Add per-segment audio effects DSP preset selector to dub pipeline

backend/api/routers/dub_generate.py

@@ -13,7 +13,7 @@
 from core.tasks import task_manager
 from schemas.requests import DubRequest
 from services.model_manager import get_model, _gpu_pool
-from services.audio_dsp import apply_mastering, normalize_audio
+from services.audio_dsp import apply_mastering, normalize_audio, apply_effects_chain, get_effect_chain
 from services.rvc import apply_rvc, is_enabled as rvc_is_enabled
 from services.incremental import segment_fingerprint
 from services.watermark import embed_watermark
@@ -107,7 +107,7 @@ async def _stream(task_id):
                 sync_scores.append(1.0)
                 continue
 
-            def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
+            def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id, effect_preset):
                 ref_audio = None
                 ref_text = None
                 used_seed = None
@@ -160,15 +160,28 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                         speed=spd, denoise=True, postprocess_output=True,
                     )
                     audio_out = audios[0]
-                    mastered_audio = apply_mastering(audio_out, sample_rate=_model.sampling_rate if hasattr(_model, 'sampling_rate') else 24000)
+                    sr = _model.sampling_rate if hasattr(_model, 'sampling_rate') else 24000
+
+                    # Apply per-segment DSP effect preset (default: broadcast)
+                    seg_effect_preset = effect_preset or "broadcast"
+                    if seg_effect_preset == "raw":
+                        return audio_out
+
+                    mastered_audio = apply_mastering(audio_out, sample_rate=sr)
+                    effect_chain = get_effect_chain(seg_effect_preset)
+                    if effect_chain:
+                        mastered_audio = apply_effects_chain(
+                            mastered_audio,
+                            sample_rate=sr,
+                            chain=effect_chain,
+                        )
                     return normalize_audio(mastered_audio, target_dBFS=-2.0)
                 except Exception as e:
                     is_oom = (
                         isinstance(e, torch.cuda.OutOfMemoryError)
                         or "out of memory" in str(e).lower()
                         or "CUDA error" in str(e)
                     )
-                    # Always try to reclaim VRAM regardless of error type.
                     import gc
                     gc.collect()
                     if torch.cuda.is_available():
@@ -177,9 +190,8 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                         torch.mps.empty_cache()
 
                     if not is_oom:
-                        raise  # Non-OOM — propagate the real error, don't mask it.
+                        raise
 
-                    # OOM recovery: retry once with reduced steps (less VRAM).
                     retry_steps = min(nstep, 8)
                     logger.warning(
                         "OOM on segment (nstep=%d), retrying with %d steps after cache flush",
@@ -194,7 +206,20 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                             speed=spd, denoise=True, postprocess_output=True,
                         )
                         audio_out = audios[0]
-                        mastered_audio = apply_mastering(audio_out, sample_rate=_model.sampling_rate if hasattr(_model, 'sampling_rate') else 24000)
+                        sr = _model.sampling_rate if hasattr(_model, 'sampling_rate') else 24000
+
+                        seg_effect_preset = effect_preset or "broadcast"
+                        if seg_effect_preset == "raw":
+                            return audio_out
+
+                        mastered_audio = apply_mastering(audio_out, sample_rate=sr)
+                        effect_chain = get_effect_chain(seg_effect_preset)
+                        if effect_chain:
+                            mastered_audio = apply_effects_chain(
+                                mastered_audio,
+                                sample_rate=sr,
+                                chain=effect_chain,
+                            )
                         return normalize_audio(mastered_audio, target_dBFS=-2.0)
                     except Exception as retry_err:
                         raise RuntimeError(
@@ -203,13 +228,13 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                             f"Try the Flush button in the header to free VRAM, "
                             f"or switch to CPU in Settings. "
                             f"Underlying error: {retry_err}"
-                        )
+                        ) from retry_err
 
-            seg_instruct = seg.instruct or req.instruct
             seg_profile = seg.profile_id or None
             seg_speed = seg.speed if hasattr(seg, 'speed') and seg.speed is not None else req.speed
             seg_lang = seg.target_lang if getattr(seg, 'target_lang', None) else req.language
 
+            seg_instruct = seg.instruct or req.instruct
             # Phase 4.2 — if the segment carries a free-form direction, parse it
             # and append the taxonomy instruct (e.g. "urgent, surprised") on top
             # of whatever instruct was already set. Also apply the director's
@@ -239,10 +264,11 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                 # flag to restore num_step=req.num_step quality.
                 _num_step = 8 if req.preview else req.num_step
                 _t_tts_0 = time.perf_counter()
+                seg_effect_preset = getattr(seg, "effect_preset", None) or "broadcast"
                 audio_tensor = await loop.run_in_executor(
                     _gpu_pool, _gen,
                     seg.text, seg_lang, seg_instruct, seg_duration,
-                    _num_step, req.guidance_scale, seg_speed, seg_profile,
+                    _num_step, req.guidance_scale, seg_speed, seg_profile, seg_effect_preset,
                 )
                 _t_tts += time.perf_counter() - _t_tts_0
 
