Why: the pilot in data/ross showed that Ross Cameron's daily recaps, joined to
the SIP tape and to what our bot saw, answer "which of his trades could we have
taken and why didn't we". It was a set of one-off scripts run by hand; the
server-side youtube_claims pipeline has been dead since ~08-24 (YouTube blocks
the TrueNAS IP) and its captions fallback hid that. This makes the pilot a
nightly, local, cumulative loop without a paid LLM API.
youtube_claims/recap/ (python -m youtube_claims.recap.nightly):
- channel: yt-dlp flat listing of @DaytradeWarrior, m4a audio, faster-whisper
small.en on CPU, pilot-format bundles (meta + our scanner list + transcript).
- extract: Ollama structured output (JSON schema) per EXTRACT_SPEC, chunked
with overlap for long transcripts and merged. The verbatim rule is enforced in
code: every quote must be found (token-level, one-word tolerance) and is
snapped to the transcript's words; unquoted items, trades with no surviving
quote, prices never spoken, and passed-on names never said are dropped.
- llm: endpoint pool over RECAP_OLLAMA_HOSTS (round-robin + failover, per-host
model/ctx/think pins) since the owner runs several Ollama boxes.
- tape/join/ourside: ported join.py/ourside.py -- tickers are price-verified on
the Alpaca SIP tape (plus an order-free check for his own named ticker), then
paper (:5433) and live (:5434) event views are attached. Bars cached on disk.
- store + schema.sql: idempotent recap_* tables in the transcripts schema,
upserted per video_id. report: the pilot's sections + "what changed since last
run" against the previous day's metrics JSON.
- evaluate: scores any model/prompt against the 36 gold extractions.
Model choice (36 gold videos, final code path, P/R):
gpt-oss:20b on the workstation GPU, two focused calls, think=low:
trades P .84 R .70; tickers P .82 R .77; entry +-2% P .54 R .50;
passed-on P .66 R .76; temperature 16/31; kind 30/36; ~49 s/video.
tools:latest (4B, server) one call: trades P .81 R .72, tickers P .83 R .85,
passed-on P .80 R .75, temperature 20/31, but it invents prices
(entry P .42) and occasionally runs away (5-9 min). Prices drive the tape
verification, so the more precise local model is the default.
Iterations: prompt v2 + spoken-price rescale (699 -> 6.99) + split calls took
gpt-oss trades F1 .74 -> .76-.78 and tickers .78 -> .79-.80; think=medium
blew the token budget. qwen3-30b-a3b on mason only finished 5 videos (host
saturated) -- inconclusive.
Also: youtube_claims/transcription.py::_captions_fallback now picks the
youtube-transcript-api shape by feature detection instead of retrying the
removed get_transcript on any error, and transcribe() reports both the whisper
and captions causes -- the IpBlocked error was being masked as AttributeError.
No trading code touched.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
143 lines
6.4 KiB
Python
143 lines
6.4 KiB
Python
"""Channel listing, audio download, whisper transcription and bundle building.
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Runs on the owner's Windows workstation: YouTube blocks the TrueNAS server's IP, and the caption
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API rate-limits (IpBlocked after ~20 requests), so we pull AUDIO with yt-dlp -- one polite
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request per new video -- and transcribe locally with faster-whisper."""
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from __future__ import annotations
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import datetime as dt
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import json
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import time
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from pathlib import Path
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from youtube_claims.recap import settings
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_whisper = None
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def list_uploads(limit: int | None = None) -> list[dict]:
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"""Newest-first [{id, title, duration}] from the channel's /videos tab (flat, one request)."""
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import yt_dlp
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opts = {"extract_flat": "in_playlist", "quiet": True, "no_warnings": True,
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"playlistend": limit or settings.LIST_LIMIT}
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with yt_dlp.YoutubeDL(opts) as ydl:
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info = ydl.extract_info(settings.CHANNEL_URL, download=False)
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out = []
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for e in info.get("entries") or []:
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if e and e.get("id"):
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out.append({"id": e["id"], "title": e.get("title") or "", "duration": e.get("duration")})
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return out
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def video_meta(vid: str, title: str = "", duration=None) -> dict:
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"""{id, title, duration, upload_date} -- cached in transcripts/<id>.meta.json (the flat
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listing has no upload date, so new ids cost one metadata request each)."""
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f = settings.dirs()["transcripts"] / f"{vid}.meta.json"
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if f.exists():
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m = json.loads(f.read_text(encoding="utf-8"))
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if m.get("upload_date"):
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return m
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import yt_dlp
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with yt_dlp.YoutubeDL({"quiet": True, "no_warnings": True, "skip_download": True}) as ydl:
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info = ydl.extract_info(f"https://www.youtube.com/watch?v={vid}", download=False)
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m = {"id": vid, "title": info.get("title") or title, "duration": str(info.get("duration") or duration or ""),
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"upload_date": info.get("upload_date") or ""}
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f.write_text(json.dumps(m), encoding="utf-8")
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return m
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def download_audio(vid: str) -> Path:
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"""Best m4a audio (no ffmpeg re-encode needed; faster-whisper decodes it directly)."""
