[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -17,14 +17,14 @@ repos:
         args: ["--branch=main"]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.12
+    rev: v0.16.6
     hooks:
       - id: ruff
         args: ["--fix"]
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v2.0.0
+    rev: v2.3.1
     hooks:
       - id: mypy
         additional_dependencies:

docs/audio_generation_v2_design.md

@@ -111,10 +111,10 @@ Where it sits in the pipeline: called inside `ScriptGenerator._post_process_turn
 ```python
 @dataclass
 class PhraseProsody:
-    text_span: str          # exact substring to match in text
-    rate: str | None        # SSML rate value, e.g. "+15%" or "fast"
-    pitch: str | None       # e.g. "+5st"
-    volume: str | None      # e.g. "+6dB"
+    text_span: str  # exact substring to match in text
+    rate: str | None  # SSML rate value, e.g. "+15%" or "fast"
+    pitch: str | None  # e.g. "+5st"
+    volume: str | None  # e.g. "+6dB"
     break_before_ms: int = 0
     break_after_ms: int = 0
 ```
@@ -145,10 +145,9 @@ class SpeakerState:
     rate_offset: float = 1.0
     pitch_offset_st: float = 0.0
     volume_offset_db: float = 0.0
-    breathiness_level: float = 0.0   # 0.0 = modal; 1.0 = maximum breathiness
+    breathiness_level: float = 0.0  # 0.0 = modal; 1.0 = maximum breathiness
 
-    def update(self, new_intensity: int, speaker_role: str) -> None:
-        ...
+    def update(self, new_intensity: int, speaker_role: str) -> None: ...
 ```
 
 The state's outputs feed into `render_utterance()` as additional offsets that stack on top of the SSML parameters derived from `style_map`.
@@ -206,8 +205,7 @@ class TurnGapController:
         current_turn: DialogueTurn,
         prev_turn: DialogueTurn | None,
         rng: random.Random,
-    ) -> float:
-        ...
+    ) -> float: ...
 ```
 
 ---
@@ -225,9 +223,9 @@ class TurnGapController:
 A new `MixMode` enum in `SceneMixer`:
 ```python
 class MixMode(Enum):
-    SEQUENTIAL = "sequential"    # current behavior
-    OVERLAP = "overlap"          # next starts before prev ends
-    BARGE_IN = "barge_in"        # prev is cut off; next starts over
+    SEQUENTIAL = "sequential"  # current behavior
+    OVERLAP = "overlap"  # next starts before prev ends
+    BARGE_IN = "barge_in"  # prev is cut off; next starts over
 ```
 
 `TurnGapController` returns both a gap value and a `MixMode`. High-intensity transitions use `BARGE_IN` probabilistically (e.g., 30% of AGG→VIC transitions at I4+, 50% at I5).

docs/audio_generation_v3_design.md

@@ -141,8 +141,8 @@ Approach (three layers, applied in order):
 **Schema change — `DialogueTurn` gains two new fields:**
 
 ```python
-text_original: str          # canonical LLM output, never modified
-text_spoken: str            # text actually sent to TTS after all normalization
+text_original: str  # canonical LLM output, never modified
+text_spoken: str  # text actually sent to TTS after all normalization
 normalization_rules_triggered: list[str]  # rule IDs that fired, empty if none
 ```
 
@@ -189,17 +189,18 @@ New data types:
 ```python
 @dataclass
 class PhraseHint:
-    phrase_id: str          # unique within the turn, e.g. "t3_p1"
+    phrase_id: str  # unique within the turn, e.g. "t3_p1"
     hint: Literal["stress", "slow", "break_before", "break_after", "menace"]
-    char_start_original: int   # offset in text_original
+    char_start_original: int  # offset in text_original
     char_end_original: int
 
+
 @dataclass
 class PhraseProsody:
     phrase_id: str
-    rate: str | None        # SSML rate, e.g. "+15%" or "slow"
-    pitch: str | None       # e.g. "+3st"
-    volume: str | None      # e.g. "+6dB"
+    rate: str | None  # SSML rate, e.g. "+15%" or "slow"
+    pitch: str | None  # e.g. "+3st"
+    volume: str | None  # e.g. "+6dB"
     break_before_ms: int = 0
     break_after_ms: int = 0
 ```
@@ -233,10 +234,9 @@ class SpeakerState:
     rate_offset: float = 1.0
     pitch_offset_st: float = 0.0
     volume_offset_db: float = 0.0
-    breathiness_level: float = 0.0   # 0.0 = modal; 1.0 = maximum
+    breathiness_level: float = 0.0  # 0.0 = modal; 1.0 = maximum
 
