Sync Labs Lipsync

Sync Labs lip sync and visual dubbing

Sync Labs (product domain sync.so, API host api.sync.so) is a hosted API and web studio that regenerates a speaker's mouth (and, on newer models, jaw/expression/head) to match a new audio track. It does not generate whole people or scenes: the input is an existing talking-head video (or, on sync-3, a single still face), plus target audio (a file, a hosted URL, or text synthesized through an integrated TTS provider). Everything outside the driven face region — background, body, hair, clothing — is left untouched and composited around the regenerated face. (Documented: sync.so/docs introduction and "how AI lip sync works", verified 2026-07-10.)

Use this skill when the job is: dubbing/localizing a video into another language, re-timing lips to corrected or re-recorded audio (ADR-style), personalizing a base video with per-recipient names, cutting podcast/interview clips with clean lips, or animating a portrait photo from a voiceover. Do not reach for it to generate a face from scratch, to swap identities, or to produce a full avatar from text alone — those are different product categories.

What the models are and how to choose

All model facts below are from sync.so/docs (models pages and pricing), verified 2026-07-10. Prices are quoted per second of output at 25 fps and move with plan tier, so treat them as dated reference, not a contract.

Decision guidance (heuristic, grounded in the documented capability differences):

• Start at lipsync-2 for a normal, well-lit, roughly frontal talking head. It is the documented "most natural" general model and the cheapest of the current-quality tier. Escalate only for a reason.
• Go to lipsync-2-pro when the mouth region shows fine texture that a viewer will scrutinize — facial hair crossing the lips, visible teeth in close-up, high-bitrate footage where 512-crop softness would read as a downgrade. It is slower and ~1.7x the cost; do not pay for it on a wide shot where the face is small.
• Go to sync-3 when the shot breaks the frontal-face assumption: profile/over-the- shoulder angles, a hand or mic crossing the mouth, a cropped/partial face, or a genuine 4K deliverable where a 512 crop composited back would be soft. sync-3 also handles obstructions with no configuration and is the only model that accepts a still image as the visual input (JPEG/PNG/WebP) to animate a portrait from audio.
• Use lipsync-1.9.0-beta only for throwaway/high-volume/tight-budget work where generic lip motion is acceptable; its mouth shapes are not speaker-specific.
• Use react-1 when the deliverable requires acting, not just sync — you want the same person re-performed happier/angrier/sadder with matching head motion. Inputs are capped at 15 seconds (documented hard limit) and it exposes an emotion prompt of happy | sad | angry | disgusted | surprised | neutral.

Documented limitation to plan around: lipsync-2 and lipsync-2-pro require natural speaking motion in the input video. A near-static frame (a locked-off portrait, a person holding still) may not sync well on those models — that is exactly the case for sync-3 (which can "open silent lips naturally") or a still-image sync-3 job.

API shape

Documented facts below verified 2026-07-10 against sync.so/docs API reference.

• Endpoint: POST https://api.sync.so/v2/generate
• Auth header: x-api-key: YOUR_API_KEY (not a Bearer token; the SDK reads SYNC_API_KEY from the environment).
• SDKs: Python pip install syncsdk (import from sync import Sync), and a TypeScript SDK (SyncClient). Both wrap generations.create() / generations.get().
• Rate limit: 100 requests/minute per key (documented). Concurrency (in-flight generations) is separately capped by plan tier — exceeding it returns concurrency_limit_reached (429), which is not the same as rate_limit_exceeded.
• Runtime error catalog: unauthenticated GET https://api.sync.so/v2/errors returns {code, message, suggestion} objects. Resolve any errorCode against it at runtime rather than hard-coding message strings.

