self._transcribe_file(wav)
+                per_chunk_out.write_text(json.dumps(res, ensure_ascii=False, indent=2), encoding="utf-8")
+                time.sleep(self.sleep_between)
+
+            # チャンク内→全体時刻に補正
+            offset = float(ch["abs_start"])
+            words = res.get("words") or []
+            for w in words:
+                if "start" in w:
+                    w["start"] = float(w["start"]) + offset
+                if "end" in w:
+                    w["end"] = float(w["end"]) + offset
+            segments = res.get("segments") or []
+            for s in segments:
+                if "start" in s:
+                    s["start"] = float(s["start"]) + offset
+                if "end" in s:
+                    s["end"] = float(s["end"]) + offset
+
+            results.append({
+                "chunk_id": ch["chunk_id"],
+                "abs_start": ch["abs_start"],
+                "abs_end": ch["abs_end"],
+                "text": res.get("text", ""),
+                "segments": segments,
+                "words": words,
+            })
+
+        # 統合保存
+        all_out = out_dir / "all.transcripts.json"
+        all_out.write_text(json.dumps({"transcripts": results}, ensure_ascii=False, indent=2), encoding="utf-8")
+        self.logger.info(f"Transcription merged: {all_out}")
+

src/jobs/job_visualize_audio.py

@@ -15,9 +15,11 @@ class JobVisualizeAudio(JobBase):
     """
     音声の波形とスペクトログラムを可視化するジョブ (CPU)
     """
-    def __init__(self):
+    def __init__(self,input_file=None,dir_name="unified"):
         super().__init__(name=self.__class__.__name__)
         self.description = "Visualize Audio (waveform & spectrogram)"
+        self.dir_name = dir_name
+        self.input_file = input_file if input_file else self.status.unified_file
 
     def get_visualization(self, audio_path: str, out_dir: str,
                 n_fft: int = 1024, hop_length: int = 256):
@@ -72,11 +74,11 @@ class JobVisualizeAudio(JobBase):
     def execute(self):
         self.logger.info(f"{self.name} execute started")
 
-        if os.path.exists(f"{self.status.output_dir}/unified"):
+        if os.path.exists(f"{self.status.output_dir}/{self.dir_name}"):
             # すでに可視化済み
-            self.logger.info(f"Visualization already done: {self.status.output_dir}/unified")
+            self.logger.info(f"Visualization already done: {self.status.output_dir}/{self.dir_name}")
             return
 
-        audio_path = self.status.unified_file
+        audio_path = self.input_file
-        self.get_visualization(audio_path, f"{self.status.output_dir}/unified")
+        self.get_visualization(audio_path, f"{self.status.output_dir}/{self.dir_name}")
         return

src/main.py

@@ -6,6 +6,9 @@ from lib.custom_logger import get_logger
 from app_status import AppStatus
 from app import app_start
 
+from dotenv import load_dotenv
+load_dotenv()  # .envファイルの内容を環境変数に読み
+
 def main():
     parser = argparse.ArgumentParser(description="Speech to Text Pipeline")
     parser.add_argument("filepath", type=str, help="Path to the audio file")

src/pipeline/app_pipeline.py

@@ -3,7 +3,13 @@ from jobs.job_get_request_id import JobGetRequestId
 from jobs.job_set_soruce_file import JobSetSourceFile
 from jobs.job_standardize_format import JobStandardizeFormat
 from jobs.job_visualize_audio import JobVisualizeAudio
-from jobs.job_chunk_files import JobChunkFiles
+# from jobs.job_chunk_files import JobChunkFiles
+from jobs.job_chunk_with_diarization import JobChunkWithDiarization
+from jobs.job_pre_denoizing_fmmpeg import JobPreDenoizingFmmpeg
+from jobs.job_pre_normalize_fmmpeg import JobPreNormalizingFmmpeg
+from jobs.job_enhancement_rnnoise import JobEnhancementRnnoise
+from jobs.job_diarization import JobDiarization
+from jobs.job_transcribe_chk_openai import JobTranscribeChunkOpenAI
 
 class AppPipeline(PipelineBase):
     """アプリケーションのパイプライン"""
@@ -13,7 +19,20 @@ class AppPipeline(PipelineBase):
         self.add_job(JobGetRequestId())
         self.add_job(JobSetSourceFile())
         self.add_job(JobStandardizeFormat())
-        self.add_job(JobChunkFiles())
+        self.add_job(JobVisualizeAudio())
-        self
+        # self.add_job(JobChunkFiles())
+        self.add_job(JobPreDenoizingFmmpeg()) # 前処理　定常ノイズ除去(弱)
+        self.add_job(JobPreNormalizingFmmpeg()) # 前処理　ラウドネス正規化
+        # self.add_job(JobEnhancementRnnoise()) # 音声強調 RNNoise(弱)
+        # 必須: ピーク正規化（-1 dBFS）
+        # 推奨: ラウドネス正規化（-23 LUFS, TruePeak -2 dBFS）
+        # 任意: ピークリミッタ（会議参加者が大声を出す環境なら）
+        self.add_job(JobVisualizeAudio(
+            input_file=self.status.preprocess_inputfile, 
+            dir_name="ready"
+        ))
+        self.add_job(JobDiarization()) # 話者分離
+        self.add_job(JobChunkWithDiarization()) # チャンク分割(話者分離結果考慮)
+        self.add_job(JobTranscribeChunkOpenAI()) # チャンクごとにOpenAIで文字起こし
 
 

src/pipeline/pipeline_base.py

@@ -1,5 +1,6 @@
 from typing import List
 from jobs.job_base import JobBase
+from app_status import AppStatus
 from lib.custom_logger import get_logger
 logger = get_logger()
 
@@ -8,6 +9,7 @@ class PipelineBase:
     def __init__(self):
         self.jobs:List[JobBase] = []
         self.logger = get_logger()
+        self.status = AppStatus()
 
     def add_job(self, job: JobBase):
         self.jobs.append(job)