2203 lines
110 KiB
Python
2203 lines
110 KiB
Python
import os
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import re
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import json
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import struct
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import logging
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import time
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import asyncio
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import torch
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import torchaudio
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from fastapi import APIRouter, HTTPException
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from core.db import db_conn
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from core.config import DUB_DIR, VOICES_DIR, dub_seg_path
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from core.tasks import task_manager
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from schemas.requests import DubRequest
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from services.model_manager import _gpu_pool, run_on_gpu_pool_guarded
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from services.tts_backend import TTSBackend, resolve_generation_backend, active_backend_id
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from services.dub_batching import batch_timeout_s, native_batch_width
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from services import gpu_gateway
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from services.audio_dsp import apply_mastering, normalize_audio, apply_effects_chain, get_effect_chain
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from services.audio_io import atomic_save_wav, _safe_torchaudio_save
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from services.ffmpeg_utils import (
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find_ffmpeg,
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spawn_subprocess,
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# Moved to ffmpeg_utils so the Smart Fit export pipeline (Phase B) can
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# reuse them; re-imported here so `dub_generate._atempo_chain` /
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# `_pitch_preserving_stretch` keep working for existing importers.
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_atempo_chain,
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_pitch_preserving_stretch,
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)
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from services.rvc import apply_rvc, is_enabled as rvc_is_enabled
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from services.incremental import segment_fingerprint, fit_fingerprint
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from services.fit_planner import UNDERRUN_TOLERANCE, FitParams, plan_fit
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from services.watermark import mark_synthetic
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from services.speaker_clone import auto_profile_id
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from services.segment_bundle import extract_segment_wavs
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from api.routers.dub_core import _get_job, _save_job
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from omnivoice.utils.voice_design import heal_design_instruct
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logger = logging.getLogger("omnivoice.dub")
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# Maximum compression ratio we'll attempt with pitch-preserving stretch
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# before declaring "no way to fit cleanly" and falling back. atempo
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# remains intelligible up to ~1.5× then introduces audible WSOLA
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# artefacts; above ~1.8× speech becomes a fast garbled stream that no
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# DSP can rescue. The contributing-factor pipeline (CPS-aware slot-fit
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# in services/speech_rate.py, gap absorption below) keeps us under this
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# in practice — this is only a guard rail.
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MAX_STRETCH_RATIO = 1.8
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class _RemoteDubBackend:
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"""Sample-rate carrier while Dubbing runs without local TTS weights."""
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sample_rate = 24_000
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async def _resolve_dub_execution():
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"""Resolve routing without loading local weights for a remote dub."""
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engine_id = active_backend_id()
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decision = gpu_gateway.decide("dub_segments")
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if decision.remote:
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await gpu_gateway.preflight(engine_id, decision, operation="dub_segments")
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return engine_id, decision, _RemoteDubBackend()
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return engine_id, decision, await resolve_generation_backend(require_cloning=True)
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def _prepare_oom_retry(error: Exception, *, execution_target: str) -> bool:
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"""Prepare one *local* low-step retry after a genuine device OOM.
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The cache being flushed must belong to the device that raised the error.
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A remote worker owns its own recovery policy; flushing this process's CUDA
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cache after a remote failure both stalls the wrong GPU and can evict an
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unrelated local job. Keep this guard at the retry chokepoint so a future
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``dub_segments`` producer cannot accidentally inherit the old behaviour.
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Returns ``False`` for non-OOM errors. Remote OOMs are deliberately raised
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unchanged: the worker may classify/retry them, but this process must not.
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"""
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is_oom = (
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isinstance(error, torch.cuda.OutOfMemoryError)
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or "out of memory" in str(error).lower()
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or "CUDA error" in str(error)
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)
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if not is_oom:
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return False
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if execution_target != "local":
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raise error
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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torch.mps.empty_cache()
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return True
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def _cached_payload_intact(path: str, info) -> bool:
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"""Cheap truth check on a cached WAV whose header we are about to trust.
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The natural-rate fast path hands the mixer a PATH instead of decoded
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audio, so a cache whose header reads fine but whose payload is truncated
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would only fail later, during assembly — after the timing plan (Smart Fit,
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video stretch) had been computed from the header's frame count. The plan
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would then describe audio that no longer exists and the segment would be
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replaced by slot-length silence, leaving the persisted video plan and the
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rendered track disagreeing.
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Comparing the declared frame count against the physical ``data`` chunk
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catches that without decoding: a truncated file cannot hold the samples
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its header claims. Anything failing here falls through to the decoding path, which
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already degrades to a warning plus silence. Formats with no fixed
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bits-per-sample (compressed caches) are left to the decoder as before.
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"""
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try:
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bits = int(getattr(info, "bits_per_sample", 0) or 0)
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frames = int(getattr(info, "num_frames", 0) or 0)
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channels = int(getattr(info, "num_channels", 0) or 0)
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if bits <= 0 or frames <= 0 or channels <= 0:
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# Undecidable metadata fails CLOSED (review on #1620): these caches
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# are PCM WAVs this module wrote itself, so anything else is
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# unexpected — and the decode path this falls through to handles
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# every format the fast path would have.
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return False
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payload = frames * channels * (bits // 8)
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if payload <= 0:
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return False
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# A WAV may carry JUNK/LIST metadata before data, so its header is not
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# necessarily 44 bytes. Locate the data chunk instead of counting
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# metadata as audio; otherwise an extended header can mask truncation.
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file_size = os.path.getsize(path)
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with open(path, "rb") as wav:
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header = wav.read(12)
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if len(header) != 12 or header[:4] != b"RIFF" or header[8:12] != b"WAVE":
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return False
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offset = 12
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while offset + 8 <= file_size:
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wav.seek(offset)
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chunk_id = wav.read(4)
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chunk_size_raw = wav.read(4)
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if len(chunk_id) != 4 or len(chunk_size_raw) != 4:
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return False
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chunk_size = struct.unpack("<I", chunk_size_raw)[0]
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data_offset = offset + 8
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if chunk_id == b"data":
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return chunk_size >= payload and file_size >= data_offset + payload
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offset = data_offset + chunk_size + (chunk_size % 2)
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return False
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except Exception: # noqa: BLE001 — an unstattable cache is the decoder's problem
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return False
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def _underrun_min_rate() -> float:
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"""Floor for the underrun fill (audio slowed toward its slot, never below
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this rate). Default 0.85 stays natural-sounding; OMNIVOICE_UNDERRUN_MIN_RATE=1.0
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disables the fill. Clamped to atempo's per-stage sane range."""
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try:
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v = float(os.environ.get("OMNIVOICE_UNDERRUN_MIN_RATE", "0.85"))
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except ValueError:
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v = 0.85
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return min(1.0, max(0.5, v))
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# How far a too-long segment is allowed to bleed into the silent gap
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# before the next segment. Buys headroom on languages with higher
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# information density (Bengali, Hindi, Arabic…) without the audio
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# colliding with the next speaker's onset.
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GAP_OVERFLOW_MAX_S = 0.25
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GAP_OVERFLOW_BUFFER_S = 0.05
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def _sync_job_segments(job: dict, req: DubRequest) -> None:
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"""Persist the segments this dub was actually generated from back onto the job.
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The editor only sends the (translated / user-edited) segment text in the
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generate request; the job itself kept the original-language ASR transcript.
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SRT/VTT export and ffmpeg subtitle burn-in read `job["segments"]`, so they
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rendered the source language instead of the dub the user just heard (#309).
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Merge strategy: rebuild `job["segments"]` from the request, carrying over
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per-segment metadata (speaker_id, id, …) from the existing job segment
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matched by stable id (fallback: index). `text_original` always keeps the
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source-language text so dual-subtitle layouts can still stack it under the
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translation.
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"""
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if not req.segments:
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return
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existing = [s for s in (job.get("segments") or []) if isinstance(s, dict)]
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by_id = {str(s["id"]): s for s in existing if s.get("id") is not None}
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seg_ids = req.segment_ids or []
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merged: list[dict] = []
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for i, seg in enumerate(req.segments):
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seg_id = seg_ids[i] if i < len(seg_ids) else None
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prev = by_id.get(str(seg_id)) if seg_id is not None else None
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if prev is None and i < len(existing):
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prev = existing[i]
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row = dict(prev) if prev else {}
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if seg_id is not None:
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# The request id is authoritative — seg_order and the per-segment
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# WAV manifest are keyed by it.
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row["id"] = seg_id
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# Source-language text survives the overwrite so dual-subtitle export
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# keeps working; never let the translation clobber it.
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row["text_original"] = row.get("text_original") or row.get("text") or ""
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row["start"] = seg.start
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row["end"] = seg.end
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row["text"] = seg.text
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merged.append(row)
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job["segments"] = merged
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# P1.2 — per-language text, additively. `job["segments"]` stays the flat
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# single-slot map every existing consumer reads (last generated language);
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# `job["segments_i18n"]` preserves EACH generated track's text so
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# /dub/srt|vtt?lang= can emit that language instead of N identical files.
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# Shape: { langCode: { segKey: text } } where segKey is the segment's
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# stable id (str) or, for id-less legacy segments, its list index (str).
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# The whole per-language map is rebuilt on every generate of that language
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# (the request always carries the full segment list), so deleted segments
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# never linger. Jobs predating this field simply lack it — every reader
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# falls back to `job["segments"]`.
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lang = (req.language_code or "und").strip() or "und"
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i18n = job.setdefault("segments_i18n", {})
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i18n[lang] = {
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(str(row["id"]) if row.get("id") is not None else str(i)): row["text"]
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for i, row in enumerate(merged)
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}
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def _seg_hashes_by_lang(job: dict) -> dict:
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"""Per-language segment fingerprints: { langCode: { segId: hash } }.
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Additive migration (P1.3): jobs written by previous builds carry ONE flat
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`seg_hashes` map that was overwritten by whichever language generated
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last. That flat map can only describe the job's last-generated track, so
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it is attributed to `job["language_code"]` (which generate has always
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kept in lock-step with the last run). When even that is unknown the
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legacy hashes are dropped — segments then read as stale and regenerate
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cleanly, which is safer than guessing a language and splicing wrong-track
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audio. Note the legacy hashes also predate language-scoped fingerprints
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(see services.incremental.segment_fingerprint), so they compare stale
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once regardless — carrying them over just preserves the job shape.
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"""
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by_lang = job.get("seg_hashes_by_lang")
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if not isinstance(by_lang, dict):
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by_lang = {}
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legacy = job.get("seg_hashes")
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prev_lang = job.get("language_code")
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if isinstance(legacy, dict) and legacy and prev_lang:
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by_lang[prev_lang] = dict(legacy)
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job["seg_hashes_by_lang"] = by_lang
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return by_lang
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def _legacy_seg_cache_ok(job: dict, lang_code: str) -> bool:
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"""May this run reuse legacy un-keyed ``seg_<id>.wav`` files?
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Only when no OTHER language's audio could be sitting in them: the job has
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no dubbed track in a different language. Single-language jobs rendered by
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previous builds therefore keep their whole on-disk cache; the moment a
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job carries a second language the un-keyed files are ambiguous (they hold
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whichever language wrote them last) and must never be spliced into a
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track again — the P1.3 cross-contamination class.
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"""
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tracks = job.get("dubbed_tracks") or {}
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return not any(lc != lang_code for lc in tracks)
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# ── voice_match="consistent" resolution ─────────────────────────────────────
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# Owner report: "still 4 segments different in voice". Per-segment refs (Wave
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# 3.2) clone each line from a clip of its own source audio — best prosody
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# match, but the voice IDENTITY drifts line to line, and heuristic-diarized
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# jobs have no pooled speaker clones to anchor it. `voice_match="consistent"`
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# resolves every segment of a speaker to ONE reference: the per-speaker clone
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# when it exists, otherwise a deterministic pick among that speaker's own
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# per-segment clips.
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# Below ~3 s zero-shot prompt-priming gets unstable, so prefer clips at or
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# above it when choosing the one shared reference.
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CONSISTENT_MIN_REF_S = 3.0
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def _speaker_key_matches(speaker_id: str, key: str) -> bool:
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"""Same matching rule the `auto:` branch has always used: the safe-name
|
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slug first (`auto_profile_id`), the raw speaker id as fallback."""
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return auto_profile_id(speaker_id) == f"auto:{key}" or speaker_id == key
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|
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def _find_speaker_clone(clones: dict, key: str):
|
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for spk, info in (clones or {}).items():
|
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if _speaker_key_matches(spk, key):
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return info
|
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return None
|
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|
||
|
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def _seg_id_order(sid: str):
|
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"""Sort key for the tie-break: numeric suffix when there is one (so
|
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'seg_2' < 'seg_10'), plain string ordering otherwise. Deterministic for
|
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any id shape."""