Request body

{
  "model": "lipsync-2",
  "input": [
    { "type": "video", "url": "https://cdn.example.com/source.mp4" },
    { "type": "audio", "url": "https://cdn.example.com/newvoice.mp3" }
  ],
  "options": {
    "sync_mode": "cut_off",
    "temperature": 0.5,
    "active_speaker_detection": { "auto_detect": true },
    "occlusion_detection_enabled": false
  },
  "outputFileName": "dub_v1",
  "webhookUrl": "https://your.app/hooks/sync"
}

Field notes (documented):

• input — exactly one visual item (video or image; image only on sync-3) plus one audio source (audio, or a text TTS item, or dubParams — pick one path). Supplying two visuals returns generation_input_too_many_visual. Each item takes either url (any publicly reachable URL) or assetId (from the Assets API), and an optional refId used to wire it to the segments array. Video items may carry segments_secs / segments_frames to lipsync only sub-ranges.
• options.sync_mode — how a video/audio duration mismatch is reconciled: silence (pad audio with silence to fill the video), cut_off (trim the video to the audio length), remap (change video playback speed to match audio), loop (repeat the video), bounce (ping-pong the video forward/back). Ignored for image inputs. cut_off is the safe default for dubbing when the new voice is shorter and you don't want frozen tails; remap risks unnatural motion speed, so use it deliberately.
• options.temperature — 0.0–1.0, expressiveness of the generated mouth motion. Higher is more animated but can over-articulate; lower is calmer/more conservative. (Documented range; effect is a first-party parameter description.)
• options.active_speaker_detection — for multi-face frames. {auto_detect: true} lets the model pick the talking face; otherwise pin the speaker with coordinates, bounding_boxes, bounding_boxes_url, and/or frame_number. Without this, a multi-face clip can drive the wrong face. Active Speaker Detection is a paid-tier feature (Creator plan and up).
• options.occlusion_detection_enabled — on lipsync-2 / lipsync-2-pro, turn on when hands/mics/hair cross the mouth. On sync-3 it is automatic and this flag is unnecessary.
• options.model_mode — lips | face | head (region the model is allowed to edit; broader modes drive expression/head, relevant to react-1-style editing).
• options.prompt — emotion for react-1: happy | sad | angry | disgusted | surprised | neutral.
• segments — array of {startTime, endTime, audioInput:{refId,...}, optionsOverride} to apply different audio and per-segment options to different time ranges of one video (multi-speaker / multi-line dubbing in a single job).
• dubParams — automatic translation dubbing: {providerName:"elevenlabs", sourceLang, targetLang, numSpeakers}. It extracts the audio from the input video, translates, and re-voices; do not also pass a separate audio/text input (that returns generation_input_dub_audio_conflict). Documented target languages include en, es, fr, de, it, pt, pl, hi, zh, ja, ar, ru, ko, id, nl, tr, sv, fil, ms, ro, uk, el, cs, da, fi, bg, hr, sk, ta; numSpeakers 0–50.
• outputFileName — alphanumerics/underscores/hyphens only, .mp4 appended.

Response and job lifecycle

POST returns 201 with an id and status. Statuses: PENDING (queued) → PROCESSING → terminal COMPLETED (outputUrl populated), FAILED (check error + errorCode), or REJECTED (validation/policy rejection before processing).

Two ways to learn the outcome:

1. Poll GET /v2/generate/{id} (SDK generations.get(id)) at ~10 s intervals. GET ...?wait=true long-polls but caps at a 10-second server timeout — it is not a substitute for polling a long job.
2. Webhooks (preferred for anything long): pass webhookUrl (public HTTPS, must return 2xx quickly). Each delivery carries a Sync-Signature: t={timestamp},v1={hmac} header; verify by computing HMAC-SHA256 over timestamp + rawBody with your signing secret (whsec_...) and comparing timing-safely. There is no automatic retry on a failed delivery, so keep a polling fallback for reconciliation.

Complete examples

Example 1 — Localize an ad into Spanish with cloned voice (production dubbing)

This is a worked example, not a required template.

