Palash Debnathand4shil
1edd35cfd0
* Add per-segment audio effects DSP preset selector to dub pipeline * Add shape assertions to podcast, warm, and bright preset tests * Fix raw preset semantics, add preset validation, update docs, remove duplicate sys.path * Narrow OOM catch to model.generate only in dub_generate * Preserve original OOM exception context in dub_generate * Bind effect_preset to _gen via explicit parameter to avoid loop capture * Catch RuntimeError instead of torch.mps.MPSError for MPS OOM --------- Co-authored-by: 4shil <166588383+4shil@users.noreply.github.com>
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
"""
|
|
Incremental re-dub — Phase 4.1 (ROADMAP.md), the other defensible bet.
|
|
|
|
Given the current dub state + a prior state, compute the minimum set of
|
|
segments whose audio needs regenerating. A segment needs re-gen when any of
|
|
its *generation inputs* changed:
|
|
|
|
text · target_lang · profile_id · instruct · speed · direction
|
|
|
|
Deterministic content hash per segment is the key. We store the hash on the
|
|
segment after every successful generation; when re-dubbing, we hash again and
|
|
only queue segments whose hash differs from the stored one.
|
|
|
|
This isn't full crossfade-at-the-edges yet — that comes with Phase 4.5. For
|
|
now the caller re-runs `/dub/generate` with a filtered segments list.
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import hashlib
|
|
import json
|
|
|
|
|
|
_GEN_INPUT_FIELDS = ("text", "target_lang", "profile_id", "instruct", "speed", "direction", "effect_preset")
|
|
|
|
|
|
def segment_fingerprint(seg: dict) -> str:
|
|
"""Deterministic hash of the inputs that actually affect TTS output.
|
|
|
|
Any change to `_GEN_INPUT_FIELDS` flips the hash and the segment becomes
|
|
a re-gen candidate. Changes to position / selection state / lip-sync
|
|
badge don't trigger regen, which is what we want.
|
|
|
|
Currently includes: text, target_lang, profile_id, instruct, speed,
|
|
direction, effect_preset.
|
|
"""
|
|
payload = {k: (seg.get(k) if seg.get(k) is not None else "") for k in _GEN_INPUT_FIELDS}
|
|
blob = json.dumps(payload, sort_keys=True, ensure_ascii=False)
|
|
return hashlib.sha1(blob.encode("utf-8")).hexdigest()[:16]
|
|
|
|
|
|
def plan_incremental(
|
|
segments: list[dict],
|
|
*,
|
|
stored_hashes: dict[str, str] | None = None,
|
|
) -> dict:
|
|
"""Return `{stale, fresh, total, fingerprints}` where:
|
|
|
|
• `stale` : list of segment ids whose generation inputs changed
|
|
since the last successful generate (i.e. need re-dub).
|
|
• `fresh` : ids whose stored hash still matches current inputs —
|
|
safe to reuse the prior audio.
|
|
• `fingerprints`: {id: sha1} for every segment, for caller to persist
|
|
after a successful regen.
|
|
|
|
`stored_hashes` may come from the caller's own bookkeeping (e.g. the
|
|
`dub_history.job_data["seg_hashes"]` we'll start writing in Phase 4.5).
|
|
When missing, every segment is considered stale (first run).
|
|
"""
|
|
stored = stored_hashes or {}
|
|
stale: list[str] = []
|
|
fresh: list[str] = []
|
|
fingerprints: dict[str, str] = {}
|
|
for seg in segments:
|
|
sid = str(seg.get("id", ""))
|
|
if not sid:
|
|
continue
|
|
fp = segment_fingerprint(seg)
|
|
fingerprints[sid] = fp
|
|
prev = stored.get(sid)
|
|
if prev == fp:
|
|
fresh.append(sid)
|
|
else:
|
|
stale.append(sid)
|
|
return {
|
|
"stale": stale,
|
|
"fresh": fresh,
|
|
"total": len(segments),
|
|
"fingerprints": fingerprints,
|
|
}
|