feat(ipw): IPW 설명 개선 및 비디오/오디오 파이프라인 정비

.claude/skills/manim-video-pipeline/scripts/gen_en_slow.mjs

@@ -0,0 +1,108 @@
+// One-off: regenerate English narration with slower speed + inter-chunk pauses.
+// Outputs build/audio/{NN}_{name}.mp3 + .timings.json (same names as before).
+import fs from "node:fs/promises";
+import os from "node:os";
+import path from "node:path";
+import { spawn } from "node:child_process";
+import dotenv from "dotenv";
+import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
+
+const VOICE_ID = "7Nah3cbXKVmGX7gQUuwz";
+const MODEL_ID = "eleven_multilingual_v2";
+const OUTPUT_FORMAT = "mp3_44100_128";
+const SPEED = 0.9;
+const GAP = 0.25; // seconds of silence between chunks
+
+const repoRoot = path.resolve(path.dirname(new URL(import.meta.url).pathname), "../../../..");
+const topicDir = path.join(repoRoot, "videos", "what_is_ipw");
+dotenv.config({ path: path.join(repoRoot, ".env") });
+const client = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_API_KEY });
+
+const SCENES = [
+  ["02", "ipw_application_en"],
+  ["03", "ipw_formula_en"],
+  ["04", "propensity_score_en"],
+  ["05", "summary_en"],
+];
+
+function splitIntoChunks(text, maxChars = 650) {
+  const paras = text.split(/\n\s*\n/g).map((p) => p.replace(/\s+/g, " ").trim()).filter(Boolean);
+  const chunks = [];
+  for (const p of paras) {
+    if (p.length <= maxChars) { chunks.push(p); continue; }
+    let cur = "";
+    for (const s of p.split(/(?<=[.!?])\s+/).map((x) => x.trim()).filter(Boolean)) {
+      if ((cur + " " + s).trim().length > maxChars) { if (cur) chunks.push(cur); cur = s; }
+      else cur = (cur + " " + s).trim();
+    }
+    if (cur) chunks.push(cur);
+  }
+  return chunks;
+}
+
+async function streamToBuffer(stream) {
+  const reader = stream.getReader();
+  const out = [];
+  while (true) { const { done, value } = await reader.read(); if (done) break; out.push(Buffer.from(value)); }
+  return Buffer.concat(out);
+}
+
+function ff(args) {
+  return new Promise((res, rej) => {
+    const p = spawn("ffmpeg", args, { stdio: "ignore" });
+    p.on("close", (c) => (c === 0 ? res() : rej(new Error("ffmpeg " + c))));
+  });
+}
+function probe(file) {
+  return new Promise((res, rej) => {
+    const p = spawn("ffprobe", ["-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", file]);
+    let o = ""; p.stdout.on("data", (d) => (o += d)); p.on("close", (c) => (c === 0 ? res(parseFloat(o)) : rej(new Error("ffprobe"))));
+  });
+}
+
+for (const [scene, name] of SCENES) {
+  const scriptPath = path.join(topicDir, "src", "scripts", `${scene}_${name}.txt`);
+  const outMp3 = path.join(topicDir, "build", "audio", `${scene}_${name}.mp3`);
+  const outTim = path.join(topicDir, "build", "audio", `${scene}_${name}.timings.json`);
+  const text = (await fs.readFile(scriptPath, "utf8")).trim();
+  const chunks = splitIntoChunks(text);
+  const tmp = await fs.mkdtemp(path.join(os.tmpdir(), "en-slow-"));
+
+  const silence = path.join(tmp, "sil.mp3");
+  await ff(["-y", "-f", "lavfi", "-i", `anullsrc=channel_layout=mono:sample_rate=44100`, "-t", String(GAP), "-c:a", "libmp3lame", "-b:a", "128k", silence]);
+
+  const chunkPaths = [];
+  for (let i = 0; i < chunks.length; i++) {
+    const stream = await client.textToSpeech.convert(VOICE_ID, {
+      text: chunks[i], modelId: MODEL_ID, outputFormat: OUTPUT_FORMAT,
+      voiceSettings: { speed: SPEED },
+    });
+    const buf = await streamToBuffer(stream);
+    const cp = path.join(tmp, `c${String(i).padStart(2, "0")}.mp3`);
+    await fs.writeFile(cp, buf);
+    chunkPaths.push(cp);
+  }
+
+  // interleave silence between chunks
+  const listItems = [];
+  for (let i = 0; i < chunkPaths.length; i++) {
+    listItems.push(`file '${chunkPaths[i]}'`);
+    if (i < chunkPaths.length - 1) listItems.push(`file '${silence}'`);
+  }
+  const listPath = path.join(tmp, "list.txt");
+  await fs.writeFile(listPath, listItems.join("\n"));
+  await ff(["-y", "-f", "concat", "-safe", "0", "-i", listPath, "-c:a", "libmp3lame", "-b:a", "128k", outMp3]);
+
+  // timings: chunk i speech start = sum(prev durations) + i*GAP
+  const durs = [];
+  for (const cp of chunkPaths) durs.push(await probe(cp));
+  let acc = 0; const tchunks = [];
+  for (let i = 0; i < durs.length; i++) {
+    const start = acc + i * GAP;
+    tchunks.push({ index: i + 1, text: chunks[i], duration: durs[i], start, end: start + durs[i] });
+    acc += durs[i];
+  }
+  const total = await probe(outMp3);
+  await fs.writeFile(outTim, JSON.stringify({ topic: "what_is_ipw", scene, sceneName: name, speed: SPEED, gap: GAP, totalDuration: total, chunkCount: chunks.length, chunks: tchunks }, null, 2));
+  console.log(`${scene}_${name}: ${chunks.length} chunks, total ${total.toFixed(2)}s`);
+}

.claude/skills/manim-video-pipeline/scripts/generate_elevenlabs_audio.mjs

@@ -108,12 +108,13 @@ async function ensureEnv(repoRoot) {
   return apiKey;
 }
 
-async function synthesizeSpeechChunk(apiKey, text) {
+async function synthesizeSpeechChunk(apiKey, text, speed = 1.0) {
   const payload = {
     model: MODEL_ID,
     voice: VOICE,
     input: text,
     response_format: OUTPUT_FORMAT,
+    speed: speed,
   };
   if (INSTRUCTIONS) {
     payload.instructions = INSTRUCTIONS;
@@ -257,13 +258,7 @@ async function main() {
   const chunkPaths = [];
 
   for (let i = 0; i < chunks.length; i += 1) {
-    const audioStream = await client.textToSpeech.convert(VOICE_ID, {
-      text: chunks[i],
-      modelId: MODEL_ID,
-      outputFormat: OUTPUT_FORMAT,
-      voiceSettings: { speed },
-    });
-    const buffer = await streamToBuffer(audioStream);
+    const buffer = await synthesizeSpeechChunk(apiKey, chunks[i], speed);
     const chunkPath = path.join(tempDir, `${String(i + 1).padStart(2, "0")}.mp3`);
     await fs.writeFile(chunkPath, buffer);
     chunkPaths.push(chunkPath);

Pipfile

@@ -0,0 +1,20 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+jupyter-book = ">=2.0.0"
+manim = ">=0.18.0"
+numpy = "*"
+pandas = "*"
+matplotlib = "*"
+seaborn = "*"
+scikit-learn = "*"
+statsmodels = "*"
+graphviz = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"