• Intent: dub a 45 s English product ad into Spanish, keeping the presenter's own voice character, delivering a clean 1080p MP4.
• Model: lipsync-2 (frontal, well-lit presenter; no occlusion) — escalate to sync-3 only if QA shows profile shots.
• Inputs/constraints: source video hosted at a public URL (over the 20 MB direct- upload limit, so URL not upload). Spanish audio produced by an ElevenLabs voice cloned from the presenter's own consented English audio (30+ s clean single-speaker sample).

from sync import Sync
from sync.common import Video, Audio, GenerationOptions

sync = Sync()  # reads SYNC_API_KEY
job = sync.generations.create(
    input=[
        Video(url="https://cdn.example.com/ad_en_1080p.mp4"),
        Audio(url="https://cdn.example.com/ad_es_clonedvoice.wav"),
    ],
    model="lipsync-2",
    options=GenerationOptions(
        sync_mode="cut_off",          # ES track is shorter; trim video tail
        occlusion_detection_enabled=False,
        active_speaker_detection={"auto_detect": True},
    ),
    output_file_name="ad_es_v1",
)
# then poll job.id, or receive a webhook

• Why structured this way: cut_off avoids a frozen final frame when the translated line runs short. Voice is cloned from the presenter's own consented sample — the consent-first path. 1080p input keeps face detection reliable while the 512 crop composites back cleanly at that scale.
• Expected result: lips track the Spanish audio, presenter's cadence preserved, rest of frame identical to source.
• Likely failures: if the ES track is much longer than the video, cut_off will truncate speech — switch to remap (accept mild speed change) or re-time the script. If QA shows soft teeth in the close-up hero shot, re-run that shot on lipsync-2-pro.

Example 2 — Multi-speaker dialogue dub in one job (segments)

Example. Two on-camera speakers, one 15 s clip, each line re-voiced by a distinct cloned voice via integrated TTS, no separate audio hosting:

job = sync.generations.create(
    input=[
        Video(url="https://cdn.example.com/interview.mp4"),
        TTS(provider={"name":"elevenlabs","voiceId":"voice_A","script":"Bienvenidos al programa."}, ref_id="a"),
        TTS(provider={"name":"elevenlabs","voiceId":"voice_B","script":"Gracias por recibirme."}, ref_id="b"),
    ],
    segments=[
        {"startTime": 0, "endTime": 8,  "audioInput": {"refId": "a"}},
        {"startTime": 8, "endTime": 15, "audioInput": {"refId": "b"}},
    ],
    model="lipsync-2",
)

• Why: segments binds each TTS line to a time range and (via active-speaker detection per segment if needed) the right face. Keeps a two-person scene in one job.
• Failure modes: overlapping/mis-timed segments return generation_input_segments_invalid; if both faces are visible per segment, add active_speaker_detection in optionsOverride to pin each speaker.

Example 3 — Animate a still portrait from a voiceover (sync-3 only)

Example. Bring a single headshot to life reading a 20 s script:

{
  "model": "sync-3",
  "input": [
    { "type": "image", "url": "https://cdn.example.com/headshot.png" },
    { "type": "audio", "url": "https://cdn.example.com/vo.mp3" }
  ],
  "outputFileName": "portrait_talk"
}

• Why sync-3: image input and "opening silent lips naturally" are sync-3-only. sync_mode is ignored for images (no intrinsic duration).
• Failure mode: a low-res or heavily stylized face may not track; use a clear, well-lit, roughly frontal headshot.

Example 4 — Batch localization (Scale/Enterprise)

Example. Batch API accepts a JSONL file, one record per line, each a /v2/generate body plus a unique request_id. Documented constraints: 20–1000 records per batch, file ≤5 MB, endpoint must be /v2/generate, request_ids unique, Scale plan or higher (batch_plan_required / 403 otherwise). Use it to fan out one video into many target languages, or many personalized variants, without 1000 individual calls.