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m = re.search(r"(\d+)$", sid)
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return (0, int(m.group(1)), sid) if m else (1, 0, sid)
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def _speaker_key_for_segment(job: dict, sid) -> str | None:
|
||
"""The `auto:`-style key of the speaker that owns segment `sid`, from the
|
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job's diarized segment rows. None when the segment is unknown (the caller
|
||
then keeps per-line behaviour for it — best effort, never a crash)."""
|
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for row in job.get("segments") or []:
|
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if isinstance(row, dict) and str(row.get("id", "")) == str(sid):
|
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spk = row.get("speaker_id") or "Speaker 1"
|
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return auto_profile_id(spk)[len("auto:"):]
|
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return None
|
||
|
||
|
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#: ``job_id -> {(segment, path)}`` already warned about, so a 300-segment dub
|
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#: whose clip directory was cleaned logs once per segment rather than once per
|
||
#: retry.
|
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#:
|
||
#: Keyed on the PATH as well as the segment, not the segment alone. The
|
||
#: single-segment preview endpoint has no segment identity to pass — it is a
|
||
#: "render this text" call — so every preview shared one key and only the first
|
||
#: missing reference in a job was ever reported, silencing the rest (greptile).
|
||
#: A distinct path is distinct information wherever it comes from, and on the
|
||
#: render path this also means a segment whose binding was changed to a second
|
||
#: missing clip is not mistaken for the one already reported.
|
||
_MISSING_REF_WARNED: dict = {}
|
||
|
||
|
||
def warn_if_ref_missing(ref_audio, *, job_id: str = "", seg_id="", where: str = "dub"):
|
||
"""Say so when a clone reference points at a file that is gone (#1331).
|
||
|
||
Reported as: re-dubbing a single sentence loses the cloned voice, while
|
||
re-dubbing everything keeps it.
|
||
|
||
Clone references are FILE PATHS into the job's extracted-clip directory,
|
||
and the whole job dict — those paths included — is persisted to
|
||
``dub_history.job_data`` so saved projects reopen after a restart. The job
|
||
therefore outlives its clips. Reopen a saved dub once the clip directory
|
||
has been cleaned, regenerate one line, and every resolution branch hands
|
||
the engine a path that is no longer there. An engine given a missing
|
||
reference renders *uncloned* rather than failing, so the line comes back in
|
||
a default voice matching nothing else in the dub, with no error anywhere.
|
||
Re-running the full dub re-extracts the clips — which is exactly why that
|
||
appears to fix it, and is the workaround the reporter found unaided.
|
||
|
||
Deliberately DIAGNOSTIC ONLY: ``ref_audio`` is returned unchanged. Nulling
|
||
it would not change what the user hears (the engine already falls back),
|
||
and it would decide on the engine's behalf that a reference it cannot
|
||
``stat`` is unusable — untrue for anything resolved inside a sidecar's own
|
||
namespace. The defect here is the silence, not the fallback.
|
||
"""
|
||
if not ref_audio:
|
||
return ref_audio
|
||
try:
|
||
if os.path.exists(ref_audio):
|
||
return ref_audio
|
||
except OSError: # unreadable path (permissions, dead mount) — same symptom
|
||
pass
|
||
seen = _MISSING_REF_WARNED.setdefault(str(job_id), set())
|
||
key = (str(seg_id), str(ref_audio))
|
||
if key not in seen:
|
||
seen.add(key)
|
||
logger.warning(
|
||
"%s: the voice reference for segment %s is gone (%s), so this line "
|
||
"will most likely render in a DEFAULT voice instead of the cloned "
|
||
"one, with no error of its own. Clone clips are extracted per job "
|
||
"and the job outlives them, so a saved dub regenerated after "
|
||
"cleanup loses them — re-running the full dub re-extracts the "
|
||
"clips, which is why that appears to fix it (#1331).",
|
||
where, seg_id, ref_audio,
|
||
)
|
||
return ref_audio
|
||
|
||
|
||
def forget_missing_ref_warnings(job_id: str) -> None:
|
||
"""Drop the once-per-segment warning memo for a job (a fresh render should
|
||
warn again if the clips are still gone)."""
|
||
_MISSING_REF_WARNED.pop(str(job_id), None)
|
||
|
||
|
||
def resolve_consistent_ref(job: dict, speaker_key: str, memo: dict | None = None):
|
||
"""ONE clone reference for every segment of `speaker_key`.
|
||
|
||
Preference order:
|
||
1. the pooled per-speaker clone (job["speaker_clones"]) — same lookup
|
||
the per-line path uses as its fallback;
|
||
2. no speaker clone (heuristic diarization skips extraction entirely —
|
||
the key case): a deterministic pick among that speaker's per-segment
|
||
clips: longest clip ≥3 s, tie-break lowest segment id. Clips all
|
||
shorter than 3 s degrade to "longest overall", same tie-break.
|
||
|
||
Returns the clone info dict ({"ref_audio", "ref_text", ...}) or None.
|
||
Pure function of the job dict; `memo` (keyed by speaker_key) just avoids
|
||
rescanning per segment — the pick is deterministic with or without it.
|
||
"""
|
||
if memo is not None and speaker_key in memo:
|
||
return memo[speaker_key]
|
||
|
||
ref = _find_speaker_clone(job.get("speaker_clones") or {}, speaker_key)
|
||
if ref is None:
|
||
seg_clones = job.get("segment_clones") or {}
|
||
candidates = []
|
||
for row in job.get("segments") or []:
|
||
if not isinstance(row, dict):
|
||
continue
|
||
spk = row.get("speaker_id") or "Speaker 1"
|
||
if not _speaker_key_matches(spk, speaker_key):
|
||
continue
|
||
sid = str(row.get("id", ""))
|
||
info = seg_clones.get(sid)
|
||
if info and info.get("ref_audio"):
|
||
candidates.append((sid, info))
|
||
if candidates:
|
||
usable = [
|
||
c for c in candidates
|
||
if float(c[1].get("duration") or 0.0) >= CONSISTENT_MIN_REF_S
|
||
] or candidates
|
||
usable.sort(
|
||
key=lambda c: (-float(c[1].get("duration") or 0.0), _seg_id_order(c[0]))
|
||
)
|
||
ref = usable[0][1]
|
||
|
||
if memo is not None:
|
||
memo[speaker_key] = ref
|
||
return ref
|
||
|
||
|
||
def _remote_voice(job: dict, profile_id: str | None, seg_id, voice_match: str,
|
||
memo: dict) -> tuple[str | None, str | None, bool, str | None, int | None]:
|
||
"""Resolve a dub binding without touching the TTS model."""
|
||
ref_audio = ref_text = instruct = None
|
||
seed = None
|
||
single_use = False
|
||
if profile_id and profile_id.startswith("auto-seg:"):
|
||
sid = profile_id[len("auto-seg:"):]
|
||
info = (job.get("segment_clones") or {}).get(sid)
|
||
shared = False
|
||
if voice_match == "consistent" and sid == str(seg_id):
|
||
key = _speaker_key_for_segment(job, sid)
|
||
alternate = resolve_consistent_ref(job, key, memo) if key else None
|
||
if alternate:
|
||
info = alternate
|
||
shared = True
|
||
if info:
|
||
ref_audio, ref_text = info.get("ref_audio"), info.get("ref_text")
|
||
single_use = not shared
|
||
elif profile_id and profile_id.startswith("auto:"):
|
||
key = profile_id[len("auto:"):]
|
||
if voice_match == "consistent":
|
||
info = resolve_consistent_ref(job, key, memo)
|
||
else:
|
||
info = ((job.get("segment_clones") or {}).get(str(seg_id))
|
||
or _find_speaker_clone(job.get("speaker_clones") or {}, key))
|
||
single_use = str(seg_id) in (job.get("segment_clones") or {})
|
||
if info:
|
||
ref_audio, ref_text = info.get("ref_audio"), info.get("ref_text")
|
||
elif profile_id:
|
||
with db_conn() as conn:
|
||
row = conn.execute("SELECT * FROM voice_profiles WHERE id=?", (profile_id,)).fetchone()
|
||
if row:
|
||
seed = row["seed"]
|
||
if row["is_locked"] and row["locked_audio_path"]:
|
||
ref_audio = os.path.join(VOICES_DIR, row["locked_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
elif row["instruct"] and not row["is_locked"]:
|
||
try:
|
||
vd_states = row["vd_states"]
|
||
except (KeyError, IndexError):
|
||
vd_states = None
|
||
instruct = heal_design_instruct(row["instruct"], vd_states)
|
||
else:
|
||
ref_audio = os.path.join(VOICES_DIR, row["ref_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
return ref_audio, ref_text, single_use, instruct, seed
|
||
|
||
|
||
def _decode_remote_dub(result: gpu_gateway.RemoteResult) -> dict[int, str]:
|
||
"""Extract the worker bundle into a task-scoped directory, path-safely."""
|
||
target = os.path.join(DUB_DIR, ".remote", result.task_id)
|
||
try:
|
||
return extract_segment_wavs(result.path or "", target)
|
||
except ValueError as exc:
|
||
# Preserve the established route-specific error wording consumed by
|
||
# diagnostics and regression tests.
|
||
raise ValueError(str(exc).replace("segment artifact", "dub artifact")) from exc
|
||
|
||
|
||
router = APIRouter()
|
||
|
||
@router.post("/dub/generate/{job_id}")
|
||
async def dub_generate(job_id: str, req: DubRequest):
|
||
"""Adds a dub generation job to the async batch task pool."""
|
||
job = _get_job(job_id)
|
||
if not job:
|
||
raise HTTPException(
|
||
status_code=404,
|
||
detail="This dub session has expired or was never created. Re-upload the video to start a new one.",
|
||
)
|
||
|
||
# ── Engine resolution (issue #312 class) ────────────────────────────────
|
||
# Every rendered segment clones either source speech or a saved profile, so
|
||
# local execution still requires a cloning-capable engine. Remote execution
|
||
# validates the selected worker here without loading duplicate local weights;
|
||
# its local backend is prepared only if gateway fallback actually selects it.
|
||
try:
|
||
engine_id, decision, backend = await _resolve_dub_execution()
|
||
except gpu_gateway.ModelNotDownloaded as e:
|
||
raise HTTPException(
|
||
status_code=409,
|
||
detail={
|
||
"error": "model_not_downloaded",
|
||
"message": str(e),
|
||
"engine": e.engine,
|
||
"repo_ids": e.repo_ids,
|
||
"target": e.target,
|
||
"target_label": e.target_label,
|
||
"downloadable": e.downloadable,
|
||
},
|
||
) from e
|
||
except ValueError as e:
|
||
raise HTTPException(status_code=400, detail=str(e))
|
||
except Exception as e:
|
||
from core.failure import is_gpu_oom
|
||
|
||
if not is_gpu_oom(e):
|
||
raise
|
||
from core.public_errors import public_exception_response
|
||
|
||
payload = public_exception_response(
|
||
e,
|
||
fallback="The TTS model could not be loaded.",
|
||
)
|
||
raise HTTPException(status_code=503, detail=payload["detail"]) from e
|
||
|
||
# Resolve the global profile once for this job. Explicit Production
|
||
# overrides remain authoritative, while ordinary Dubbing now follows the
|
||
# same Fast/Balanced/Quality/Max contract as Clone and long-form work.
|
||
from services.performance_profiles import tts_defaults
|
||
_profile_defaults = tts_defaults(engine_id)
|
||
_job_num_step = req.num_step if req.num_step is not None else _profile_defaults.get("num_step", 16)
|
||
_job_postprocess = _profile_defaults.get("postprocess_output", True)
|
||
|
||
async def _stream(task_id):
|
||
total = len(req.segments)
|
||
all_segment_wavs = []
|
||
sync_scores = []
|
||
|
||
# Track language for this run. Everything per-track — the per-segment
|
||
# WAV cache, fingerprints, seg_wav_kind — is keyed by it (P1.3) so a
|
||
# multi-language job's tracks can't cross-contaminate.
|
||
lang_code = req.language_code or "und"
|
||
|
||
def _seg_lang_path(seg_key) -> str:
|
||
# Per-language per-segment WAV: seg_{lang}_{id}.wav. Built through
|
||
# dub_seg_path so the sanitisation + DUB_DIR containment guard
|
||
# apply to the combined key. Legacy un-keyed seg_{id}.wav files
|
||
# remain readable via the gated fallback (_legacy_seg_cache_ok).
|
||
return dub_seg_path(job_id, f"{lang_code}_{seg_key}")
|
||
|
||
# Throttle the device cache flush. empty_cache() is a synchronous
|
||
# device stall, so calling it every segment (as the old code did)
|
||
# serialised the GPU loop; the batched-I/O design it replaced kept
|
||
# it off the hot path on purpose. Flush every ~16 releases instead —
|
||
# frequent enough to bound VRAM, rare enough to stay invisible.
|
||
_RELEASE_FLUSH_EVERY = 16
|
||
_release_count = {"n": 0}
|
||
|
||
def _release_audio_tensors(*objs) -> None:
|
||
"""Best-effort VRAM cleanup after a segment is safely on disk.
