fix: chunk size limit + keep MP3s + Respond Accepted fix; add rocinante stack

- Chunk Text: word-level fallback for sentences > 600 chars
- watch.py: keep MP3 after transcription
- Respond Accepted: remove cross-node reference
- rocinante/: Whisper CUDA + watcher stack for RTX 4090 transcription
This commit is contained in:
Paccoco
2026-05-09 21:10:23 -05:00
parent 31edbf1970
commit bdc81cbc2e
5 changed files with 1226 additions and 0 deletions

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{
"parameters": {
"jsCode": "const llmResponse = $input.first().json;\nconst classInfo = $('Extract Class Info').first().json;\n\nlet notes;\ntry {\n notes = JSON.parse(llmResponse.choices?.[0]?.message?.content || '{}');\n} catch(e) {\n notes = { summary: 'Could not parse notes', key_concepts: [], definitions: [], assignments: [] };\n}\n\nconst ragText = [\n `Class: ${classInfo.className}`,\n `Lecture: ${classInfo.lectureTitle}`,\n `Duration: ${classInfo.durationMinutes} minutes`,\n `Summary: ${notes.summary}`,\n `Key Concepts: ${(notes.key_concepts || []).join(', ')}`,\n '',\n 'Full Transcript:',\n classInfo.fullText\n].join('\\n');\n\nreturn [{\n json: {\n className: classInfo.className,\n classSlug: classInfo.classSlug,\n lectureTitle: classInfo.lectureTitle,\n durationMinutes: classInfo.durationMinutes,\n fullText: classInfo.fullText,\n notes,\n ragText,\n source: `class-${classInfo.classSlug}-${Date.now()}`,\n metadata: {\n class_name: classInfo.className,\n class_slug: classInfo.classSlug,\n lecture_title: classInfo.lectureTitle,\n duration_minutes: classInfo.durationMinutes,\n type: 'class_recording',\n recorded_at: new Date().toISOString()\n }\n }\n}];"
},
"id": "a1b2c3d4-0001-0001-0001-000000000005",
"name": "Prepare for RAG",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
672,
0
]
},
{
"parameters": {
"jsCode": "const input = $input.first().json;\nconst text = input.ragText || '';\nconst source = input.source;\nconst metadata = input.metadata;\n\nconst CHUNK_SIZE = 400;\nconst MAX_CHUNK = 600;\nconst OVERLAP = 50;\n\nconst chunks = [];\nconst sentences = text.split(/(?<=[.!?])\\s+/);\nlet current = '';\n\nfor (const sentence of sentences) {\n // If a single sentence exceeds MAX_CHUNK, split it by words first\n const parts = [];\n if (sentence.length > MAX_CHUNK) {\n const words = sentence.split(' ');\n let part = '';\n for (const word of words) {\n if ((part + ' ' + word).length > CHUNK_SIZE && part.length > 0) {\n parts.push(part.trim());\n part = word;\n } else {\n part = part ? part + ' ' + word : word;\n }\n }\n if (part.trim()) parts.push(part.trim());\n } else {\n parts.push(sentence);\n }\n\n for (const part of parts) {\n if ((current + ' ' + part).length > CHUNK_SIZE && current.length > 0) {\n chunks.push(current.trim());\n const words = current.split(' ');\n current = words.slice(-OVERLAP).join(' ') + ' ' + part;\n } else {\n current = current ? current + ' ' + part : part;\n }\n }\n}\nif (current.trim()) chunks.push(current.trim());\n\nreturn chunks.map((chunk, i) => ({\n json: { chunk, chunkIndex: i, totalChunks: chunks.length, source, metadata }\n}));"
},
"id": "a1b2c3d4-0001-0001-0001-000000000006",
"name": "Chunk Text",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
896,
0
]
},
{
"parameters": {
"method": "POST",
"url": "http://10.5.1.6:11434/v1/embeddings",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"model\": \"nomic-embed-text\",\n \"input\": {{ JSON.stringify($json.chunk) }}\n}",
"options": {
"batching": {
"batch": {
"batchSize": 5,
"batchInterval": 500
}
}
}
},
"id": "a1b2c3d4-0001-0001-0001-000000000007",
"name": "Get Embedding",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1120,
0
]
},
{
"parameters": {
"jsCode": "const embeddingResponse = $input.first().json;\nconst chunkData = $('Chunk Text').item;\nconst embedding = embeddingResponse.data?.[0]?.embedding || [];\n\nconst pointId = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {\n const r = Math.random() * 16 | 0;\n return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);\n});\n\nreturn [{\n json: {\n id: pointId,\n vector: embedding,\n payload: {\n text: chunkData.json.chunk,\n source: chunkData.json.source,\n chunkIndex: chunkData.json.chunkIndex,\n totalChunks: chunkData.json.totalChunks,\n metadata: chunkData.json.metadata,\n ingested_at: new Date().toISOString()\n }\n }\n}];"
},
"id": "a1b2c3d4-0001-0001-0001-000000000008",
"name": "Prepare Qdrant Point",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1344,
0
]
},
{
"parameters": {
"method": "PUT",
"url": "http://10.5.1.6:6333/collections/knowledge_base/points",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"points\": [\n {\n \"id\": \"{{ $json.id }}\",\n \"vector\": {{ JSON.stringify($json.vector) }},\n \"payload\": {{ JSON.stringify($json.payload) }}\n }\n ]\n}",