Input requirements and constraints (documented, 2026-07-10)

• Video containers: MP4, MOV, WebM, AVI. Codec: H.264 gives best quality; H.265/MPEG-2 lose up to ~15%, VP9 ~20%, AV1 >20% (everything is transcoded to H.264, output re-encoded libx264 -crf 17 -preset slow). Prefer MP4/H.264 in, 1080p.
• Resolution: ≥480p required for reliable face detection; 1080p recommended; max 4K (4096×2160). Non-sync-3 models extract the face at 512×512, process it, and composite back — so an enormous input resolution does not raise face detail on those models; sync-3 works at 4K natively.
• Aspect ratio: any (9:16, 16:9, 1:1, custom).
• Frame rate: constant 24/25/30 fps recommended.
• Audio: WAV/MP3 recommended (also OGG/FLAC/ALAC/MP4-audio fully supported; WMA/M4A/AAC limited). 44.1 or 48 kHz recommended; up to 32-bit float, up to 7.1 ch. One speaker per audio track, isolated from music/crowd noise.
• Direct upload limit: 20 MB (413/file_size_exceeds_plan_limit); above that, host at a public URL (no size cap) or use the Assets API (POST /v2/assets/upload).
• Duration: per-generation cap is plan-bound — Free 20 s, Hobbyist 1 min, Creator 5 min, Growth 10 min, Scale/Enterprise 30 min. Additional hard caps: react-1 input ≤15 s; audio ≤300 s (5 min) per generation; TTS text ≤5000 characters.

Pricing and plans (verified 2026-07-10; volatile)

Usage is metered per second of output; a subscription unlocks higher limits, features, and usage discounts. Per-second model prices are in the model table above.

Note: the free/low tiers watermark output and cap concurrency to 1 — remove the watermark by upgrading to Creator+. Batch API is Growth+ (batch_plan_required cites Scale for the batch endpoint specifically — confirm your tier against the live error catalog before building a batch pipeline).

Reviewing lip sync output (quality gate)

Before shipping, check each of these — the first two are the ones that make output look "AI", and they fail independently:

1. Sync accuracy / timing. Do mouth shapes land on the right phonemes, not early or late? Scrub plosives (b/p/m — lips must fully close) and open vowels. Drift that grows over a long clip usually means duration mismatch — revisit sync_mode (try cut_off) or split into <2-minute segments.
2. Identity preservation. Does it still look like the same person? Watch for a "smeared" or averaged mouth, lost lip shape, or a jaw that moves unlike the subject. On lipsync-1.9.0-beta expect generic (non-speaker-specific) motion by design.
3. Mouth-region artifacts. Inspect teeth (blurred/merged teeth are the classic lipsync-2 tell — fix with lipsync-2-pro), the lip/skin seam (blend/color/lighting mismatch at the composite boundary), and beard/mustache continuity across the mouth.
4. Occlusion handling. If a hand, mic, or hair crosses the mouth, does the model paint through it (wrong) or respect it? If wrong on lipsync-2/pro, set occlusion_detection_enabled=true; if still wrong, move to sync-3 (automatic).
5. Wrong-face / multi-speaker. Confirm the intended speaker was driven; if not, pin with active-speaker detection.
6. Silent/static frames. On lipsync-2/pro, a near-still input can under-sync — this is the documented "requires natural speaking motion" limit; move to sync-3.

Failure modes and repair (from the documented error catalog, 2026-07-10)

Branch on errorCode, not the human error string, and read field to see which input was rejected. Key cases and fixes:

• generation_input_face_selection_invalid ("selected face unusable") — re-run face detection / enable auto-detect, or improve face size/lighting/angle.
• generation_input_too_many_visual — you sent two visuals; send exactly one.
• generation_input_segments_invalid — segments overlap or have invalid times; make them non-overlapping with valid ranges.
• generation_input_dub_audio_conflict — you passed both dubParams and an audio/text input; choose one.
• generation_audio_length_exceeded / generation_text_length_exceeded — trim audio ≤300 s / text ≤5000 chars, or split.
• generation_plan_duration_exceeded (402) — output exceeds your plan's per-gen cap; upgrade, trim input, or use a shortening sync_mode.
• concurrency_limit_reached (429) vs rate_limit_exceeded (429) — the first means too many in-flight jobs (wait/upgrade); the second means too many requests/min (exponential backoff). Do not confuse them.
• generation_media_metadata_missing — audio needs duration; video needs duration + frame_rate. Re-encode with FFmpeg to embed metadata.
• REJECTED status — validation or policy rejection before processing; do not blindly retry, inspect the reason.
• Transient (500/503/504, controller_*, generation_timeout, generation_infra_*) — retry with exponential backoff, honor Retry-After, try off-peak; escalate with requestId if persistent.
• 402 payment / 401 auth — retry never helps; fix billing or the key.