|
||
|
||
Tensors are freed by the callers' own ``del`` once they fall out
|
||
of scope; this only throttles the device cache flush. ``*objs`` is
|
||
kept for call-site compatibility but intentionally unused — a local
|
||
``del`` here would only unbind the parameter, never the caller's
|
||
reference.
|
||
"""
|
||
_release_count["n"] += 1
|
||
if _release_count["n"] % _RELEASE_FLUSH_EVERY != 0:
|
||
return
|
||
try:
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||
torch.mps.empty_cache()
|
||
except Exception:
|
||
pass
|
||
|
||
# mix_<id> scratch WAVs written for silence/cached-fail/error slots are
|
||
# pure assembly inputs (no preview/regen contract), so they're deleted
|
||
# once the final track is written.
|
||
_mix_temp_paths: list[str] = []
|
||
|
||
def _store_mix_wav(start: float, end: float, wav: torch.Tensor, sr: int, seg_key: str):
|
||
"""Write one segment to disk and keep only its path in the mix manifest.
|
||
|
||
A zero/negative-length buffer is never written (``atomic_save_wav``
|
||
raises on empty audio); instead a harmless zero-length in-memory
|
||
entry is returned, which the assembly tolerates via its ``e > s``
|
||
guard.
|
||
"""
|
||
if wav.shape[-1] <= 0:
|
||
return (start, end, torch.zeros(1, 0), sr)
|
||
path = dub_seg_path(job_id, seg_key)
|
||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||
atomic_save_wav(path, wav.detach().cpu(), sr)
|
||
if seg_key.startswith("mix_"):
|
||
_mix_temp_paths.append(path)
|
||
_release_audio_tensors(wav)
|
||
return (start, end, path, sr)
|
||
|
||
def _entry_num_samples(entry) -> int:
|
||
# Zero/negative-duration slots are kept as in-memory tensors (never
|
||
# written to disk); report their length directly.
|
||
if isinstance(entry[2], torch.Tensor):
|
||
return int(entry[2].shape[-1])
|
||
try:
|
||
info = torchaudio.info(entry[2])
|
||
return int(info.num_frames)
|
||
except Exception:
|
||
wav, _sr = torchaudio.load(entry[2])
|
||
n = int(wav.shape[-1])
|
||
_release_audio_tensors(wav)
|
||
return n
|
||
|
||
def _load_entry_wav(entry, target_sr: int) -> torch.Tensor:
|
||
if isinstance(entry[2], torch.Tensor):
|
||
return entry[2]
|
||
wav, loaded_sr = torchaudio.load(entry[2])
|
||
if loaded_sr != target_sr:
|
||
import torchaudio.functional as AF
|
||
wav = AF.resample(wav, loaded_sr, target_sr)
|
||
return wav
|
||
|
||
def _write_memmap_wav_atomic(target_path: str, samples, sample_rate: int) -> None:
|
||
"""Write a mono float32 memmap to int16 WAV without loading it all.
|
||
|
||
Intentionally does NOT watermark: the final track is assembled from
|
||
per-segment WAVs that were already watermarked once at synthesis
|
||
time (see the seg-write path below), exactly as ``main`` does.
|
||
Re-marking here would double-mark every segment in the final mix.
|
||
"""
|
||
import tempfile
|
||
import wave
|
||
import numpy as np
|
||
|
||
target_dir = os.path.dirname(target_path) or "."
|
||
target_base = os.path.basename(target_path)
|
||
fd, tmp_path = tempfile.mkstemp(
|
||
prefix=f".{target_base}.",
|
||
suffix=".wav",
|
||
dir=target_dir,
|
||
)
|
||
os.close(fd)
|
||
chunk_samples = max(sample_rate * 30, 1)
|
||
try:
|
||
with wave.open(tmp_path, "wb") as wf:
|
||
wf.setnchannels(1)
|
||
wf.setsampwidth(2)
|
||
wf.setframerate(sample_rate)
|
||
total_len = int(samples.shape[0])
|
||
for off in range(0, total_len, chunk_samples):
|
||
chunk = np.array(samples[off: off + chunk_samples], dtype=np.float32, copy=True)
|
||
if chunk.size == 0:
|
||
continue
|
||
np.nan_to_num(chunk, copy=False, nan=0.0, posinf=1.0, neginf=-1.0)
|
||
chunk = np.clip(chunk, -1.0, 1.0)
|
||
pcm = (chunk * 32767.0).astype("<i2", copy=False)
|
||
wf.writeframes(pcm.tobytes())
|
||
os.replace(tmp_path, target_path)
|
||
except BaseException:
|
||
try:
|
||
os.unlink(tmp_path)
|
||
except OSError:
|
||
pass
|
||
raise
|
||
|
||
# Phase 4.1 — partial regen. If `regen_only` is set, we only run TTS
|
||
# on segments whose id is in that set; the others reuse their existing
|
||
# `seg_i.wav` on disk and slot into the final mix unchanged.
|
||
regen_only = set(req.regen_only or []) if req.regen_only is not None else None
|
||
seg_ids = req.segment_ids or []
|
||
strategy = (req.timing_strategy or "concise").lower()
|
||
# Voice-identity mode (see DubRequest.voice_match). The memo makes the
|
||
# "consistent" pick once per speaker and hands the SAME reference to
|
||
# every segment of that speaker for the whole run.
|
||
voice_match = (req.voice_match or "per_line").lower()
|
||
_consistent_ref_memo: dict = {}
|
||
remote_audio: dict[int, str] = {}
|
||
# Strategy-transition guard: concise, stretch_video and smart_fit all
|
||
# re-mix *natural-rate* per-segment WAVs. If the previous run used
|
||
# strict_slot, the on-disk WAVs are slot-squeezed ("slotted") — the
|
||
# missing tails cannot be recovered by a re-mix. Force one full regen;
|
||
# afterwards partial regen / fit-only re-mix (regen_only=[]) is safe.
|
||
# Jobs predating this field have unknown kind → also regen once.
|
||
# P1.3: the kind is per-track now (each language renders under its own
|
||
# strategy); the flat job["seg_wav_kind"] is only consulted for jobs
|
||
# written before the per-language map existed — once the map is
|
||
# present, a language without an entry has unknown-kind WAVs (or none
|
||
# at all) and must regen once, exactly like the pre-field case.
|
||
_kind_map = job.get("seg_wav_kind_by_lang")
|
||
_wav_kind = (
|
||
_kind_map.get(lang_code) if isinstance(_kind_map, dict) else job.get("seg_wav_kind")
|
||
)
|
||
if strategy != "strict_slot" and regen_only is not None and _wav_kind != "natural":
|
||
regen_only = None
|
||
# Manifest: stable segment id per current index. Per-segment WAVs are
|
||
# named by stable id (dub_seg_path) so regen reuses the right audio after
|
||
# reorder; index-keyed readers (preview/export) resolve via this manifest.
|
||
job["seg_order"] = [seg_ids[k] if k < len(seg_ids) else f"seg_{k}" for k in range(len(req.segments))]
|
||
|
||
# Per-segment metadata to persist after the hot loop. Audio itself is
|
||
# written immediately and only file paths are kept, so long videos don't
|
||
# retain every generated tensor in RAM until final assembly.
|
||
_pending_seg_writes: list[tuple] = []
|
||
|
||
# Calibration records for the pre-synthesis duration planner
|
||
# (services/duration_planner.py): text length + the NATURAL-rate TTS
|
||
# duration of every freshly synthesized segment. Only meaningful for
|
||
# the natural-rate strategies — strict_slot forces the audio to the
|
||
# slot length, which would poison the observed chars-per-second.
|
||
_natural_dur_records: dict[str, dict] = {}
|
||
|
||
# Phase 4.1 bench instrumentation: measure where incremental time goes.
|
||
# Only prints when regen_only is active (real-user incremental path).
|
||
_t_start = time.perf_counter()
|
||
_t_cache = 0.0
|
||
_t_tts = 0.0
|
||
_batched_audio: dict[int, torch.Tensor] = {}
|
||
_profile_row_cache: dict[str, object | None] = {}
|
||
_has_native_batch = (
|
||
getattr(type(backend), "generate_batch", TTSBackend.generate_batch)
|
||
is not TTSBackend.generate_batch
|
||
)
|
||
_native_batch_width = native_batch_width(backend) if _has_native_batch else 1
|
||
|
||
async def _prepare_local_dub():
|
||
"""Load local TTS only when gateway fallback actually needs it."""
|
||
nonlocal backend, _has_native_batch, _native_batch_width
|
||
if isinstance(backend, _RemoteDubBackend):
|
||
backend = await resolve_generation_backend(require_cloning=True)
|
||
_has_native_batch = (
|
||
getattr(type(backend), "generate_batch", TTSBackend.generate_batch)
|
||
is not TTSBackend.generate_batch
|
||
)
|
||
_native_batch_width = (
|
||
native_batch_width(backend) if _has_native_batch else 1
|
||
)
|
||
return gpu_gateway.LocalCall(fn=lambda: {})
|
||
|
||
def _segment_generation_args(index, segment) -> dict:
|
||
"""Resolve the per-row controls shared by serial and native batches."""
|
||
current_id = seg_ids[index] if index < len(seg_ids) else f"seg_{index}"
|
||
duration = segment.end - segment.start
|
||
profile_id = segment.profile_id or None
|
||
speed = segment.speed if segment.speed is not None else req.speed
|
||
language = segment.target_lang or req.language
|
||
instruct = segment.instruct or req.instruct
|
||
direction_text = getattr(segment, "direction", None)
|
||
if direction_text and direction_text.strip():
|
||
try:
|
||
from services.director import parse as _parse_direction
|
||
|
||
direction = _parse_direction(direction_text)
|
||
extra = direction.instruct_prompt()
|
||
if extra:
|
||
instruct = f"{instruct}, {extra}" if instruct else extra
|
||
bias = direction.rate_bias()
|
||
if (
|
||
bias
|
||
and abs(bias - 1.0) > 0.01
|
||
and strategy == "strict_slot"
|
||
):
|
||
speed = (speed or 1.0) * bias
|
||
except Exception as error:
|
||
logger.debug("direction parse skipped for %s: %s", current_id, error)
|
||
return {
|
||
"seg_id": current_id,
|
||
"text": segment.text,
|
||
"language": language,
|
||
"instruct": instruct,
|
||
"duration": duration if strategy == "strict_slot" else None,
|
||
"num_step": 8 if req.preview else _job_num_step,
|
||
"guidance_scale": req.guidance_scale,
|
||
"speed": speed,
|
||
"profile_id": profile_id,
|
||
"effect_preset": getattr(segment, "effect_preset", None) or "broadcast",
|
||
}
|
||
|
||
# One coarse remote lease for every segment that actually needs fresh
|
||
# synthesis. Assembly, fitting and the separately-pooled RVC pass stay
|
||
# here; the worker returns a single verified bundle of segment WAVs.
|
||
if decision.remote:
|
||
remote_rows: list[dict] = []
|
||
remote_refs: list[str | None] = []
|
||
for i, seg in enumerate(req.segments):
|
||
seg_id = seg_ids[i] if i < len(seg_ids) else f"seg_{i}"
|
||
if (regen_only is not None and seg_id not in regen_only) or not seg.text.strip():
|
||
continue
|
||
ref_audio, ref_text, ref_single_use, profile_instruct, seed = _remote_voice(
|
||
job, seg.profile_id or None, seg_id, voice_match, _consistent_ref_memo
|
||
)
|
||
ref_audio = warn_if_ref_missing(
|
||
ref_audio, job_id=job_id, seg_id=seg_id, where="remote dub render"
|
||
)
|
||
seg_instruct = seg.instruct or req.instruct or profile_instruct
|
||
seg_speed = seg.speed if seg.speed is not None else req.speed
|
||
if seg.direction and seg.direction.strip():
|
||
try:
|
||
from services.director import parse as _parse_direction
|
||
direction = _parse_direction(seg.direction)
|
||
extra = direction.instruct_prompt()
|
||
if extra:
|
||
seg_instruct = f"{seg_instruct}, {extra}" if seg_instruct else extra
|
||
bias = direction.rate_bias()
|
||
if bias and abs(bias - 1.0) > 0.01 and strategy == "strict_slot":
|
||
seg_speed = (seg_speed or 1.0) * bias
|
||
except Exception:
|
||
logger.debug("direction parse skipped for remote segment %s", seg_id,
|
||
exc_info=True)
