Recipe: transcribe a channel

Goal: the full transcript set of a creator’s recent videos, ready for search or an LLM. Two endpoints: channel-videos to enumerate, transcript per video.

The loop

import httpx

BASE = "https://api.scrapersocial.com/v1"
H = {"Authorization": "Bearer sk_live_..."}

def channel_videos(handle: str, max_items: int = 100):
    params = {"handle": handle, "limit": 50}
    while max_items > 0:
        r = httpx.get(f"{BASE}/tiktok/channel-videos", params=params,
                      headers=H, timeout=120).json()
        for v in r["data"][:max_items]:
            yield v
        max_items -= len(r["data"])
        if not r["meta"]["has_more"]:
            return
        params["cursor"] = r["meta"]["cursor"]

transcripts = {}
for video in channel_videos("tiktok", max_items=50):
    t = httpx.get(f"{BASE}/tiktok/transcript",
                  params={"url": video["url"]}, headers=H, timeout=120)
    if t.status_code == 422:      # image post / no speech
        continue
    t.raise_for_status()
    transcripts[video["id"]] = t.json()["data"]["text"]

Credit math

50 videos listed (3 credits each) + 50 transcripts (8 credits each) = 550 credits — about $2.75 at the monthly rate, $2.48 on annual.

Notes

Transcripts are cached forever, so re-running the loop is fast for already-seen videos (same credits, millisecond responses).
Prefer format=text if you’re piping straight into a prompt.
For Instagram, swap tiktok/channel-videos for instagram/channel-reels and expect higher transcript pricing (audio pipeline).