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ad = settings.dirs()["audio"]
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have = next(iter(ad.glob(f"{vid}.*")), None)
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if have and not have.name.endswith(".part"):
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return have
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import yt_dlp
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opts = {"format": "ba[ext=m4a]/ba", "outtmpl": str(ad / "%(id)s.%(ext)s"), "quiet": True,
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"no_warnings": True, "noprogress": True}
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with yt_dlp.YoutubeDL(opts) as ydl:
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ydl.download([f"https://www.youtube.com/watch?v={vid}"])
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got = next((p for p in ad.glob(f"{vid}.*") if not p.name.endswith(".part")), None)
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if got is None:
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raise RuntimeError(f"yt-dlp produced no audio file for {vid}")
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return got
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def transcribe(vid: str, audio: Path) -> list[dict]:
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"""faster-whisper segments, cached in transcripts/<id>.whisper.json (the pilot's format)."""
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out = settings.dirs()["transcripts"] / f"{vid}.whisper.json"
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if out.exists():
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return json.loads(out.read_text(encoding="utf-8"))["segments"]
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global _whisper
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if _whisper is None:
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from faster_whisper import WhisperModel
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_whisper = WhisperModel(settings.WHISPER_MODEL, device="cpu", compute_type="int8",
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cpu_threads=settings.WHISPER_THREADS)
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t0 = time.time()
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segs, _ = _whisper.transcribe(str(audio), initial_prompt=settings.WHISPER_PROMPT, vad_filter=True,
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language="en")
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rows = [{"start": round(s.start, 2), "end": round(s.end, 2), "text": s.text} for s in segs]
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out.write_text(json.dumps({"video_id": vid, "source": "whisper", "whisper_model": settings.WHISPER_MODEL,
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"seconds": round(time.time() - t0, 1), "segments": rows}), encoding="utf-8")
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return rows
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def best_transcript(vid: str) -> tuple[str | None, list[dict] | None]:
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"""Local whisper (small.en) > repo whisper (medium) > YouTube captions -- as the pilot."""
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td = settings.dirs()["transcripts"]
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repo_tx = settings.DATA_DIR.parent / "transcripts" / f"{vid}.json" # youtube_claims whisper-medium
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for path, src in ((td / f"{vid}.whisper.json", "whisper-small"), (repo_tx, "whisper-medium")):
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if path.exists():
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return src, json.loads(path.read_text(encoding="utf-8"))["segments"]
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cap = td / f"{vid}.captions.json"
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if cap.exists():
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rows = json.loads(cap.read_text(encoding="utf-8"))
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return "captions", [{"start": r["start"], "text": r["text"]} for r in rows]
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return None, None
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def prior_weekdays(upload_date: str, n: int = 1) -> list[str]:
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"""The upload date (if a weekday) and the n weekdays before it, as YYYY-MM-DD."""
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d = dt.date(int(upload_date[:4]), int(upload_date[4:6]), int(upload_date[6:]))
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out = [d.isoformat()] if d.weekday() < 5 else []
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while len(out) < (n + 1 if d.weekday() < 5 else n):
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d -= dt.timedelta(days=1)
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if d.weekday() < 5:
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out.append(d.isoformat())
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return out
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def scanner_for(upload_date: str, ourside=None) -> dict:
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"""{session date: [(symbol, gap %, price)]} for the upload day and the weekday before --
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cached in scanner/<upload_date>.json (the pilot's format)."""
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f = settings.dirs()["scanner"] / f"{upload_date}.json"
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if f.exists():
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return json.loads(f.read_text())
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if ourside is None:
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return {}
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scan = {d: [list(r) for r in ourside.day_universe(d)] for d in prior_weekdays(upload_date)}
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if any(scan.values()):
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f.write_text(json.dumps(scan))
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return scan
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def bundle_source(bundle: str) -> str | None:
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for ln in bundle.splitlines()[:6]:
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if ln.startswith("transcript_source: "):
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return ln.split(": ", 1)[1].strip()
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return None
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def make_bundle(meta: dict, source: str, segments: list[dict], scanner: dict) -> str:
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"""One plain-text bundle: meta + our scanner list + timestamped transcript (pilot format)."""
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lines = [f"video_id: {meta['id']}", f"upload_date: {meta['upload_date']}", f"title: {meta.get('title', '')}",
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f"transcript_source: {source}", "", "OUR SCANNER LIST (symbol, gap %, price) by session date:"]
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for d, rows in scanner.items():
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lines.append(f" {d}: " + ", ".join(f"{s} {g}% ${p}" if p else f"{s} {g}%" for s, g, p in rows))
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lines += ["", "TRANSCRIPT (mm:ss text):"]
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for s in segments:
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t = int(s["start"])
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lines.append(f"[{t // 60:02d}:{t % 60:02d}] {s['text'].strip()}")
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return "\n".join(lines)
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