|
||
remote_rows.append({
|
||
"index": i, "text": seg.text,
|
||
"language": seg.target_lang or req.language,
|
||
"ref_text": ref_text, "ref_single_use": ref_single_use,
|
||
"instruct": seg_instruct,
|
||
"duration": (seg.end - seg.start) if strategy == "strict_slot" else None,
|
||
"num_step": 8 if req.preview else _job_num_step,
|
||
"postprocess_output": _job_postprocess,
|
||
"guidance_scale": req.guidance_scale, "speed": seg_speed,
|
||
"effect_preset": seg.effect_preset or "broadcast",
|
||
"seed": seed,
|
||
# RVC changes the waveform locally after TTS, so that path
|
||
# is marked at the existing post-RVC chokepoint below.
|
||
"watermark": not rvc_is_enabled(),
|
||
})
|
||
remote_refs.append(ref_audio)
|
||
if remote_rows:
|
||
states: asyncio.Queue = asyncio.Queue()
|
||
call = gpu_gateway.RemoteCall(
|
||
engine=engine_id, operation="dub_segments",
|
||
params={"segments": remote_rows, "ref_audio": remote_refs},
|
||
decode=_decode_remote_dub,
|
||
)
|
||
dub_run = gpu_gateway.JobRun("dub_segments")
|
||
run = asyncio.create_task(gpu_gateway.run(
|
||
"dub_segments", local=gpu_gateway.LocalCall(prepare=_prepare_local_dub),
|
||
remote=call, decision=decision, job=dub_run,
|
||
on_state=states.put_nowait,
|
||
))
|
||
while not run.done():
|
||
if task_manager.is_cancelled(task_id):
|
||
run.cancel()
|
||
try:
|
||
await run
|
||
except asyncio.CancelledError:
|
||
pass
|
||
yield f"data: {json.dumps({'type': 'cancelled', 'segments_processed': 0})}\n\n"
|
||
return
|
||
try:
|
||
state = await asyncio.wait_for(states.get(), timeout=0.25)
|
||
except asyncio.TimeoutError:
|
||
continue
|
||
fraction = float(state.get("progress") or 0.0)
|
||
yield f"data: {json.dumps({'type': 'progress', 'current': round(fraction * total, 2), 'total': total, 'text': state.get('stage') or state.get('phase')})}\n\n"
|
||
try:
|
||
remote_audio = await run
|
||
except Exception as error:
|
||
from core.public_errors import stream_generation_failure
|
||
|
||
detail = stream_generation_failure(error)["detail"]
|
||
yield f"data: {json.dumps({'type': 'error', 'error': detail})}\n\n"
|
||
return
|
||
notice = dub_run.notice()
|
||
if notice is not None:
|
||
yield f"data: {json.dumps({'type': 'routing_notice', 'status': notice[0], 'reason': notice[1]})}\n\n"
|
||
|
||
if remote_audio and isinstance(backend, _RemoteDubBackend):
|
||
first_remote = next(iter(remote_audio.values()))
|
||
backend.sample_rate = int(torchaudio.info(first_remote).sample_rate)
|
||
elif isinstance(backend, _RemoteDubBackend):
|
||
# Fit-only / cache-only reruns synthesize nothing. Keep the cached
|
||
# track's native rate when one exists instead of resampling it to
|
||
# the carrier's conservative 24 kHz default.
|
||
for cached_id in seg_ids:
|
||
cached_path = _seg_lang_path(cached_id)
|
||
if os.path.exists(cached_path):
|
||
try:
|
||
backend.sample_rate = int(torchaudio.info(cached_path).sample_rate)
|
||
break
|
||
except Exception:
|
||
continue
|
||
|
||
for i, seg in enumerate(req.segments):
|
||
seg_id = seg_ids[i] if i < len(seg_ids) else f"seg_{i}"
|
||
|
||
# Check abort flag before each segment
|
||
if task_manager.is_cancelled(task_id):
|
||
yield f"data: {json.dumps({'type': 'cancelled', 'segments_processed': i})}\n\n"
|
||
return
|
||
|
||
if not remote_audio:
|
||
yield f"data: {json.dumps({'type': 'progress', 'current': i, 'total': total, 'text': seg.text[:50]})}\n\n"
|
||
|
||
seg_duration = seg.end - seg.start
|
||
if seg_duration <= 0.05 or not seg.text.strip():
|
||
sr = backend.sample_rate
|
||
# max(0, …): a zero/negative-duration slot must not feed a
|
||
# negative length to torch.zeros (raises) — _store_mix_wav
|
||
# turns the empty buffer into a harmless in-memory entry.
|
||
silence = torch.zeros(1, max(0, int(seg_duration * sr)))
|
||
all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, silence, sr, f"mix_{seg_id}"))
|
||
try:
|
||
del silence
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
sync_scores.append(1.0)
|
||
continue
|
||
|
||
# Partial regen: if this segment isn't in the allow-list, reuse its
|
||
# previously-rendered WAV so the final mix still covers the timeline.
|
||
if regen_only is not None and seg_id not in regen_only:
|
||
# This track's own cache first (seg_{lang}_{id}.wav). Legacy
|
||
# un-keyed files (seg_{id}.wav / seg_{index}.wav) are reused
|
||
# ONLY when no other-language track exists on the job — a
|
||
# multi-track job's un-keyed files hold whichever language
|
||
# rendered last, and splicing them here was exactly how
|
||
# "Regen N changed" mixed language B into track A (P1.3).
|
||
seg_wav_path = _seg_lang_path(seg_id)
|
||
if not os.path.exists(seg_wav_path) and _legacy_seg_cache_ok(job, lang_code):
|
||
for _legacy_key in (seg_id, i):
|
||
_legacy = dub_seg_path(job_id, _legacy_key)
|
||
if os.path.exists(_legacy):
|
||
seg_wav_path = _legacy
|
||
break
|
||
if os.path.exists(seg_wav_path):
|
||
try:
|
||
_t_cache_0 = time.perf_counter()
|
||
# Natural-rate caches are already the exact assembly
|
||
# input. Keep the durable path in the manifest so the
|
||
# mixer decodes it once; the old path decoded here,
|
||
# wrote an identical mix_<id> scratch WAV, then decoded
|
||
# that copy again. Header-only inspection preserves
|
||
# the resample fallback for caches made by an engine
|
||
# with a different sample rate.
|
||
if strategy != "strict_slot":
|
||
try:
|
||
cached_info = torchaudio.info(seg_wav_path)
|
||
except Exception:
|
||
cached_info = None
|
||
if (
|
||
cached_info is not None
|
||
and int(cached_info.sample_rate) == int(backend.sample_rate)
|
||
and _cached_payload_intact(seg_wav_path, cached_info)
|
||
):
|
||
all_segment_wavs.append(
|
||
(seg.start, seg.end, seg_wav_path, backend.sample_rate)
|
||
)
|
||
sync_scores.append(getattr(seg, 'sync_ratio', None) or 1.0)
|
||
_t_cache += time.perf_counter() - _t_cache_0
|
||
continue
|
||
|
||
cached_wav, cached_sr = torchaudio.load(seg_wav_path)
|
||
if cached_sr != backend.sample_rate:
|
||
import torchaudio.functional as AF
|
||
cached_wav = AF.resample(cached_wav, cached_sr, backend.sample_rate)
|
||
# strict_slot persists slot-sized buffers. Every other
|
||
# strategy consumes natural-rate audio and lets the mix
|
||
# loop fit it to the current timeline.
|
||
if strategy == "strict_slot":
|
||
target_samples = int(seg_duration * backend.sample_rate)
|
||
current_samples = cached_wav.shape[-1]
|
||
if target_samples > current_samples:
|
||
cached_wav = torch.nn.functional.pad(cached_wav, (0, target_samples - current_samples))
|
||
elif current_samples > target_samples:
|
||
cached_wav = cached_wav[..., :target_samples]
|
||
all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, cached_wav, backend.sample_rate, f"mix_{seg_id}"))
|
||
try:
|
||
del cached_wav
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
sync_scores.append(getattr(seg, 'sync_ratio', None) or 1.0)
|
||
_t_cache += time.perf_counter() - _t_cache_0
|
||
continue
|
||
except Exception as e:
|
||
# Fall through to a silent placeholder if the cached WAV
|
||
# is broken — cleaner than aborting the whole mix.
|
||
yield f"data: {json.dumps({'type': 'warning', 'segment': i, 'message': f'cached seg lost, padding silence: {str(e)[:120]}'})}\n\n"
|
||
sr = backend.sample_rate
|
||
silence = torch.zeros(1, max(0, int(seg_duration * sr)))
|
||
all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, silence, sr, f"mix_{seg_id}"))
|
||
try:
|
||
del silence
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
sync_scores.append(1.0)
|
||
continue
|
||
|
||
def _gen(text, lang, instruct_str, dur_s, nstep, cfg, spd, profile_id, effect_preset,
|
||
*, execution_target="local", prepare_only=False, current_seg_id=None):
|
||
# Normalize once at the segment's text→engine choke point
|
||
# (covers the OOM-retry generate below too, which reuses this
|
||
# closure's `text`). Pref-gated, idempotent, never raises.
|
||
from services.text_normalization import normalize_for_tts
|
||
text = normalize_for_tts(text, lang)
|
||
|
||
effective_seg_id = seg_id if current_seg_id is None else current_seg_id
|
||
ref_audio = None
|
||
ref_text = None
|
||
used_seed = None
|
||
# Per-segment refs are a distinct file per segment, each used
|
||
# exactly once in this render — telling the prompt cache to
|
||
# store them would evict the per-speaker / locked-profile
|
||
# prompts that every OTHER segment reuses (LRU of 8 vs
|
||
# potentially hundreds of segment clips). cache_ref=False =
|
||
# "encode it, don't let it displace anything".
|
||
ref_single_use = False
|
||
|
||
# Auto-clones extracted from the source video during prepare
|
||
# (see services/speaker_clone.py) live at job["speaker_clones"]
|
||
# keyed by speaker_id. We use the `auto:` prefix so they can't
|
||
# collide with persistent voice_profiles.id values.
|
||
# Wave 3.2: a per-segment clone ref (cut from this line's own
|
||
# source audio) takes precedence over the per-speaker clone.
|
||
if profile_id and profile_id.startswith("auto-seg:"):
|
||
sid = profile_id[len("auto-seg:"):]
|
||
info = (job.get("segment_clones") or {}).get(sid)
|
||
# voice_match="consistent": an auto-seg binding to the
|
||
# segment's OWN id is the server default from prepare —
|
||
# heuristic diarization skips speaker-clone extraction, so
|
||
# every long line gets `auto-seg:{its own id}` (see
|
||
# dub_core's assignment loop). That's not a user choice
|
||
# (the Voice dropdown can't even render auto-seg ids), so
|
||
# swap it for the speaker's ONE consistent reference. A
|
||
# CROSS binding (sid != this segment) can only come from an
|
||
# explicit request — honour its clip unchanged.
|
||
_consistent_alt = None
|
||
if voice_match == "consistent" and sid == str(effective_seg_id):
|
||
_spk_key = _speaker_key_for_segment(job, sid)
|
||
if _spk_key:
|
||
_consistent_alt = resolve_consistent_ref(
|
||
job, _spk_key, _consistent_ref_memo
|
||
)
|
||
if _consistent_alt:
|
||
ref_audio = _consistent_alt.get("ref_audio")
|
||
ref_text = _consistent_alt.get("ref_text")
|
||
# Shared by every segment of the speaker → multi-use;
|
||
# keep it warm in the prompt cache (#1132 semantics).
|
||
elif info:
|
||
ref_audio = info.get("ref_audio")
|
||
ref_text = info.get("ref_text")
|
||
ref_single_use = True
|
||
profile_id = None # prevent the voice_profiles lookup below
|
||
|
||
elif profile_id and profile_id.startswith("auto:"):
|
||
key = profile_id[len("auto:"):]
|
||
if voice_match == "consistent":
|
||
# ONE reference per speaker for the whole dub: the
|
||
# pooled per-speaker clone, else the deterministic
|
||
# segment-clip pick (heuristic-diarized jobs have no
|
||
# speaker_clones at all — the key case). Multi-use by
|
||
# construction → ref_single_use stays False so the
|
||
# prompt cache keeps it warm across segments (#1132).
|
||
auto = resolve_consistent_ref(job, key, _consistent_ref_memo)
|
||
if auto:
|
||
ref_audio = auto.get("ref_audio")
|
||
ref_text = auto.get("ref_text")
|
||
else:
|
||
# per_line (DEFAULT) — #486: an `auto:{speaker}`
|
||
# binding still prefers THIS segment's own per-segment
|
||
# ref when one exists (cut from this line's source
|
||
# audio → matches its prosody), falling back to the
|
||
# per-speaker clone otherwise. This keeps the Wave 3.2
|
||
# per-segment-ref quality win while letting every
|
||
# segment carry the UI-visible `auto:` id the dub
|
||
# editor's Voice dropdown can actually render ("From
|
||
# Video → Speaker N"). `seg_id` is closed over from
|
||
# the per-segment loop below.
|
||
segment_speaker_key = _speaker_key_for_segment(
|
||
job, effective_seg_id
|
||
)
|
||
# Legacy jobs may not persist diarized segment rows.
|
||
# Preserve their established per-line preference; only
|
||
# suppress it when current metadata proves the user
|
||
# explicitly selected a different speaker.
|
||
selected_is_segment_speaker = (
|
||
segment_speaker_key is None or segment_speaker_key == key
|
||
)
|
||
seg_ref = (
|
||
(job.get("segment_clones") or {}).get(str(effective_seg_id))
|
||
if selected_is_segment_speaker
|
||
else None
|
||
)
|
||
if seg_ref:
|
||
ref_audio = seg_ref.get("ref_audio")
|
||
ref_text = seg_ref.get("ref_text")
|
||
ref_single_use = True
|
||
else:
|
||
auto = _find_speaker_clone(
|
||
job.get("speaker_clones") or {}, key
|
||
)
|
||
if auto is None:
|
||
# Short lines may have no line-specific clip.
|
||
# Reuse this speaker's best source instead of
|
||
# silently reverting to the engine default.
|
||
auto = resolve_consistent_ref(
|
||
job, key, _consistent_ref_memo
|
||
)
|
||
if auto:
|
||
ref_audio = auto.get("ref_audio")
|
||
ref_text = auto.get("ref_text")
|
||
profile_id = None # prevent the voice_profiles lookup below
|
||
|
||
if profile_id:
|
||
if profile_id not in _profile_row_cache:
|
||
with db_conn() as conn:
|
||
_profile_row_cache[profile_id] = conn.execute(
|
||
"SELECT * FROM voice_profiles WHERE id=?",
|
||
(profile_id,),
|
||
).fetchone()
|
||
row = _profile_row_cache[profile_id]
|
||
if row:
|
||
if row["is_locked"] and row["locked_audio_path"]:
|
||
ref_audio = os.path.join(VOICES_DIR, row["locked_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
used_seed = row["seed"]
|
||
elif row["instruct"] and not row["is_locked"]:
|
||
used_seed = row["seed"]
|
||
else:
|
||
ref_audio = os.path.join(VOICES_DIR, row["ref_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
used_seed = row["seed"]
|
||
|
||
if not instruct_str:
|
||
try:
|
||
_vd = row["vd_states"]
|
||
except (KeyError, IndexError):
|
||
_vd = None
|
||
instruct_str = heal_design_instruct(row["instruct"], _vd)
|
||
|
||
if used_seed is not None and not prepare_only:
|
||
torch.manual_seed(used_seed)
|
||
|
||
# Last gate before the engine: every resolution branch above
|
||
# produces a PATH, and none of them can know it still exists.
|
||
ref_audio = warn_if_ref_missing(
|
||
ref_audio, job_id=job_id, seg_id=effective_seg_id, where="dub render",
|
||
)
|
||
|
||
if prepare_only:
|
||
return {
|
||
"text": text,
|
||
"language": lang if lang != "Auto" else None,
|
||
"ref_audio": ref_audio,
|
||
"ref_text": ref_text,
|
||
"cache_ref": not ref_single_use,
|
||
"instruct": instruct_str if instruct_str else None,
|
||
"duration": dur_s,
|
||
"num_step": nstep,
|
||
"guidance_scale": cfg,
|
||
"speed": spd,
|
||
"denoise": True,
|
||
"postprocess_output": _job_postprocess,
|
||
"effect_preset": effect_preset or "broadcast",
|
||
"seed": used_seed,
|
||
}
|
||
|
||
try:
|
||
audio_out = backend.generate(
|
||
text=text, language=lang if lang != "Auto" else None,
|
||
ref_audio=ref_audio, ref_text=ref_text,
|
||
cache_ref=not ref_single_use,
|
||
instruct=instruct_str if instruct_str else None,
|
||
duration=dur_s, num_step=nstep, guidance_scale=cfg,
|
||
speed=spd, denoise=True, postprocess_output=_job_postprocess,
|
||
)
|
||
sr = backend.sample_rate
|
||
|
||
# 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 = audio_out
|
||
if not getattr(backend, "applies_own_mastering", False):
|
||
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:
|
||
if not _prepare_oom_retry(e, execution_target=execution_target):
|
||
raise
|
||
|
||
retry_steps = min(nstep, 8)
|
||
logger.warning(
|
||
"OOM on segment (nstep=%d), retrying with %d steps after cache flush",
|
||
nstep, retry_steps,
|
||
)
|
||
try:
|
||
# An OOM retry on a single-use ref pays the reference
|
||
# encode a second time (~0.4s) — deliberate: caching it
|
||
# would reintroduce the eviction this flag exists to
|
||
# prevent, to optimize a path that only runs after an
|
||
# OOM already cost seconds.
|
||
audio_out = backend.generate(
|
||
text=text, language=lang if lang != "Auto" else None,
|
||
ref_audio=ref_audio, ref_text=ref_text,
|
||
cache_ref=not ref_single_use,
|
||
instruct=instruct_str if instruct_str else None,
|
||
duration=dur_s, num_step=retry_steps, guidance_scale=cfg,
|
||
speed=spd, denoise=True, postprocess_output=_job_postprocess,
|
||
)
|
||
sr = backend.sample_rate
|
||
|
||
seg_effect_preset = effect_preset or "broadcast"
|
||
if seg_effect_preset == "raw":
|
||
return audio_out
|
||
|
||
mastered_audio = audio_out
|
||
if not getattr(backend, "applies_own_mastering", False):
|
||
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(
|
||
f"Ran out of GPU memory generating this segment. "
|
||
f"Retried with {retry_steps} steps but still failed. "
|
||
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
|
||
|
||
async def _prefetch_native_batch(first_index: int) -> None:
|
||
"""Render one bounded batch and retain only its small output window."""
|
||
if _native_batch_width < 2 or remote_audio:
|
||
return
|
||
batch: list[tuple[int, dict]] = []
|
||
compatibility = None
|
||
for candidate_index in range(first_index, len(req.segments)):
|
||
candidate = req.segments[candidate_index]
|
||
candidate_id = (
|
||
seg_ids[candidate_index]
|
||
if candidate_index < len(seg_ids)
|
||
else f"seg_{candidate_index}"
|
||
)
|
||
if (
|
||
candidate_index in _batched_audio
|
||
or candidate.end - candidate.start <= 0.05
|
||
or not candidate.text.strip()
|
||
or (
|
||
regen_only is not None
|
||
and candidate_id not in regen_only
|
||
)
|
||
):
|
||
continue
|
||
args = _segment_generation_args(candidate_index, candidate)
|
||
try:
|
||
prepared = _gen(
|
||
args["text"],
|
||
args["language"],
|
||
args["instruct"],
|
||
args["duration"],
|
||
args["num_step"],
|
||
args["guidance_scale"],
|
||
args["speed"],
|
||
args["profile_id"],
|
||
args["effect_preset"],
|
||
prepare_only=True,
|
||
current_seg_id=args["seg_id"],
|
||
)
|
||
except Exception:
|
||
if candidate_index == first_index:
|
||
raise
|
||
break
|
||
# Fixed-seed profiles deliberately keep their established
|
||
# one-row deterministic RNG contract.
|
||
if prepared["seed"] is not None:
|
||
if candidate_index == first_index:
|
||
return
|
||
break
|
||
candidate_compatibility = (
|
||
prepared["cache_ref"],
|
||
bool(prepared["ref_audio"]),
|
||
prepared["num_step"],
|
||
prepared["guidance_scale"],
|
||
prepared["postprocess_output"],
|
||
)
|
||
if compatibility is None:
|
||
compatibility = candidate_compatibility
|
||
elif candidate_compatibility != compatibility:
|
||
break
|
||
batch.append((candidate_index, prepared))
|
||
if len(batch) >= _native_batch_width:
|
||
break
|
||
if len(batch) < 2:
|
||
return
|
||
|
||
def _render_batch() -> list[torch.Tensor]:
|
||
prepared_rows = [prepared for _, prepared in batch]
|
||
outputs = backend.generate_batch(
|
||
[prepared["text"] for prepared in prepared_rows],
|
||
language=[prepared["language"] for prepared in prepared_rows],
|
||
ref_audio=[prepared["ref_audio"] for prepared in prepared_rows],
|
||
ref_text=[prepared["ref_text"] for prepared in prepared_rows],
|
||
cache_ref=prepared_rows[0]["cache_ref"],
|
||
instruct=[prepared["instruct"] for prepared in prepared_rows],
|
||
duration=[prepared["duration"] for prepared in prepared_rows],
|
||
num_step=prepared_rows[0]["num_step"],
|
||
guidance_scale=prepared_rows[0]["guidance_scale"],
|
||
speed=[prepared["speed"] for prepared in prepared_rows],
|
||
denoise=True,
|
||
postprocess_output=prepared_rows[0]["postprocess_output"],
|
||
)
|
||
if len(outputs) != len(prepared_rows):
|
||
raise RuntimeError(
|
||
f"native batch returned {len(outputs)} outputs for "
|
||
f"{len(prepared_rows)} segments"
|
||
)
|
||
rendered = []
|
||
for output, prepared in zip(outputs, prepared_rows):
|
||
preset = prepared["effect_preset"]
|
||
if preset == "raw":
|
||
rendered.append(output)
|
||
continue
|
||
mastered = output
|
||
if not getattr(backend, "applies_own_mastering", False):
|
||
mastered = apply_mastering(mastered, sample_rate=backend.sample_rate)
|
||
effect_chain = get_effect_chain(preset)
|
||
if effect_chain:
|
||
mastered = apply_effects_chain(
|
||
mastered,
|
||
sample_rate=backend.sample_rate,
|
||
chain=effect_chain,
|
||
)
|
||
rendered.append(normalize_audio(mastered, target_dBFS=-2.0))
|
||
return rendered
|
||
|
||
try:
|
||
outputs = await run_on_gpu_pool_guarded(
|
||
_render_batch,
|
||
what="Dub generate batch",
|
||
timeout=batch_timeout_s(
|
||
[prepared["text"] for _, prepared in batch], backend
|
||
),
|
||
)
|
||
except TimeoutError:
|
||
raise
|
||
except Exception as error:
|
||
_prepare_oom_retry(error, execution_target="local")
|
||
logger.warning(
|
||
"Native dub batch failed for segments %s-%s; falling back: %s",
|
||
batch[0][0] + 1,
|
||
batch[-1][0] + 1,
|
||
error,
|
||
)
|
||
return
|
||
_batched_audio.update(
|
||
(index, output) for (index, _), output in zip(batch, outputs)
|
||
)
|
||
|
||
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
|
||
# speed bias so "urgent" actually sounds a bit quicker.
|
||
seg_direction = getattr(seg, 'direction', None)
|
||
if seg_direction and seg_direction.strip():
|
||
try:
|
||
from services.director import parse as _parse_direction
|
||
d = _parse_direction(seg_direction)
|
||
extra_instruct = d.instruct_prompt()
|
||
if extra_instruct:
|
||
seg_instruct = (
|
||
f"{seg_instruct}, {extra_instruct}" if seg_instruct else extra_instruct
|
||
)
|
||
bias = d.rate_bias()
|
||
if bias and abs(bias - 1.0) > 0.01:
|
||
# Speed-bias from a Direction only multiplies seg_speed
|
||
# in strict_slot mode, which is the legacy path that
|
||
# compresses audio at synthesis time to fit the slot.
|
||
# In concise / stretch_video modes we preserve natural
|
||
# rate so the user gets the "urgent" or "slow" voice
|
||
# the director asked for without the chipmunk side-
|
||
# effect that overshooting the slot would otherwise
|
||
# cause.
|
||
if (req.timing_strategy or "concise") == "strict_slot":
|
||
seg_speed = (seg_speed or 1.0) * bias
|
||
except Exception as e:
|
||
logger.debug("direction parse skipped for %s: %s", getattr(seg, 'id', '?'), e)
|
||
|
||
loop = asyncio.get_running_loop()
|
||
try:
|
||
# Fast-preview mode for interactive edits — trade ~10–20 %
|
||
# quality for ~2× speed by dropping flow-matching steps.
|
||
# Client sends `preview=true` when the user is iterating;
|
||
# before final export the client should re-call without the
|
||
# flag to restore the explicit override or shared profile.
|
||
_num_step = 8 if req.preview else _job_num_step
|
||
_t_tts_0 = time.perf_counter()
|
||
seg_effect_preset = getattr(seg, "effect_preset", None) or "broadcast"
|
||
|
||
# In concise / stretch_video / smart_fit modes we pass
|
||
# dur_s=None so the TTS model speaks at its natural rate for
|
||
# this text length — the whole point of the new timing
|
||
# strategies is to never squeeze the speech to fit at
|
||
# synthesis time. strict_slot keeps the legacy behaviour
|
||
# where dur_s is the slot hint.
|
||
_dur_for_tts = seg_duration if strategy == "strict_slot" else None
|
||
|
||
# Bounded + pool-reset on hang so a wedged dub segment can't
|
||
# starve the GPU pool and brick the backend (#730 class).
|
||
# Budget from the shared length-scaled helper (#1190): a long
|
||
# dub segment used to die on the flat 300s even after v0.3.22.
|
||
from services.model_manager import generate_timeout_s
|
||
if i in remote_audio:
|
||
audio_tensor, remote_sr = torchaudio.load(remote_audio[i])
|
||
try:
|
||
os.unlink(remote_audio[i])
|
||
except OSError:
|
||
pass
|
||
if remote_sr != backend.sample_rate:
|
||
import torchaudio.functional as AF
|
||
audio_tensor = AF.resample(audio_tensor, remote_sr, backend.sample_rate)
|
||
else:
|
||
if i not in _batched_audio:
|
||
await _prefetch_native_batch(i)
|
||
if i in _batched_audio:
|
||
audio_tensor = _batched_audio.pop(i)
|
||
else:
|
||
audio_tensor = await run_on_gpu_pool_guarded(
|
||
lambda: _gen(
|
||
seg.text, seg_lang, seg_instruct, _dur_for_tts,
|
||
_num_step, req.guidance_scale, seg_speed, seg_profile, seg_effect_preset,
|
||
),
|
||
what="Dub generate",
|
||
timeout=generate_timeout_s(seg.text, engine=backend),
|
||
)
|
||
_t_tts += time.perf_counter() - _t_tts_0
|
||
|
||
# Check abort immediately after GPU work completes
|
||
if task_manager.is_cancelled(task_id):
|
||
yield f"data: {json.dumps({'type': 'cancelled', 'segments_processed': i + 1})}\n\n"
|
||
return
|
||
|
||
target_samples = int(seg_duration * backend.sample_rate)
|
||
current_samples = audio_tensor.shape[-1]
|
||
# Capture the real spoken duration before strict-slot padding
|
||
# or trimming. This is the evidence used by Agent timing and
|
||
# keeps sync badges truthful for every timing strategy.
|
||
natural_generated_dur = current_samples / backend.sample_rate
|
||
|
||
if strategy == "strict_slot":
|
||
# Legacy: pad short audio + trim long audio so the mix
|
||
# loop receives slot-sized buffers. The atempo squeeze
|
||
# in the mix loop never fires here because we already
|
||
# forced size = target_samples.
|
||
if target_samples > current_samples:
|
||
pad_amount = target_samples - current_samples
|
||
audio_tensor = torch.nn.functional.pad(audio_tensor, (0, pad_amount))
|
||
elif current_samples > target_samples:
|
||
audio_tensor = audio_tensor[..., :target_samples]
|
||
# concise / stretch_video / smart_fit: keep audio at its
|
||
# natural length. The mix loop decides per-mode whether to
|
||
# trim, slip, stretch the video, or split audio/video
|
||
# retiming (smart_fit) to accommodate it.
|
||
|
||
generated_dur = natural_generated_dur
|
||
sync_ratio = round(generated_dur / max(seg_duration, 0.01), 3)
|
||
|
||
sync_scores.append(sync_ratio)
|
||
|
||
# Duration-planner calibration sample: this text length spoke
|
||
# for this long at natural rate. Keyed by stable seg id and
|
||
# merged into the per-language job map after the loop.
|
||
if strategy != "strict_slot" and seg.text.strip() and generated_dur > 0:
|
||
_natural_dur_records[str(seg_id)] = {
|
||
"chars": len(seg.text.strip()),
|
||
"dur": round(generated_dur, 4),
|
||
}
|
||
|
||
# Build the fingerprint now (cheap) but defer the disk write
|
||
# and job flush to the batch-write phase after the GPU loop.
|
||
_seg_fp = None
|
||
try:
|
||
# track_lang scopes the hash to THIS track (P1.3); the
|
||
# client-side recompute (/tools/incremental) sends the
|
||
# same code, so parity (#281 class) holds per language.
|
||
_seg_fp = segment_fingerprint({
|
||
"text": seg.text,
|
||
"target_lang": getattr(seg, "target_lang", None),
|
||
"profile_id": getattr(seg, "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),
|
||
}, track_lang=lang_code, voice_match=voice_match)
|
||
except Exception as e:
|
||
logger.debug("seg fingerprint skipped for %s: %s", seg_id, e)
|
||
|
||
_pending_seg_writes.append((i, backend.sample_rate, seg_id, _seg_fp, _num_step))
|
||
|
||
# RVC needs the WAV on disk, so write it immediately only
|
||
# when RVC is active (uncommon path).
|
||
if rvc_is_enabled():
|
||
seg_wav_path = _seg_lang_path(seg_id)
|
||
atomic_save_wav(seg_wav_path, audio_tensor, backend.sample_rate)
|
||
try:
|
||
await loop.run_in_executor(_gpu_pool, apply_rvc, seg_wav_path)
|
||
rvc_wav, rvc_sr = torchaudio.load(seg_wav_path)
|
||
if rvc_sr == backend.sample_rate:
|
||
audio_tensor = rvc_wav
|
||
|
||
if strategy == "strict_slot":
|
||
target_samples = int(seg_duration * backend.sample_rate)
|
||
current_samples = audio_tensor.shape[-1]
|
||
if target_samples > current_samples:
|
||
audio_tensor = torch.nn.functional.pad(
|
||
audio_tensor, (0, target_samples - current_samples)
|
||
)
|
||
elif current_samples > target_samples:
|
||
audio_tensor = audio_tensor[..., :target_samples]
|
||
except Exception as e:
|
||
yield f"data: {json.dumps({'type': 'warning', 'segment': i, 'message': f'RVC skipped: {str(e)[:120]}'})}\n\n"
|
||
|
||
# Watermark this FRESH TTS output exactly once, right before it
|
||
# is persisted. The same seg_{lang}_{id}.wav is BOTH the
|
||
# downloadable per-segment file AND the assembly input for the
|
||
# final track, so marking it here (and nowhere else) gives the
|
||
# downloadable WAV its mark back and the final mix inherits it —
|
||
# no double-mark. Cached-reuse audio is already marked;
|
||
# silence/zero slots carry no speech to mark, so neither is
|
||
# re-watermarked.
|
||
if i not in remote_audio or rvc_is_enabled():
|
||
audio_tensor = mark_synthetic(audio_tensor, backend.sample_rate,
|
||
context="dub_generate.segment")
|
||
|
||
seg_wav_path = _seg_lang_path(seg_id)
|
||
try:
|
||
# Keep the existing per-segment WAV contract for previews
|
||
# and partial regeneration, but do not keep the tensor in RAM.
|
||
atomic_save_wav(seg_wav_path, audio_tensor, backend.sample_rate)
|
||
except Exception as e:
|
||
logger.warning("seg write failed for %s: %s", seg_id, e)
|
||
# If the durable segment write fails, still preserve a mix
|
||
# copy so this generation can finish.
|
||
all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, audio_tensor, backend.sample_rate, f"mix_{seg_id}"))
|
||
try:
|
||
del audio_tensor
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
else:
|
||
all_segment_wavs.append((seg.start, seg.end, seg_wav_path, backend.sample_rate))
|
||
try:
|
||
del audio_tensor
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
except Exception as e:
|
||
# A task-stream error bypasses the global exception handler.
|
||
# Never publish engine exception text here: allocator errors
|
||
# carry process tables and arbitrary failures can carry paths,
|
||
# tokens, or source text. The shared helper enriches recognized
|
||
# classes using VoiceStudio-owned constants only.
|
||
from core.public_errors import stream_generation_failure
|
||
|
||
error_detail = stream_generation_failure(e)["detail"]
|
||
yield f"data: {json.dumps({'type': 'error', 'segment': i, 'error': error_detail})}\n\n"
|
||
sr = backend.sample_rate
|
||
all_segment_wavs.append(_store_mix_wav(seg.start, seg.end, torch.zeros(1, max(0, int(seg_duration * sr))), sr, f"mix_{seg_id}"))
|
||
sync_scores.append(1.0)
|
||
|
||
_t_loop_end = time.perf_counter()
|
||
|
||
yield f"data: {json.dumps({'type': 'assembling'})}\n\n"
|
||
|
||
# ── Batch metadata phase ──────────────────────────────────────
|
||
# Per-segment WAVs were written during the loop to keep RAM bounded.
|
||
# Flush only lightweight fingerprints/quality metadata here.
|
||
_t_diskw_0 = time.perf_counter()
|
||
# P1.3 — fingerprints live per language so each track's staleness is
|
||
# judged against ITS OWN last generate. The flat job["seg_hashes"] is
|
||
# kept as a mirror of the CURRENT track's map: every existing consumer
|
||
# (the `done` event, dub-history restore, older frontends) already
|
||
# treats it as "the hashes of the language generated last", which is
|
||
# exactly what it now provably contains.
|
||
hashes = _seg_hashes_by_lang(job).setdefault(lang_code, {})
|
||
quality_map = job.setdefault("seg_num_step", {})
|
||
for (_si, _sr, _sid, _fp, _nstep) in _pending_seg_writes:
|
||
if _fp is not None:
|
||
hashes[_sid] = _fp
|
||
quality_map[_sid] = _nstep
|
||
job["seg_hashes"] = dict(hashes)
|
||
# Duration-planner calibration: per-language (chars, natural dur)
|
||
# records. update() (not replace) so partial regens keep accumulating
|
||
# samples from earlier runs of this track.
|
||
if _natural_dur_records:
|
||
job.setdefault("seg_natural_durs_by_lang", {}).setdefault(
|
||
lang_code, {},
|
||
).update(_natural_dur_records)
|
||
# Single job flush instead of one per 8 segments.
|
||
_save_job(job_id, job)
|
||
_t_diskw = time.perf_counter() - _t_diskw_0
|
||
|
||
sr = backend.sample_rate
|
||
slot_fit = (req.slot_fit or "time_stretch").lower()
|
||
overflow_budget_s = max(0.0, float(req.overflow_budget_s or 0.0))
|
||
|
||
# Per-segment fit_status emitted alongside the legacy sync_scores so
|
||
# the UI can replace the lying "Sync: 100%" badge with a truthful
|
||
# "Fits / Overflows +0.4s / Slipped 0.2s / Video stretched 1.18×".
|
||
fit_status: list[dict] = []
|
||
|
||
# Mode B layout: when stretch_video is on, compute a new timeline
|
||
# where each segment's slot equals the natural-rate dub audio length;
|
||
# gaps stay at 1.0×. Persisted on the job so dub_export.py can build
|
||
# the matching per-segment setpts filter chain on the source video.
|
||
new_layout: list[tuple[float, float]] = []
|
||
video_stretch_plan: list[dict] = []
|
||
fit_plan = None # smart_fit only — services.fit_planner.FitPlan
|
||
orig_total_dur = float(job.get("duration") or 0.0)
|
||
|
||
if strategy == "stretch_video":
|
||
cursor = 0.0
|
||
for i, (orig_start, orig_end, wav_path, _) in enumerate(all_segment_wavs):
|
||
wl_i = _entry_num_samples((orig_start, orig_end, wav_path, sr))
|
||
natural_dur = (wl_i / sr) if wl_i > 0 else max(0.0, orig_end - orig_start)
|
||
if i == 0:
|
||
# Preserve the pre-roll (silence before the first seg).
|
||
cursor = orig_start
|
||
else:
|
||
prev_orig_end = all_segment_wavs[i - 1][1]
|
||
gap = max(0.0, orig_start - prev_orig_end)
|
||
cursor += gap
|
||
new_start = cursor
|
||
new_end = cursor + natural_dur
|
||
new_layout.append((new_start, new_end))
|
||
orig_dur = max(1e-3, orig_end - orig_start)
|
||
video_stretch_plan.append({
|
||
"orig_start": round(orig_start, 4),
|
||
"orig_end": round(orig_end, 4),
|
||
"new_start": round(new_start, 4),
|
||
"new_end": round(new_end, 4),
|
||
"stretch_ratio": round(natural_dur / orig_dur, 4),
|
||
})
