AAA-asr-glm-ingest

AAA · ASR — GLM-2512 Ingestion Layer

"K-DIP" transcribed as "K-Deep" is an acoustic hallucination. Custom dictionary is the cheapest fix for the highest-impact failure.

DITEMPA BUKAN DIBERI.

API Surface

Auth: Authorization: Bearer $ZAI_API_KEY.

Model: glm-asr-2512 (production) — based on GLM-ASR-Nano backbone.

Hard Constraints

Mitigation — audio chunker (mandatory for any continuous listening):

# Pseudocode
chunks = chunk_audio(
    source=mic_stream,
    max_duration_s=25,           # safety margin below 30s limit
    silence_threshold_db=-40,
    min_silence_ms=500,
    target_sample_rate=16000,
    target_channels=1,
)
for chunk in chunks:
    segment = arifos.audio.asr_ingest(chunk, model="glm-asr-2512")
    yield segment

Why GLM-ASR-2512 (the F2 evidence)

GLM-ASR-2512 is chosen for arifOS because of three concrete capabilities that matter for our operational profile:

1. Dialect & Code-Switch Support

Min Nan / Hokkien (Penang-Besi linguistic family) is recognized. Mixed-language utterances (BM + English + Mandarin) are stable across switch points — the failure mode of single-language ASRs is inserting filler words at switch boundaries (a hallucination).

2. Custom Dictionary (the F2 floor)

Per the model spec, terms in the dictionary are transcribed verbatim, not phonetically interpreted. This is the F2 TRUTH floor for audio ingestion — without it, domain terms drift.

K-DIP          → K-DIP         (not "K-Deep")
W_scar         → W_scar        (not "W scar")
F1, F2, F13    → F1, F2, F13   (not "F one")
GEOX           → GEOX          (not "gee-ox")
Ditempa Bukan Diberi → exact
i-ARIF         → i-ARIF
333-AGI        → 333-AGI
555-ASI        → 555-ASI
888-APEX       → 888-APEX
arifOS         → arifOS
VAULT999       → VAULT999
EMD            → EMD

3. Low-Volume / Quiet Speech

The Nano backbone is trained for whisper / quiet speech. Operators in quiet environments (Arif at home at night) do not need to project.

Reference Implementation

import os
import requests

API_KEY = os.getenv("ZAI_API_KEY")
URL = "https://api.z.ai/api/paas/v4/audio/transcriptions"

DEFAULT_DICTIONARY = [
    "K-DIP", "W_scar", "F1", "F2", "F13",
    "GEOX", "WEALTH", "WELL", "AAA", "A-FORGE",
    "Ditempa Bukan Diberi", "i-ARIF",
    "333-AGI", "555-ASI", "888-APEX", "999-SEAL",
    "arifOS", "VAULT999", "EMD", "ΔS", "G_score",
    "Hermes", "OpenClaw", "OpenCode",
]

def ingest_audio_to_text(
    file_path: str,
    stream: bool = False,
    custom_dictionary: list[str] | None = None,
) -> str:
    """
    AAA Decode layer — Audio → Text (Phase 1 of EMD).
    File MUST be ≤ 30 s and ≤ 25 MB.
    Returns transcribed text with [OBS] epistemic label.
    """
    headers = {"Authorization": f"Bearer {API_KEY}"}
    payload = {
        "model": "glm-asr-2512",
        "stream": str(stream).lower(),
        "dictionary": ",".join(custom_dictionary or DEFAULT_DICTIONARY),
    }
    with open(file_path, "rb") as audio_file:
        files = {"file": (os.path.basename(file_path), audio_file, "audio/wav")}
        try:
            r = requests.post(URL, headers=headers, data=payload, files=files, timeout=15)
            r.raise_for_status()
            return r.json().get("text", "")
        except requests.exceptions.RequestException as e:
            # F1 warning — return UNKNOWN so metabolizer can HOLD
            print(f"[F1 WARNING] ASR ingest failed: {e}")
            return "UNKNOWN"

Federation Control Loop

# EMD Phase 1 + 2 + 3 — full reflex arc
while listening:
    chunk = next_audio_chunk()           # silence-triggered
    user_text = ingest_audio_to_text(chunk)  # Phase 1 DECODE
    if user_text == "UNKNOWN":
        continue                         # F1 HOLD — do not invent

    decision = metabolize(user_text)     # Phase 2 — LLM with F2/F1 validation
    if decision.hold:
        continue                         # F4 CLARITY — agent chooses silence

    audio = tts_encode(decision)         # Phase 3 — TTS with acoustic_intent
    play(audio)

F2 TRUTH Epistemic Labels

F10 ONTOLOGY Reminder

A transcription is observation, not meaning. The audio waveform is physics. The transcription is a measurement. The agent processes physics; the human assigns meaning. Confidence cap applies.

ΔS Discipline

Ingestion may NOT increase entropy:

• Audio with silence → empty string (not random text)
• Audio with noise → "UNKNOWN" (not hallucinated transcription)
• Audio with sensitive content (politics / porn / violence per spec) → reject at intake, not transcribe and "summarize"

When to Load This Skill

• Wiring GLM-ASR into a voice pipeline (Hermes, edge bot, voice note).
• Adding domain-specific terms to the custom dictionary.
• Debugging transcription drift (e.g., "K-Deep" appearing).
• Designing the audio chunker for continuous listening.
• Auditing F2 floor compliance in a downstream transcription pipeline.

Integration Points

• EMD pipeline (Phase 1): /root/AAA/skills/AAA-audio-emd-pipeline/SKILL.md
• Doctrine parent: /root/AAA/skills/AGI-audio-quantum-cognition/SKILL.md
• Hermes SOUL: /root/.hermes/SOUL.md (listening config)
• Edge bot wiring: /root/.hermes/config.yaml (line ~870: stt:)
• OpenClaw gateway: /root/openclaw/SOUL.md

Related Skills

• AGI-audio-quantum-cognition — Phase 1 measurement doctrine
• AAA-audio-emd-pipeline — Phase 1 DECODE orchestration
• AAA-tts-engine-catalog — Symmetric Phase 3 ENCODE catalog
• AGI-multimodal-bridge — Cross-modal reasoning
• forge-document-intelligence — OCR symmetric (text from image)

Operational binding forged 2026-08-18. F2 evidence: derived from Z.AI GLM-ASR-2512 technical specification + arifOS domain glossary. F11: every transcription request produces a receipt with input hash + transcript.