General repair ladder for a poor-but-successful result (heuristic): improve the input (lighting, isolate voice, stabilize, raise resolution to 1080p, front the face) → adjust parameters (sync_mode, temperature, occlusion_detection_enabled, active-speaker pin) → escalate model (lipsync-2 → lipsync-2-pro for detail, or → sync-3 for angle/occlusion/4K). Input quality dominates; try it before spending on a pricier model.

Consent, likeness, and rights (do this before generating)

Editing a real person's face to say new words is a likeness-and-voice action, not just a render. Sync Labs' own published guidance frames the line clearly (sync.so blog, verified 2026-07-10): dubbing is legitimate when the speaker/rights-holder agreed; the identical output without that agreement is a deepfake. Their design is "consent-first" — the intended path is cloning your own voice, and using someone else's voice or face requires their explicit permission.

Operating rules for an agent:

• Verify rights before submitting. Confirm the user owns or has licensed the source video, and has consent for any voice being cloned or any face being re-voiced. If those are unclear, ask — do not proceed on assumption.
• Do not impersonate real public figures or private individuals without consent, and do not produce deceptive content (fake statements, fabricated endorsements), non- consensual intimate content, or fraud/misinformation. These are the categories most likely to be REJECTED and are legally and ethically out of bounds regardless of the API accepting them.
• Voice-clone samples should be the consenting person's own clear single-speaker audio (30+ s). Cloning a third party's voice from scraped audio is the exact non- consensual case to refuse.
• Data handling. For sensitive footage, check the provider's retention/training/ deletion posture (Sync Labs' own guidance advises verifying whether a provider retains, trains on, or deletes your content on request). Prefer not uploading sensitive material you cannot account for.
• Disclosure. For dubbed/altered public-facing content, follow applicable synthetic- media disclosure norms and platform rules; a paid tier is what removes the watermark, which means the burden of honest labeling shifts to you.

These obligations are independent of which model you pick and take precedence over completing the generation.

Sources (verified 2026-07-10)

First-party (Sync Labs / sync.so):

• Introduction — https://sync.so/docs/introduction
• Lipsync models — https://sync.so/docs/models/lipsync
• sync-3 model — https://sync.so/docs/models/sync-3 ; https://sync.so/sync-3
• react-1 model — https://sync.so/react-1
• Quickstart — https://sync.so/docs/quickstart
• Create Generation API reference — https://sync.so/docs/api-reference/api/generate-api/create.md
• Error handling — https://sync.so/docs/developer-guides/error-handling (catalog: GET https://api.sync.so/v2/errors)
• Webhooks — https://sync.so/docs/api-reference/guides/webhooks
• Media formats support — https://sync.so/docs/compatibility-and-tips/media-formats-support
• Improving lip sync quality — https://sync.so/docs/compatibility-and-tips/improving-lip-sync-quality
• How AI lip sync works — https://sync.so/docs/product/how-ai-lip-sync-works
• Text-to-speech lip sync tutorial — https://sync.so/docs/tutorials/text-to-speech-lipsync
• Pricing — https://sync.so/pricing
• "Is AI dubbing safe?" (consent/rights guidance) — https://sync.so/blog/is-ai-dubbing-safe/
• Privacy — https://sync.so/privacy

Secondary (third-party model listings, used only to cross-check model availability, not as the basis for any consequential claim): fal.ai, wavespeed.ai, replicate.com, segmind.com listings of the Sync lipsync models.