|
||
cursor = new_end
|
||
# Preserve the trailing tail (anything after the last seg in the
|
||
# original video) at 1.0× rate.
|
||
if all_segment_wavs:
|
||
last_orig_end = all_segment_wavs[-1][1]
|
||
cursor += max(0.0, orig_total_dur - last_orig_end)
|
||
new_total_dur = max(cursor, orig_total_dur)
|
||
total_samples = int(new_total_dur * sr)
|
||
elif strategy == "smart_fit":
|
||
# Smart Fit: plan the audio-rate / video-ratio split per segment
|
||
# from the natural-rate WAV lengths. Pure planning — the mix
|
||
# loop below applies the audio side; the video side ships as
|
||
# fit_plans[lang] for the (Phase B) export pipeline.
|
||
_fo = req.fit_options
|
||
_fit_defaults = FitParams()
|
||
fit_params = FitParams(
|
||
max_audio_only_rate=float(getattr(_fo, "max_audio_only_rate", None) or _fit_defaults.max_audio_only_rate),
|
||
audio_rate_cap=float(getattr(_fo, "audio_rate_cap", None) or _fit_defaults.audio_rate_cap),
|
||
video_slow_cap=float(getattr(_fo, "video_slow_cap", None) or _fit_defaults.video_slow_cap),
|
||
gap_guard_s=float(_fo.gap_guard_s) if _fo is not None and _fo.gap_guard_s is not None else _fit_defaults.gap_guard_s,
|
||
allow_video_retime=bool(_fo.allow_video_retime) if _fo is not None and _fo.allow_video_retime is not None else _fit_defaults.allow_video_retime,
|
||
min_audio_rate=_underrun_min_rate(),
|
||
)
|
||
_seg_order = job.get("seg_order") or []
|
||
fit_plan = plan_fit(
|
||
[
|
||
{
|
||
"id": _seg_order[i] if i < len(_seg_order) else f"seg_{i}",
|
||
"start": s,
|
||
"end": e,
|
||
}
|
||
for i, (s, e, _path, _) in enumerate(all_segment_wavs)
|
||
],
|
||
[_entry_num_samples(entry) / sr for entry in all_segment_wavs],
|
||
orig_total_dur,
|
||
fit_params,
|
||
)
|
||
total_samples = int(fit_plan.total_duration * sr)
|
||
else:
|
||
total_samples = int(orig_total_dur * sr)
|
||
|
||
# smart_fit: cue times for the fitted timeline, computed from the
|
||
# ACTUAL stretched/trimmed sample positions in the mix loop below —
|
||
# not from the plan — so subtitles land exactly on the audio.
|
||
fitted_cues: list[dict] = []
|
||
|
||
track_path = os.path.join(DUB_DIR, job_id, f"dubbed_{lang_code}.wav")
|
||
os.makedirs(os.path.dirname(track_path), exist_ok=True)
|
||
|
||
import gc
|
||
import tempfile
|
||
import numpy as np
|
||
|
||
mix_samples = max(total_samples, 1)
|
||
fd, mix_path = tempfile.mkstemp(
|
||
prefix=f".{os.path.basename(track_path)}.mix.",
|
||
suffix=".f32",
|
||
dir=os.path.dirname(track_path),
|
||
)
|
||
os.close(fd)
|
||
try:
|
||
with open(mix_path, "r+b") as mix_file:
|
||
mix_file.truncate(mix_samples * 4)
|
||
mix_audio = np.memmap(mix_path, dtype=np.float32, mode="r+", shape=(mix_samples,))
|
||
|
||
for i, (start, end, wav_path, _) in enumerate(all_segment_wavs):
|
||
seg_ref = req.segments[i] if i < len(req.segments) else None
|
||
seg_gain = getattr(seg_ref, "gain", None) if seg_ref is not None else None
|
||
seg_gain = seg_gain if seg_gain is not None else 1.0
|
||
seg_gain = max(0.0, min(2.0, seg_gain))
|
||
try:
|
||
wav = _load_entry_wav((start, end, wav_path, sr), sr)
|
||
except Exception as e:
|
||
# A WAV header can be readable while its payload is
|
||
# truncated. Direct cache reuse deliberately defers the
|
||
# decode to assembly, so preserve the old recovery contract
|
||
# here: warn and fill this slot with silence instead of
|
||
# aborting the entire dub.
|
||
warning = {
|
||
"type": "warning",
|
||
"segment": i,
|
||
"message": f"cached seg lost, padding silence: {str(e)[:120]}",
|
||
}
|
||
yield f"data: {json.dumps(warning)}\n\n"
|
||
wav = torch.zeros(1, max(0, int((end - start) * sr)))
|
||
adjusted = wav * seg_gain
|
||
if adjusted.ndim == 2 and adjusted.shape[0] > 1:
|
||
adjusted = adjusted.mean(dim=0, keepdim=True)
|
||
wl = adjusted.shape[-1]
|
||
natural_dur = wl / sr if wl > 0 else 0.0
|
||
orig_dur = max(0.0, end - start)
|
||
|
||
if strategy == "stretch_video":
|
||
# Mode B: audio at natural rate, placed on the stretched
|
||
# timeline. No trim, no atempo. dub_export handles the video.
|
||
new_start, _new_end = new_layout[i]
|
||
place_at = new_start
|
||
fit_status.append({
|
||
"status": "video_stretched",
|
||
"stretch_ratio": round(natural_dur / max(orig_dur, 1e-3), 3),
|
||
})
|
||
|
||
elif strategy == "smart_fit":
|
||
# Smart Fit: apply the planner's audio_rate via the same
|
||
# pitch-preserving atempo pipe strict_slot uses, place the
|
||
# result at the planned new_start, and hard-trim whatever
|
||
# the caps couldn't absorb. The video side (video_ratio per
|
||
# chunk) is persisted below for the export pipeline.
|
||
sf = fit_plan.segments[i]
|
||
place_at = sf.new_start
|
||
# Both directions: >1 compresses an overrun, <1 slows an
|
||
# underrun toward the slot (the "hole" fix — a dub that
|
||
# finishes early leaves the mouth moving over near-silence).
|
||
if abs(sf.audio_rate - 1.0) > 1e-6 and wl > 0:
|
||
target = max(1, int(round(wl / sf.audio_rate)))
|
||
try:
|
||
adjusted = await _pitch_preserving_stretch(
|
||
adjusted, target, sr,
|
||
)
|
||
except Exception as e:
|
||
logger.warning(
|
||
"atempo stretch failed for seg %d (%.2f×), "
|
||
"falling back to linear interp: %s",
|
||
i, sf.audio_rate, e,
|
||
)
|
||
adjusted = torch.nn.functional.interpolate(
|
||
adjusted.unsqueeze(0),
|
||
size=target,
|
||
mode='linear',
|
||
align_corners=False,
|
||
).squeeze(0)
|
||
wl = adjusted.shape[-1]
|
||
# Residual overflow → hard-trim to the segment's new video
|
||
# slot (fade below keeps the cut pop-free).
|
||
new_slot_samples = int(max(0.0, sf.new_end - sf.new_start) * sr)
|
||
if new_slot_samples > 0 and wl > new_slot_samples:
|
||
adjusted = adjusted[..., :new_slot_samples]
|
||
wl = adjusted.shape[-1]
|
||
# Truthful per-segment verdict for the UI badge.
|
||
entry = {"status": sf.status}
|
||
if abs(sf.audio_rate - 1.0) > 1e-6:
|
||
entry["audio_rate"] = round(sf.audio_rate, 3)
|
||
if sf.video_ratio > 1.0 + 1e-6:
|
||
entry["video_ratio"] = round(sf.video_ratio, 3)
|
||
if sf.overflow_s > 0:
|
||
entry["overflow_s"] = round(sf.overflow_s, 3)
|
||
fit_status.append(entry)
|
||
# Cue times from the ACTUAL stretched sample positions.
|
||
fitted_cues.append({
|
||
"id": sf.seg_id,
|
||
"start": round(place_at, 4),
|
||
"end": round(place_at + wl / sr, 4),
|
||
})
|
||
|
||
elif strategy == "concise":
|
||
# Mode A: never compress. Allow the audio to extend into the
|
||
# silent gap before the next seg (existing heuristic) plus
|
||
# any extra `overflow_budget_s`. Beyond that, hard-trim with
|
||
# a short fade so we never overlap the next speaker.
|
||
place_at = start
|
||
effective_end = end
|
||
if i + 1 < len(all_segment_wavs):
|
||
next_start = all_segment_wavs[i + 1][0]
|
||
gap = next_start - end
|
||
if gap > GAP_OVERFLOW_BUFFER_S:
|
||
effective_end = end + min(
|
||
gap - GAP_OVERFLOW_BUFFER_S, GAP_OVERFLOW_MAX_S,
|
||
)
|
||
effective_end += overflow_budget_s
|
||
slot_samples_eff = int(max(0.0, (effective_end - start)) * sr)
|
||
if slot_samples_eff > 0 and wl > slot_samples_eff:
|
||
overflow_s = (wl - slot_samples_eff) / sr
|
||
adjusted = adjusted[..., :slot_samples_eff]
|
||
wl = adjusted.shape[-1]
|
||
fit_status.append({
|
||
"status": "overflows",
|
||
"overflow_s": round(overflow_s, 3),
|
||
})
|
||
else:
|
||
fit_status.append({"status": "fits"})
|
||
|
||
else:
|
||
# strict_slot (legacy): preserve the previous atempo / trim /
|
||
# off semantics so existing callers and back-compat tests
|
||
# keep passing.
|
||
place_at = start
|
||
effective_end = end
|
||
slowed_rate = None
|
||
if i + 1 < len(all_segment_wavs):
|
||
next_start = all_segment_wavs[i + 1][0]
|
||
gap = next_start - end
|
||
if gap > GAP_OVERFLOW_BUFFER_S:
|
||
effective_end = end + min(
|
||
gap - GAP_OVERFLOW_BUFFER_S, GAP_OVERFLOW_MAX_S,
|
||
)
|
||
slot_samples = int(max(0.0, (effective_end - start)) * sr)
|
||
if slot_fit != "off" and slot_samples > 0 and wl > slot_samples:
|
||
if slot_fit == "time_stretch":
|
||
ratio = wl / slot_samples
|
||
capped_ratio = min(ratio, MAX_STRETCH_RATIO)
|
||
capped_target = int(wl / capped_ratio)
|
||
try:
|
||
adjusted = await _pitch_preserving_stretch(
|
||
adjusted, capped_target, sr,
|
||
)
|
||
if adjusted.shape[-1] > slot_samples:
|
||
adjusted = adjusted[..., :slot_samples]
|
||
if ratio > MAX_STRETCH_RATIO:
|
||
logger.info(
|
||
"seg %d compression %.2f× exceeded cap; "
|
||
"stretched to %.2f×, tail trimmed",
|
||
i, ratio, capped_ratio,
|
||
)
|
||
except Exception as e:
|
||
logger.warning(
|
||
"atempo stretch failed for seg %d (%.2f×), "
|
||
"falling back to linear interp: %s",
|
||
i, ratio, e,
|
||
)
|
||
adjusted = torch.nn.functional.interpolate(
|
||
adjusted.unsqueeze(0),
|
||
size=slot_samples,
|
||
mode='linear',
|
||
align_corners=False,
|
||
).squeeze(0)
|
||
else: # "trim"
|
||
adjusted = adjusted[..., :slot_samples]
|
||
wl = adjusted.shape[-1]
|
||
elif (
|
||
slot_fit == "time_stretch"
|
||
and slot_samples > 0
|
||
and wl > 0
|
||
and wl < slot_samples * UNDERRUN_TOLERANCE
|
||
and _underrun_min_rate() < 1.0 - 1e-6
|
||
):
|
||
# Underrun fill (mirror of the compression above): the
|
||
# dub finished early, leaving the on-screen mouth moving
|
||
# over the thin under-speech bed residue — perceived as
|
||
# dead air. Slow toward the slot, never below the floor.
|
||
rate = max(wl / slot_samples, _underrun_min_rate())
|
||
target = min(slot_samples, int(round(wl / rate)))
|
||
try:
|
||
adjusted = await _pitch_preserving_stretch(
|
||
adjusted, target, sr,
|
||
)
|
||
slowed_rate = rate
|
||
except Exception as e:
|
||
logger.warning(
|
||
"underrun fill failed for seg %d (%.2f×), "
|
||
"keeping natural rate: %s", i, rate, e,
|
||
)
|
||
wl = adjusted.shape[-1]
|
||
# Truthful verdict: a slowed segment says so (and by how
|
||
# much) instead of hiding behind "fits" — the same honesty
|
||
# contract the smart_fit branch keeps.
|
||
if slowed_rate is not None:
|
||
fit_status.append({
|
||
"status": "audio_slowed",
|
||
"audio_rate": round(slowed_rate, 3),
|
||
})
|
||
else:
|
||
fit_status.append({
|
||
"status": "fits",
|
||
"compression_applied": (slot_fit == "time_stretch"
|
||
and wl != int(natural_dur * sr)),
|
||
})