"options": {}
},
"id": "a1b2c3d4-0001-0001-0001-000000000009",
"name": "Upsert to Qdrant",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1568,
0
]
},
{
"parameters": {
"jsCode": "const rag = $('Prepare for RAG').first().json;\nreturn [{\n json: {\n title: `\ud83c\udf93 ${rag.className} Lecture Processed`,\n message: `${rag.lectureTitle} (${rag.durationMinutes}min)\\nSummary: ${rag.notes.summary}\\nKey concepts: ${(rag.notes.key_concepts || []).slice(0, 5).join(', ')}`,\n priority: 5\n }\n}];"
},
"id": "a1b2c3d4-0001-0001-0001-000000000010",
"name": "Format Gotify Message",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1552,
-200
]
},
{
"parameters": {
"message": "={{ $json.message }}",
"additionalFields": {
"priority": "={{ $json.priority }}",
"title": "={{ $json.title }}"
},
"options": {
"contentType": "text/plain"
}
},
"id": "a1b2c3d4-0001-0001-0001-000000000011",
"name": "Notify via Gotify",
"type": "n8n-nodes-base.gotify",
"typeVersion": 1,
"position": [
1776,
-200
],
"credentials": {
"gotifyApi": {
"id": "SETUP_REQUIRED",
"name": "Class Recordings"
}
}
},
{
"parameters": {
"jsCode": "const rag = $('Prepare for RAG').first().json;\nconst notes = rag.notes || {};\nconst concepts = (notes.key_concepts || []).map((c,i) => `${i+1}. ${c}`).join('\\n');\nconst defList = notes.definitions || [];\nconst defs = Array.isArray(defList)\n ? defList.map(d => `${d.term}: ${d.definition}`).join('\\n')\n : Object.entries(defList).map(([k,v]) => `${k}: ${v}`).join('\\n');\nconst assignments = (notes.assignments || []).map((a,i) => `${i+1}. ${a}`).join('\\n');\n\nconst content = [\n `Lecture Notes: ${rag.lectureTitle}`,\n `Class: ${rag.className}`,\n `Duration: ${rag.durationMinutes} minutes`,\n `Date: ${new Date().toISOString()}`, '',\n `Summary`, `=======`, notes.summary || '', '',\n `Key Concepts`, `=============`, concepts,\n ...(defs ? ['', `Definitions`, `===========`, defs] : []),\n ...(assignments ? ['', `Assignments`, `===========`, assignments] : []),\n '', `Full Transcript`, `================`, rag.fullText || '',\n].join('\\n');\n\nconst title = `${rag.className} \u2014 ${rag.lectureTitle}`;\nreturn [{\n binary: {\n document: {\n data: Buffer.from(content, 'utf-8').toString('base64'),\n mimeType: 'text/plain',\n fileName: `${title}.txt`\n }\n },\n json: { title, className: rag.className }\n}];"
},
"id": "a1b2c3d4-0001-0001-0001-000000000012",
"name": "Prepare Notes Doc",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1776,
200
]
},
{
"parameters": {
"method": "POST",
"url": "https://paperless.paccoco.com/api/documents/post_document/",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "=Token {{ $env.PAPERLESS_API_TOKEN }}"
}
]
},
"sendBody": true,
"contentType": "multipart-form-data",
"bodyParameters": {
"parameters": [
{
"parameterType": "formBinaryData",
"name": "document",
"inputDataFieldName": "document"
},
{
"name": "title",
"value": "={{ $json.title }}"
},
{
"name": "tags[]",
"value": "={{ $env.PAPERLESS_TAG_LECTURE_NOTES }}"
}
]
},
"options": {
"response": {
"response": {
"responseFormat": "text"
}
}
}
},
"id": "a1b2c3d4-0001-0001-0001-000000000013",
"name": "Save Notes to Paperless",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
2000,
200
],
"continueOnFail": true
}
],
"connections": {
"Class Ingest Webhook": {
"main": [
[
{
"node": "Extract Class Info",
"type": "main",
"index": 0
},
{
"node": "Respond Accepted",
"type": "main",
"index": 0
}
]
]
},
"Extract Class Info": {
"main": [
[
{
"node": "AI Extract Notes",
"type": "main",
"index": 0
}
]
]
},
"AI Extract Notes": {
"main": [
[
{
"node": "Prepare for RAG",
"type": "main",
"index": 0
}
]
]
},
"Prepare for RAG": {
"main": [
[
{
"node": "Chunk Text",
"type": "main",
"index": 0
}
]
]
},
"Chunk Text": {
"main": [
[
{
"node": "Get Embedding",
"type": "main",
"index": 0
}
]
]
},
"Get Embedding": {
"main": [
[
{
"node": "Prepare Qdrant Point",
"type": "main",
"index": 0
}
]
]
},
"Prepare Qdrant Point": {
"main": [
[
{
"node": "Upsert to Qdrant",
"type": "main",
"index": 0
}
]
]
},
"Upsert to Qdrant": {
"main": [
[
{
"node": "Format Gotify Message",
"type": "main",
"index": 0
},
{
"node": "Prepare Notes Doc",
"type": "main",
"index": 0
}
]
]
},
"Format Gotify Message": {
"main": [
[
{
"node": "Notify via Gotify",
"type": "main",
"index": 0
}
]
]
},
"Prepare Notes Doc": {
"main": [
[
{
"node": "Save Notes to Paperless",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1",
"binaryMode": "separate"
},
"staticData": null,
"meta": null,
"pinData": {},
"tags": [
{
"name": "school"
},
{
"name": "rag"
},
{
"name": "ai"
}
]
}