@@ -276,6 +302,7 @@ def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id=None):
                         "instruct": getattr(seg, "instruct", None),
                         "speed": getattr(seg, "speed", None),
                         "direction": getattr(seg, "direction", None),
+                        "effect_preset": getattr(seg, "effect_preset", None),
                     })
                 except Exception as e:
                     logger.debug("seg fingerprint skipped for %s: %s", seg_id, e)

backend/api/routers/engines.py

@@ -17,6 +17,8 @@
 
 from core import prefs
 from services import tts_backend, asr_backend, llm_backend, translation_engines
+from services.audio_dsp import list_effect_presets
+from api.schemas import EffectPresetsResponse
 
 router = APIRouter()
 
@@ -60,6 +62,16 @@ def list_llm_backends():
     return {"active": llm_backend.active_backend_id(), "backends": llm_backend.list_backends()}
 
 
+@router.get("/engines/effects/presets", response_model=EffectPresetsResponse)
+def list_effects_presets():
+    """Return available DSP effect presets for the dub pipeline.
+
+    Each preset is a named chain of audio effects (EQ, compressor, reverb, etc.)
+    that can be applied to generated TTS audio on a per-segment basis.
+    """
+    return {"presets": list_effect_presets()}
+
+
 @router.get("/engines/translation")
 def list_translation_engines():
     """Translation engines with per-engine pip-package availability.

backend/api/routers/generation.py

@@ -14,7 +14,7 @@
 from core.db import db_conn
 from core.config import OUTPUTS_DIR, VOICES_DIR
 from services.model_manager import get_model, _gpu_pool
-from services.audio_dsp import apply_mastering, normalize_audio
+# audio_dsp imported in _run_inference
 from core import event_bus
 
 router = APIRouter()
@@ -24,8 +24,9 @@ def _run_inference(
     model, text, language, ref_audio_path, ref_text, instruct, duration,
     num_step, guidance_scale, speed, t_shift, denoise,
     postprocess_output, layer_penalty_factor, position_temperature,
-    class_temperature, used_seed,
+    class_temperature, used_seed, effect_preset="broadcast",
 ):
+    from services.audio_dsp import apply_mastering, normalize_audio, apply_effects_chain, get_effect_chain
     import torch
     try:
         if used_seed is not None:
@@ -46,7 +47,30 @@ def _run_inference(
         )
         audio_out = audios[0]
 
-        mastered_audio = apply_mastering(audio_out, sample_rate=model.sampling_rate if hasattr(model, 'sampling_rate') else 24000)
+        sr = model.sampling_rate if hasattr(model, 'sampling_rate') else 24000
+
+        # Apply DSP effect preset
+        _effect_preset = effect_preset or "broadcast"
+
+        # Validate preset ID
+        from services.audio_dsp import EFFECT_PRESETS
+        if _effect_preset not in EFFECT_PRESETS:
+            raise ValueError(
+                f"Unknown effect preset: {_effect_preset!r}. "
+                f"Valid: {list(EFFECT_PRESETS.keys())}"
+            )
+
+        if _effect_preset == "raw":
+            # Raw: skip all DSP — return raw model output
+            return audio_out
+
+        mastered_audio = apply_mastering(audio_out, sample_rate=sr)
+        _chain = get_effect_chain(_effect_preset)
+        if _chain:
+            mastered_audio = apply_effects_chain(
+                mastered_audio, sample_rate=sr, chain=_chain,
+            )
+
         return normalize_audio(mastered_audio, target_dBFS=-2.0)
 
     except ValueError as e:
@@ -84,6 +108,7 @@ async def generate_speech(
     class_temperature: Optional[float] = Form(None),
     profile_id: Optional[str] = Form(None),
     seed: Optional[int] = Form(None),
+    effect_preset: str = Form("broadcast"),
 ):
     _model = await get_model()
 
@@ -137,7 +162,7 @@ async def generate_speech(
             _model, text, language, ref_audio_path, ref_text, instruct, duration,
             num_step, guidance_scale, speed, t_shift, denoise,
             postprocess_output, layer_penalty_factor, position_temperature,
-            class_temperature, used_seed,
+            class_temperature, used_seed, effect_preset,
         )
         gen_time = round(time.time() - start_time, 2)
 

backend/api/schemas.py

@@ -147,3 +147,20 @@ class ModelEntry(BaseModel):
     supported: bool = True
     size_on_disk: int | None = None
     nb_files: int | None = None
+
+
+# ── Effect presets ─────────────────────────────────────────────────────
+
+class EffectPresetEntry(BaseModel):
+    """One DSP effect preset."""
+    model_config = ConfigDict(extra="allow")
+
+    id: str
+    label: str
+    icon: str
+    description: str
+
+
+class EffectPresetsResponse(BaseModel):
+    """GET /engines/effects/presets"""
+    presets: list[EffectPresetEntry] = Field(default_factory=list)

backend/schemas/requests.py

@@ -1,6 +1,8 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, field_validator
 from typing import List, Optional
 