|
||
|
||
# Common: short fades to avoid pops, then mix into disk-backed audio.
|
||
fade_ms = 15
|
||
fade_samples = int((fade_ms / 1000.0) * sr)
|
||
if wl > fade_samples * 2:
|
||
ramp_up = torch.linspace(0, 1, fade_samples, device=adjusted.device)
|
||
ramp_down = torch.linspace(1, 0, fade_samples, device=adjusted.device)
|
||
adjusted[0, :fade_samples] *= ramp_up
|
||
adjusted[0, -fade_samples:] *= ramp_down
|
||
|
||
s = int(place_at * sr)
|
||
if s < 0:
|
||
adjusted = adjusted[..., -s:]
|
||
wl = adjusted.shape[-1]
|
||
s = 0
|
||
e = min(s + wl, total_samples)
|
||
if s < total_samples and e > s:
|
||
mix_len = e - s
|
||
seg_np = (
|
||
adjusted[:, :mix_len]
|
||
.detach()
|
||
.cpu()
|
||
.to(torch.float32)
|
||
.clamp(-1.0, 1.0)
|
||
.squeeze(0)
|
||
.numpy()
|
||
)
|
||
mix_audio[s:e] += seg_np
|
||
try:
|
||
del wav, adjusted
|
||
except Exception:
|
||
pass
|
||
_release_audio_tensors()
|
||
|
||
_t_save_0 = time.perf_counter()
|
||
mix_audio.flush()
|
||
_write_memmap_wav_atomic(track_path, mix_audio[:mix_samples], sr)
|
||
_t_save = time.perf_counter() - _t_save_0
|
||
_t_mix = _t_save_0 - _t_loop_end
|
||
finally:
|
||
try:
|
||
mix_audio.flush()
|
||
mix_mmap = getattr(mix_audio, "_mmap", None)
|
||
if mix_mmap is not None:
|
||
mix_mmap.close()
|
||
except Exception:
|
||
pass
|
||
try:
|
||
del mix_audio
|
||
except Exception:
|
||
pass
|
||
gc.collect()
|
||
try:
|
||
os.unlink(mix_path)
|
||
except OSError:
|
||
pass
|
||
# The final track is written; the mix_<id> scratch WAVs (silence /
|
||
# cached-fail / error slots) have served their only purpose as
|
||
# assembly inputs and would otherwise leak into the job dir.
|
||
for _mp in _mix_temp_paths:
|
||
try:
|
||
os.unlink(_mp)
|
||
except OSError:
|
||
pass
|
||
# Per-track metadata. For stretch_video, the dub wav is at the new
|
||
# (longer) timeline, so we record its actual duration here too — the
|
||
# mux step needs this to know whether to use the original video as-is
|
||
# or stretch it per the plan.
|
||
track_dur = total_samples / sr if total_samples > 0 else 0.0
|
||
job["dubbed_tracks"][lang_code] = {
|
||
"path": track_path,
|
||
"language": req.language,
|
||
"language_code": lang_code,
|
||
"duration": round(track_dur, 4),
|
||
"timing_strategy": strategy,
|
||
}
|
||
|
||
# Persist the timing strategy + (for Mode B) the per-segment stretch
|
||
# plan so dub_export can build the matching video pipeline at mux
|
||
# time. Plans are keyed by language code because each language gets
|
||
# its own dub track with its own natural-rate audio layout.
|
||
job["language"] = req.language
|
||
job["language_code"] = lang_code
|
||
job["timing_strategy"] = strategy
|
||
# Keep job segments in lock-step with what was just rendered so
|
||
# subtitle export / burn-in use the translated text (#309).
|
||
_sync_job_segments(job, req)
|
||
if strategy == "stretch_video":
|
||
stretch_plans = job.setdefault("video_stretch_plans", {})
|
||
stretch_plans[lang_code] = {
|
||
"plan": video_stretch_plan,
|
||
"total_duration": round(track_dur, 4),
|
||
"orig_duration": round(orig_total_dur, 4),
|
||
}
|
||
elif strategy == "smart_fit" and fit_plan is not None:
|
||
# video_stretch_plans stays untouched — smart_fit persists its
|
||
# own keyspace so a job can carry both without clobbering.
|
||
_fit_params_payload = {
|
||
"timing_strategy": strategy,
|
||
"max_audio_only_rate": fit_params.max_audio_only_rate,
|
||
"audio_rate_cap": fit_params.audio_rate_cap,
|
||
"video_slow_cap": fit_params.video_slow_cap,
|
||
"gap_guard_s": fit_params.gap_guard_s,
|
||
"allow_video_retime": fit_params.allow_video_retime,
|
||
}
|
||
fit_fp = fit_fingerprint(_fit_params_payload)
|
||
job.setdefault("fit_plans", {})[lang_code] = {
|
||
# Same dict shape _build_video_stretch_filter_graph consumes.
|
||
"plan": fit_plan.video_plan,
|
||
# Cue times from actual stretched sample positions — for
|
||
# subtitle export on the fitted timeline.
|
||
"fitted_segments": fitted_cues,
|
||
"total_duration": round(track_dur, 4),
|
||
"orig_duration": round(orig_total_dur, 4),
|
||
"params": _fit_params_payload,
|
||
"fit_fp": fit_fp,
|
||
}
|
||
job["dubbed_tracks"][lang_code]["fit_fp"] = fit_fp
|
||
# Record what kind of per-segment WAVs are on disk so a later
|
||
# smart_fit run knows whether partial regen / fit-only re-mix can
|
||
# reuse them ("natural") or must regen once ("slotted"). Per-track
|
||
# (P1.3) — each language renders under its own strategy; the flat
|
||
# field stays in lock-step for older readers.
|
||
_kind = "slotted" if strategy == "strict_slot" else "natural"
|
||
job.setdefault("seg_wav_kind_by_lang", {})[lang_code] = _kind
|
||
job["seg_wav_kind"] = _kind
|
||
_save_job(job_id, job)
|
||
|
||
_t_total = time.perf_counter() - _t_start
|
||
logger.info(
|
||
"bench[generate] total=%.2fs tts=%.2fs cache=%.2fs diskw=%.2fs mix=%.2fs save=%.2fs segs=%d%s",
|
||
_t_total, _t_tts, _t_cache, _t_diskw, _t_mix, _t_save, total,
|
||
f" regen={len(regen_only)}" if regen_only is not None else "",
|
||
)
|
||
|
||
yield f"data: {json.dumps({'type': 'done', 'segments_processed': total, 'language_code': lang_code, 'tracks': list(job['dubbed_tracks'].keys()), 'sync_scores': sync_scores, 'fit_status': fit_status, 'timing_strategy': strategy, 'seg_hashes': job.get('seg_hashes', {}), 'seg_num_step': job.get('seg_num_step', {})})}\n\n"
|
||
|
||
task_id = f"dub_{job_id}_{int(time.time())}"
|
||
await task_manager.add_task(task_id, "dub_generate", _stream, task_id)
|
||
return {"task_id": task_id}
|
||
|
||
|
||
# ── Real-time segment preview ──────────────────────────────────────────
|
||
# Stream TTS for a single segment without the full pipeline overhead.
|
||
# The frontend calls this when the user edits a segment's text/instruct
|
||
# and wants to hear the result immediately.
|
||
|
||
from pydantic import BaseModel
|
||
from typing import Optional
|
||
from fastapi.responses import Response
|
||
import io
|
||
|
||
|
||
class SegmentPreviewRequest(BaseModel):
|
||
# Optional, and only used to label diagnostics: a preview is a "render this
|
||
# text" call, so it has never needed segment identity. Supplying it makes a
|
||
# missing-clone warning name the line the user was editing instead of a
|
||
# bare "preview" (greptile).
|
||
segment_id: Optional[str] = None
|
||
text: str
|
||
language: str = "Auto"
|
||
instruct: Optional[str] = None
|
||
profile_id: Optional[str] = None
|
||
speed: float = 1.0
|
||
duration: Optional[float] = None
|
||
|
||
|
||
@router.post("/dub/preview-segment/{job_id}")
|
||
async def preview_segment(job_id: str, req: SegmentPreviewRequest):
|
||
"""Generate TTS for a single segment and return WAV bytes.
|
||
|
||
This is the fast path for interactive editing — 8 diffusion steps,
|
||
no disk write, no mix. The preview IS synthetic audio leaving the app,
|
||
so it carries the same invisible provenance mark as every other
|
||
producer (#1169 — "no watermark" here used to be an exemption).
|
||
"""
|
||
job = _get_job(job_id)
|
||
if not job:
|
||
raise HTTPException(status_code=404, detail="Job not found")
|
||
|
||
# See the /dub/generate/{job_id} resolution above (issue #312 class) —
|
||
# a segment preview resolves ref_audio from the same auto-clone /
|
||
# voice-profile sources, so it needs the same cloning-capable gate.
|
||
try:
|
||
backend = await resolve_generation_backend(require_cloning=True)
|
||
except ValueError as e:
|
||
raise HTTPException(status_code=400, detail=str(e))
|
||
|
||
def _gen():
|
||
ref_audio = None
|
||
ref_text = None
|
||
|
||
# Resolve profile / auto-clone
|
||
pid = req.profile_id
|
||
if pid and pid.startswith("auto:"):
|
||
key = pid[len("auto:"):]
|
||
info = None
|
||
if (
|
||
req.segment_id is not None
|
||
and _speaker_key_for_segment(job, req.segment_id) == key
|
||
):
|
||
info = (job.get("segment_clones") or {}).get(str(req.segment_id))
|
||
if info is None:
|
||
info = resolve_consistent_ref(job, key)
|
||
if info:
|
||
ref_audio = info.get("ref_audio")
|
||
ref_text = info.get("ref_text")
|
||
pid = None
|
||
|
||
instruct_str = req.instruct
|
||
if pid:
|
||
with db_conn() as conn:
|
||
row = conn.execute(
|
||
"SELECT * FROM voice_profiles WHERE id=?", (pid,)
|
||
).fetchone()
|
||
if row:
|
||
if row["is_locked"] and row["locked_audio_path"]:
|
||
ref_audio = os.path.join(VOICES_DIR, row["locked_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
elif row["ref_audio_path"]:
|
||
ref_audio = os.path.join(VOICES_DIR, row["ref_audio_path"])
|
||
ref_text = row["ref_text"]
|
||
if not instruct_str and row["instruct"]:
|
||
instruct_str = row["instruct"]
|
||
|
||
ref_audio = warn_if_ref_missing(
|
||
ref_audio, job_id=job_id,
|
||
seg_id=req.segment_id or "preview", where="dub preview",
|
||
)
|
||
lang = req.language if req.language != "Auto" else None
|
||
# Same normalization as the full dub render above, so a preview
|
||
# sounds exactly like the final segment. Pref-gated, never raises.
|
||
from services.text_normalization import normalize_for_tts
|
||
audio_out = backend.generate(
|
||
text=normalize_for_tts(req.text, lang),
|
||
language=lang,
|
||
ref_audio=ref_audio,
|
||
ref_text=ref_text,
|
||
instruct=instruct_str if instruct_str else None,
|
||
duration=req.duration,
|
||
num_step=8, # fast preview
|
||
guidance_scale=2.0,
|
||
speed=req.speed,
|
||
denoise=True,
|
||
postprocess_output=True,
|
||
)
|
||
if not getattr(backend, "applies_own_mastering", False):
|
||
audio_out = apply_mastering(audio_out, sample_rate=backend.sample_rate)
|
||
audio_out = normalize_audio(audio_out, target_dBFS=-2.0)
|
||
# Invisible provenance mark before the WAV encode (#1169); pref-gated,
|
||
# never raises, and short-preview embedding runs in this same
|
||
# GPU-pool job.
|
||
return mark_synthetic(audio_out, backend.sample_rate,
|
||
context="dub_generate.preview_segment")
|
||
|
||
# Bounded + pool-reset on hang so a wedged preview generate can't starve the
|
||
# GPU pool and brick the backend (#730 class). Length-scaled budget (#1190).
|
||
from services.model_manager import generate_timeout_s
|
||
audio_tensor = await run_on_gpu_pool_guarded(
|
||
_gen, what="Dub preview generate",
|
||
timeout=generate_timeout_s(req.text, engine=backend),
|
||
)
|
||
|
||
sr = backend.sample_rate
|
||
buf = io.BytesIO()
|
||
_safe_torchaudio_save(buf, audio_tensor, sr, format="wav")
|
||
buf.seek(0)
|
||
|
||
return Response(
|
||
content=buf.read(),
|
||
media_type="audio/wav",
|
||
headers={
|
||
"X-Audio-Duration": str(round(audio_tensor.shape[-1] / sr, 2)),
|
||
},
|
||
)
|