View File

@@ -0,0 +1,40 @@
name: rocinante
services:
whisper:
image: fedirz/faster-whisper-server:latest-cuda
container_name: rocinante-whisper
restart: unless-stopped
ports:
- "8787:8000"
environment:
WHISPER__MODEL: large-v3
WHISPER__INFERENCE_DEVICE: cuda
TZ: ${TZ}
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
- whisper-cache:/root/.cache/huggingface
watcher:
build: ./watcher
container_name: rocinante-watcher
restart: unless-stopped
environment:
WHISPER_URL: http://whisper:8000
N8N_WEBHOOK_URL: ${N8N_WEBHOOK_URL}
WATCH_DIR: /recordings
TZ: ${TZ}
volumes:
- C:/Recordings:/recordings
depends_on:
- whisper
volumes:
whisper-cache:

View File

@@ -0,0 +1,12 @@
FROM python:3.12-slim
RUN apt-get update && \
apt-get install -y --no-install-recommends ffmpeg && \
rm -rf /var/lib/apt/lists/*
RUN pip install --no-cache-dir watchdog requests
COPY watch.py /app/watch.py
WORKDIR /app
CMD ["python", "-u", "watch.py"]

213
rocinante/watcher/watch.py Normal file
View File

@@ -0,0 +1,213 @@
#!/usr/bin/env python3
"""
Class recording watcher for Rocinante.
Watches C:/Recordings (mounted as /recordings) for new .wav files.
For each file detected:
1. Waits for the file to finish being written
2. Converts WAV -> MP3 via ffmpeg (deletes original WAV)
3. Transcribes via local faster-whisper-server (large-v3, CUDA)
4. POSTs transcript + metadata to n8n /webhook/class/ingest
5. Deletes the MP3
Folder convention: subfolder name = class name
/recordings/HIST-2020/lecture-03-3.wav -> class=HIST-2020, title=lecture 03 3
"""
import os
import time
import subprocess
import logging
import requests
from pathlib import Path
from watchdog.observers.polling import PollingObserver as Observer
from watchdog.events import FileSystemEventHandler
# Config from environment
WHISPER_URL = os.environ.get("WHISPER_URL", "http://whisper:8000")
N8N_WEBHOOK = os.environ.get("N8N_WEBHOOK_URL", "https://n8n.paccoco.com/webhook/class/ingest")
WATCH_DIR = os.environ.get("WATCH_DIR", "/recordings")
WHISPER_MODEL = os.environ.get("WHISPER_MODEL", "large-v3")
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)-8s %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger("watcher")
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def wait_for_file_stable(path: Path, interval: float = 2.0, required_stable: int = 3) -> None:
"""Block until the file size stops changing (i.e. the copy is complete)."""
log.info(f"Waiting for {path.name} to finish writing...")
prev_size = -1
stable_count = 0
while stable_count < required_stable:
try:
size = path.stat().st_size
except FileNotFoundError:
time.sleep(interval)
continue
if size == prev_size and size > 0:
stable_count += 1
else:
stable_count = 0
prev_size = size
time.sleep(interval)
log.info(f"{path.name} is stable ({prev_size:,} bytes)")
def convert_to_mp3(wav_path: Path) -> Path:
mp3_path = wav_path.with_suffix(".mp3")
log.info(f"Converting to MP3: {wav_path.name}")
result = subprocess.run(
[
"ffmpeg", "-y",
"-i", str(wav_path),
"-codec:a", "libmp3lame",
"-qscale:a", "4",
str(mp3_path),
],
capture_output=True,
text=True,
)
if result.returncode != 0:
raise RuntimeError(f"ffmpeg failed:\n{result.stderr}")
wav_path.unlink()
log.info(f"Deleted original WAV: {wav_path.name}")
return mp3_path
def transcribe(mp3_path: Path) -> dict:
log.info(f"Transcribing {mp3_path.name} with {WHISPER_MODEL}...")
with open(mp3_path, "rb") as f:
resp = requests.post(
f"{WHISPER_URL}/v1/audio/transcriptions",
files={"file": (mp3_path.name, f, "audio/mpeg")},