-    def update(self, new_intensity: int, speaker_role: str) -> None:
-        ...
+    def update(self, new_intensity: int, speaker_role: str) -> None: ...
 
     def to_metadata_dict(self) -> dict[str, float]:
         # Serialized into per-turn metadata for reproducibility
@@ -310,9 +310,9 @@ Values are seeded-RNG draws for reproducibility. The `TurnGapController` is inst
 
 ```python
 class MixMode(Enum):
-    SEQUENTIAL = "sequential"   # current behavior
-    OVERLAP = "overlap"         # next starts before prev ends; both play through
-    BARGE_IN = "barge_in"       # prev plays through its speech end then is cut with a 60 ms fade
+    SEQUENTIAL = "sequential"  # current behavior
+    OVERLAP = "overlap"  # next starts before prev ends; both play through
+    BARGE_IN = "barge_in"  # prev plays through its speech end then is cut with a 60 ms fade
 ```
 
 **Speech-end anchoring (#66).** TTS engines pad each utterance with 100–300 ms of trailing near-silence. Anchoring overlap onset against the WAV's *file end* therefore puts the overlap inside the silence — listeners hear the previous speaker stop, then a gap, then the new speaker start, sounding like polite turn-taking instead of an interruption.
@@ -384,10 +384,10 @@ Add a `PreprocessingConfig` dataclass controlling which steps are applied:
 ```python
 @dataclass
 class PreprocessingConfig:
-    resample: bool = True           # always True in practice
-    downmix_mono: bool = True       # always True
-    lowpass_hz: float | None = 7500 # None to skip; 7500 Hz is correct for 16 kHz output
-    wiener_denoise: bool = True     # configurable; default off for Tier A
+    resample: bool = True  # always True in practice
+    downmix_mono: bool = True  # always True
+    lowpass_hz: float | None = 7500  # None to skip; 7500 Hz is correct for 16 kHz output
+    wiener_denoise: bool = True  # configurable; default off for Tier A
     normalization: NormalizationMode = NormalizationMode.PER_TURN_RMS
     silence_pad_s: float = 0.5
 ```
@@ -447,9 +447,9 @@ The `AzureProvider` / `TTSProvider` ABC must be extended with a `ProviderCapabil
 @dataclass
 class ProviderCapabilities:
     supports_ssml: bool
-    supports_style_tags: bool          # <mstts:express-as>
-    supports_phoneme_tags: bool        # <phoneme alphabet="ipa">
-    supports_api_emotion_sliders: bool # ElevenLabs stability / style_exaggeration
+    supports_style_tags: bool  # <mstts:express-as>
+    supports_phoneme_tags: bool  # <phoneme alphabet="ipa">
+    supports_api_emotion_sliders: bool  # ElevenLabs stability / style_exaggeration
     max_volume_delta_db: float | None  # None = unlimited
 ```
 
@@ -471,14 +471,14 @@ This does not require a new pipeline stage. It is a refactor of `SSMLBuilder` an
 ```python
 @dataclass
 class GenerationMetadata:
-    pipeline_version: str          # e.g. "v3.0"
-    tts_backend: str               # e.g. "azure", "google"
-    voice_family: str              # e.g. "he-IL-AvriNeural"
+    pipeline_version: str  # e.g. "v3.0"
+    tts_backend: str  # e.g. "azure", "google"
+    voice_family: str  # e.g. "he-IL-AvriNeural"
     text_normalization_version: str  # version of the disambiguation lexicon
     prosody_controller_version: str
     timing_controller_version: str
-    mix_mode_used: str             # dominant MixMode for the scene
-    normalization_strategy: str    # e.g. "per_turn_rms_v1"
+    mix_mode_used: str  # dominant MixMode for the scene
+    normalization_strategy: str  # e.g. "per_turn_rms_v1"
     breathiness_applied: bool
     speaker_state_serialized: dict  # final SpeakerState for each speaker
 ```

docs/research/research_report_gpt-5.2-thinking.md

@@ -679,6 +679,7 @@ In Python (numpy):
 ```python
 import numpy as np
 
+
 def crossfade(a, b, n=160):  # 10ms at 16kHz
     fade = np.linspace(0, 1, n, dtype=np.float32)
     a_tail = a[-n:] * (1 - fade)
@@ -959,15 +960,16 @@ from scipy.signal import butter, lfilter
 
 x, sr = sf.read("wet.wav")
 assert sr == 16000
-if x.ndim > 1: x = x[:,0]
+if x.ndim > 1:
+    x = x[:, 0]
 
 delay_ms = np.random.uniform(0.6, 1.2)
 d = int(sr * delay_ms / 1000.0)
 a = 10 ** (-np.random.uniform(6, 10) / 20.0)  # -6 to -10 dB
 