+from services.audio_dsp import EFFECT_PRESETS
+
 class ExportRequest(BaseModel):
     source_filename: str
     destination_path: str
@@ -23,6 +25,17 @@ class DubSegment(BaseModel):
     speed: Optional[float] = None
     gain: Optional[float] = None  # Per-segment volume (0.0 - 2.0, default 1.0)
     target_lang: Optional[str] = None  # Per-segment language override (ISO code)
+    effect_preset: str = "broadcast"   # NEW: DSP preset id (default: broadcast)
+
+    @field_validator("effect_preset")
+    @classmethod
+    def validate_effect_preset(cls, v: str) -> str:
+        if v not in EFFECT_PRESETS:
+            raise ValueError(
+                f"Unknown effect preset: {v!r}. "
+                f"Valid: {list(EFFECT_PRESETS.keys())}"
+            )
+        return v
 
 class DubRequest(BaseModel):
     segments: List[DubSegment]

backend/services/batched_tts.py

@@ -42,7 +42,7 @@ class SegmentSpec:
     __slots__ = (
         "index", "text", "language", "instruct", "speed", "duration",
         "num_step", "guidance_scale", "profile_id",
-        "ref_audio", "ref_text", "start", "end",
+        "ref_audio", "ref_text", "start", "end", "effect_preset",
     )
 
     def __init__(self, **kwargs):
@@ -93,7 +93,7 @@ async def generate_segments_batched(
         List of (segment_index, audio_tensor, sample_rate) tuples,
         ordered by segment_index.
     """
-    from services.audio_dsp import apply_mastering, normalize_audio
+    from services.audio_dsp import apply_mastering, normalize_audio, apply_effects_chain, get_effect_chain
 
     sr = getattr(model, "sampling_rate", 24000)
     loop = asyncio.get_running_loop()
@@ -144,7 +144,20 @@ def _gen_one(s=seg):
                     postprocess_output=True,
                 )
                 audio_out = audios[0]
+
+                # Apply per-segment DSP effect preset (default: broadcast)
+                seg_effect_preset = getattr(s, "effect_preset", None) or "broadcast"
+                if seg_effect_preset == "raw":
+                    # Raw: skip all DSP — return raw model output
+                    return audio_out
+
                 mastered = apply_mastering(audio_out, sample_rate=sr)
+                effect_chain = get_effect_chain(seg_effect_preset)
+                if effect_chain:
+                    mastered = apply_effects_chain(
+                        mastered, sample_rate=sr, chain=effect_chain
+                    )
+
                 return normalize_audio(mastered, target_dBFS=-2.0)
 
             audio = await loop.run_in_executor(gpu_pool, _gen_one)

backend/services/incremental.py

@@ -20,7 +20,7 @@
 import json
 
 
-_GEN_INPUT_FIELDS = ("text", "target_lang", "profile_id", "instruct", "speed", "direction")
+_GEN_INPUT_FIELDS = ("text", "target_lang", "profile_id", "instruct", "speed", "direction", "effect_preset")
 
 
 def segment_fingerprint(seg: dict) -> str:
@@ -29,6 +29,9 @@ def segment_fingerprint(seg: dict) -> str:
     Any change to `_GEN_INPUT_FIELDS` flips the hash and the segment becomes
     a re-gen candidate. Changes to position / selection state / lip-sync
     badge don't trigger regen, which is what we want.
+
+    Currently includes: text, target_lang, profile_id, instruct, speed,
+    direction, effect_preset.
     """
     payload = {k: (seg.get(k) if seg.get(k) is not None else "") for k in _GEN_INPUT_FIELDS}
     blob = json.dumps(payload, sort_keys=True, ensure_ascii=False)

frontend/src/api/engines.ts

@@ -82,3 +82,20 @@ export async function getJob(id: string): Promise<unknown> {
 export async function getJobEvents(id: string, afterSeq: number = 0): Promise<unknown> {
   return apiJson(`/jobs/${id}/events?after_seq=${afterSeq}`);
 }
+
+// ── Effect presets ──────────────────────────────────────────────────────
+
+export interface EffectPreset {
+  id: string;
+  label: string;
+  icon: string;
+  description: string;
+}
+
+export interface EffectPresetsResponse {
+  presets: EffectPreset[];
+}
+
+export async function fetchEffectPresets(): Promise<EffectPresetsResponse> {
+  return apiJson<EffectPresetsResponse>('/engines/effects/presets');
+}

frontend/src/api/types.ts

@@ -134,6 +134,18 @@ export interface DubHistoryResponse {
   jobs: DubJobMeta[];
 }
 
+export interface DubSegment {
+  start: number;
+  end: number;
+  text: string;
+  instruct?: string;
+  profile_id?: string;
+  speed?: number;
+  gain?: number;
+  target_lang?: string;
+  effect_preset?: string;
+}
+
 export interface DubTranslateResponse {
   segments: { id: string; text: string; text_original?: string; rate_ratio?: number; rate_error?: string }[];
 }