data={
"model": WHISPER_MODEL,
"language": "en",
"response_format": "verbose_json",
},
timeout=7200, # 2 hours — large recordings on first load can be slow
)
resp.raise_for_status()
log.info("Transcription complete")
return resp.json()
def build_timestamped(segments: list) -> str:
lines = []
for s in segments:
mins = int(s.get("start", 0) // 60)
secs = int(s.get("start", 0) % 60)
lines.append(f"[{mins}:{secs:02d}] {s.get('text', '').strip()}")
return "\n".join(lines)
def send_to_n8n(class_name: str, lecture_title: str, result: dict) -> None:
segments = result.get("segments", [])
full_text = result.get("text", "").strip()
duration = result.get("duration") or (segments[-1].get("end", 0) if segments else 0)
payload = {
"class_name": class_name,
"lecture_title": lecture_title,
"transcript": full_text,
"timestamped_transcript": build_timestamped(segments),
"duration_seconds": duration,
}
log.info(f"Sending to n8n: {class_name} / {lecture_title} ({len(full_text)} chars)")
resp = requests.post(N8N_WEBHOOK, json=payload, timeout=300)
resp.raise_for_status()
log.info(f"n8n accepted — HTTP {resp.status_code}")
# ---------------------------------------------------------------------------
# Processing pipeline
# ---------------------------------------------------------------------------
def process_wav(wav_path: Path) -> None:
# Must be inside a class subfolder, not the root recordings dir
if wav_path.parent == Path(WATCH_DIR):
log.warning(f"Skipping {wav_path.name} — drop files into a class subfolder, e.g. /recordings/HIST-2020/")
return
class_name = wav_path.parent.name
lecture_title = wav_path.stem.replace("-", " ").replace("_", " ")
log.info(f"--- Processing: {class_name} / {lecture_title} ---")
wait_for_file_stable(wav_path)
mp3_path = convert_to_mp3(wav_path)
result = transcribe(mp3_path)
send_to_n8n(class_name, lecture_title, result)
log.info(f"--- Done: {class_name} / {lecture_title} ---")
# ---------------------------------------------------------------------------
# Watchdog handler
# ---------------------------------------------------------------------------
class WavHandler(FileSystemEventHandler):
def on_created(self, event):
if event.is_directory:
return
path = Path(event.src_path)
if path.suffix.lower() == ".wav":
log.info(f"Detected new WAV: {path}")
try:
process_wav(path)
except Exception as exc:
log.error(f"Failed to process {path}: {exc}", exc_info=True)
# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
def scan_existing(watch_dir: Path) -> None:
"""Process any .wav files already present at startup."""
existing = list(watch_dir.rglob("*.wav")) + list(watch_dir.rglob("*.WAV"))
if not existing:
return
log.info(f"Found {len(existing)} existing WAV(s) at startup — processing...")
for wav in existing:
try:
process_wav(wav)
except Exception as exc:
log.error(f"Failed to process {wav}: {exc}", exc_info=True)
if __name__ == "__main__":
watch_dir = Path(WATCH_DIR)
watch_dir.mkdir(parents=True, exist_ok=True)
log.info(f"Whisper URL : {WHISPER_URL}")
log.info(f"n8n webhook : {N8N_WEBHOOK}")
log.info(f"Watching : {watch_dir} (recursive)")
# Process any WAVs already sitting in the folder
scan_existing(watch_dir)
observer = Observer()
observer.schedule(WavHandler(), str(watch_dir), recursive=True)
observer.start()
log.info("Watcher started — drop a .wav into a class subfolder to begin")
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
pass
finally:
observer.stop()
observer.join()
log.info("Watcher stopped")