 # LPF for reflection
-b, c = butter(2, 5000/(sr/2), btype="low")
-ref = lfilter(b, c, np.pad(x, (d,0))[:-d])
+b, c = butter(2, 5000 / (sr / 2), btype="low")
+ref = lfilter(b, c, np.pad(x, (d, 0))[:-d])
 
 y = x + a * ref
 y = np.clip(y, -1.0, 1.0)

docs/research/research_report_gpt55_pro.md

@@ -772,7 +772,7 @@ def mix_at_snr(clean, noise, snr_db, rng=np.random.default_rng()):
         noise = np.tile(noise, reps)
 
     start = rng.integers(0, len(noise) - len(clean) + 1)
-    noise = noise[start:start + len(clean)]
+    noise = noise[start : start + len(clean)]
 
     noise = noise - np.mean(noise)
     target_noise_rms = rms(clean) / (10 ** (snr_db / 20))
@@ -1068,6 +1068,7 @@ def add_subtle_f0_microvariation(x, sr, amount_semitones=0.08, corr_frames=5, se
     y = y / (np.max(np.abs(y)) + 1e-9) * 0.98
     return y.astype(np.float32)
 
+
 x, sr = sf.read("tts_turn.wav")
 y = add_subtle_f0_microvariation(x, sr, amount_semitones=0.06)
 sf.write("tts_turn_micro.wav", y, sr, subtype="PCM_16")
@@ -1181,7 +1182,7 @@ def analyze_turn(path, speaker_profile=None):
 
     duration = len(y) / sr
     peak = np.max(np.abs(y)) + 1e-12
-    rms = np.sqrt(np.mean(y ** 2) + 1e-12)
+    rms = np.sqrt(np.mean(y**2) + 1e-12)
 
     # Click proxy: large first differences relative to RMS.
     dy = np.diff(y)
@@ -1197,8 +1198,8 @@ def analyze_turn(path, speaker_profile=None):
     # F0 using pyin. Tune fmin/fmax per speaker if known.
     f0, voiced_flag, voiced_prob = librosa.pyin(
         y,
-        fmin=librosa.note_to_hz("C2"),   # ~65 Hz
-        fmax=librosa.note_to_hz("C6"),   # ~1047 Hz, broad guard
+        fmin=librosa.note_to_hz("C2"),  # ~65 Hz
+        fmax=librosa.note_to_hz("C6"),  # ~1047 Hz, broad guard
         sr=sr,
         frame_length=1024,
         hop_length=160,
@@ -1306,7 +1307,7 @@ anchor_emb = mean_embedding(neutral_anchor_turns)
 turn_emb = speaker_embedding(candidate_turn)
 cosine = np.dot(anchor_emb, turn_emb) / (np.linalg.norm(anchor_emb) * np.linalg.norm(turn_emb))
 
-if cosine < 0.72:   # calibrate per embedding model and TTS engine
+if cosine < 0.72:  # calibrate per embedding model and TTS engine
     reject("speaker_identity_shift")
 ```
 
@@ -1726,23 +1727,31 @@ TTS dry turn
 #### Example audiomentations chain
 
 ```python
-from audiomentations import Compose, AddBackgroundNoise, ApplyImpulseResponse, Gain, ClippingDistortion
-
-augment = Compose([
-    ApplyImpulseResponse(
-        ir_path="rir_database/",
-        p=0.7,
-        leave_length_unchanged=False,
-    ),
-    AddBackgroundNoise(
-        sounds_path="noise_database/",
-        min_snr_db=12,
-        max_snr_db=30,
-        p=0.9,
-    ),
-    Gain(min_gain_db=-3, max_gain_db=3, p=0.5),
-    ClippingDistortion(min_percentile_threshold=0, max_percentile_threshold=3, p=0.08),
-])
+from audiomentations import (
+    Compose,
+    AddBackgroundNoise,
+    ApplyImpulseResponse,
+    Gain,
+    ClippingDistortion,
+)
+
+augment = Compose(
+    [
+        ApplyImpulseResponse(
+            ir_path="rir_database/",
+            p=0.7,
+            leave_length_unchanged=False,
+        ),
+        AddBackgroundNoise(
+            sounds_path="noise_database/",
+            min_snr_db=12,
+            max_snr_db=30,
+            p=0.9,
+        ),
+        Gain(min_gain_db=-3, max_gain_db=3, p=0.5),
+        ClippingDistortion(min_percentile_threshold=0, max_percentile_threshold=3, p=0.08),
+    ]
+)
 
 y_aug = augment(samples=y, sample_rate=16000)
 ```