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Sync Lipsync 1.9.0 Beta

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Generate realistic lip-sync animations from audio using advanced algorithms for high-quality facial synchronization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

digital-human
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$0.025每次運行·~40 / $1

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README

sync/lipsync-1.9.0-beta — Audio-to-Video Lip Sync

sync/lipsync-1.9.0-beta takes an existing video and a separate audio track, then reanimates the speaker’s mouth so the lips match the new speech. It’s a zero-shot lipsync model from Sync Labs—no training or cloning step required.

🔍 Highlights

  • Zero-shot lipsync – Works on any person in any video; just upload video + audio.
  • Style-aware editing – Adjusts only the mouth region while keeping the person’s identity, lighting, and background intact.
  • Cross-domain support – Handles live-action footage, stylised CG, and AI-generated faces.
  • Flexible timing controlsync_mode lets you decide how to handle length mismatches between video and audio.

🧩 Parameters

  • video* Required. Input video to be edited (URL or upload). Use a shot with a clearly visible face for best results.

  • audio* Required. Target speech track (URL or upload, e.g. MP3/WAV). The model will align lip movements to this audio.

  • sync_mode Controls behavior when video and audio durations differ. Options:

  • loop

  • bounce

  • cut_off

  • silence

  • remap

Choose how you want the shorter stream to be treated (looped, trimmed, padded with silence, or time-remapped).

Output: a new video where the speaker’s lips follow the uploaded audio.

💰 Pricing

Rate: $0.025 per second of processed video.

Clip length (s)Price (USD)
5$0.13
10$0.25
20$0.50
30$0.75
60$1.50

You will only be charged for the actual duration of the input video after upload.

🚀 How to Use

  1. Upload your video in the video field (face should be front-facing or ¾ view, with minimal occlusion).
  2. Upload your audio in the audio field (clean speech, minimal background noise).
  3. Pick a sync_mode depending on how you want to handle length mismatches.
  4. Click Run and wait for the processed clip.
  5. Review the result; if timing feels off, try a different sync_mode or tweak your source video/audio.

💡 Tips

  • Use clean, well-lit close-ups for the most convincing lipsync.
  • Avoid heavy head turns or faces partially out of frame.
  • For dubbed content, make sure the speech rhythm in your audio is reasonably close to the original—lipsync works best when phrasing and pauses roughly match the performance.

More Models to Try

  • WaveSpeedAI / InfiniteTalk WaveSpeedAI’s single-avatar talking-head model that turns one photo plus audio into smooth, lip-synced digital presenter videos for tutorials, marketing, and social content.

  • WaveSpeedAI / InfiniteTalk Multi Multi-avatar version of InfiniteTalk that drives several characters in one scene from separate audio tracks, ideal for dialog-style explainers, interviews, and role-play videos.

  • Kwaivgi / Kling V2 AI Avatar Standard Cost-effective Kling-based AI avatar model that generates natural talking-face videos from a single reference image and voice track, suitable for everyday content and customer support.

  • Kwaivgi / Kling V2 AI Avatar Pro Higher-fidelity Kling V2 avatar model for premium digital humans, offering smoother motion, better lip-sync, and more stable faces for commercials, brand spokespeople, and product demos.

提示:本網站部分功能由第三方 AI 模型提供支援。

Lipsync 1.9.0 Beta API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/sync/lipsync-1.9.0-beta with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Lipsync 1.9.0 Beta below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "sync_mode": "cut_off"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/sync/lipsync-1.9.0-beta" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/sync/lipsync-1.9.0-beta";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
        "sync_mode": "cut_off"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "sync_mode": "cut_off"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/sync/lipsync-1.9.0-beta", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Lipsync 1.9.0 Beta API — Frequently asked questions

What is the Lipsync 1.9.0 Beta API?

Lipsync 1.9.0 Beta is a Sync model for talking-avatar generation, exposed as a REST API on WaveSpeedAI. Generate realistic lip-sync animations from audio using advanced algorithms for high-quality facial synchronization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Lipsync 1.9.0 Beta API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/sync/sync-lipsync-1.9.0-beta.

How much does Lipsync 1.9.0 Beta cost per run?

Lipsync 1.9.0 Beta starts at $0.025 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Lipsync 1.9.0 Beta accept?

Key inputs: `video`, `audio`, `sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/sync/sync-lipsync-1.9.0-beta.

How long does Lipsync 1.9.0 Beta take to generate?

Median end-to-end generation time on WaveSpeedAI is around 269 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Lipsync 1.9.0 Beta outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Sync). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Sync Lipsync 1.9.0 Beta | AI Digital Human API | WaveSpeedAI