| |
| """Tactical Radio Channel Simulator (TRCS) - Hugging Face Dataset & Manifest Generator. |
| |
| Generates self-contained sample audio fixtures and JSONL split manifests for the |
| Open TRCS Hugging Face dataset distribution: |
| - Split 1: `hallucination` (E1 Silence & Squelch Hallucination Benchmark) |
| - Split 2: `telephone_grid` (E2 Tactical Maritime Narrowband Codec Grid Benchmark) |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| from datetime import datetime, timezone |
| import hashlib |
| import json |
| import math |
| import os |
| import shutil |
| import subprocess |
| import sys |
| import tempfile |
| from pathlib import Path |
| from typing import Any, Dict, List, Optional, Tuple |
| import wave |
|
|
| import numpy as np |
| import scipy.io.wavfile as wavfile |
|
|
| PACKAGE_ROOT = Path(__file__).resolve().parent |
| REPO_ROOT = PACKAGE_ROOT.parents[1] |
| if str(REPO_ROOT) not in sys.path: |
| sys.path.insert(0, str(REPO_ROOT)) |
|
|
| from scripts.research.trcs_simulator import ( |
| TACTICAL_PROFILES, |
| apply_codec, |
| apply_tactical_channel, |
| ensure_mono, |
| generate_ctcss, |
| generate_noise, |
| generate_silence, |
| generate_squelch_click, |
| generate_squelch_tail, |
| mix_at_snr, |
| resample_audio, |
| ) |
|
|
| |
| TACTICAL_UTTERANCES: List[Dict[str, Any]] = [ |
| { |
| "id": "CG_01", |
| "text": "Coast Guard Sector Columbia River, this is Cutter Bertholf CG-7501, standing by on VHF channel 16 with Sierra Bravo.", |
| "callsigns": ["bertholf"], |
| "expected_entities": ["bertholf", "cg-7501", "16", "sierra", "bravo"], |
| }, |
| { |
| "id": "CG_02", |
| "text": "Cutter Munro CG-755, Sector San Juan, acknowledge Mayday Relay from vessel Alpha Tango 4.", |
| "callsigns": ["munro"], |
| "expected_entities": ["munro", "cg-755", "alpha", "tango", "4"], |
| }, |
| { |
| "id": "CG_03", |
| "text": "Coast Guard Rescue 6033, Cutter Kimball CG-756, establish comms on tactical channel 22 Alpha.", |
| "callsigns": ["kimball"], |
| "expected_entities": ["6033", "kimball", "cg-756", "22", "alpha"], |
| }, |
| { |
| "id": "CG_04", |
| "text": "Cutter Stratton CG-752, Sector Puget Sound, intercepting target vessel Yankee Zulu 89 in position Mike Charlie.", |
| "callsigns": ["stratton"], |
| "expected_entities": ["stratton", "cg-752", "yankee", "zulu", "89", "mike", "charlie"], |
| }, |
| { |
| "id": "CG_05", |
| "text": "Cutter Waesche CG-751, Sector San Francisco, deploying rescue boat Charlie 1 with four crew.", |
| "callsigns": ["waesche"], |
| "expected_entities": ["waesche", "cg-751", "charlie", "1", "four"], |
| }, |
| { |
| "id": "CG_06", |
| "text": "Sector Boston, Cutter Spencer CG-905, departing Boston Harbor for offshore patrol area Delta Echo.", |
| "callsigns": ["spencer"], |
| "expected_entities": ["boston", "spencer", "cg-905", "delta", "echo"], |
| }, |
| { |
| "id": "CG_07", |
| "text": "Cutter Stone CG-758, Sector Charleston, requesting weather update for position Foxtrot Golf 12.", |
| "callsigns": ["stone"], |
| "expected_entities": ["stone", "cg-758", "charleston", "foxtrot", "golf", "12"], |
| }, |
| { |
| "id": "CG_08", |
| "text": "Sector Miami, Cutter Hamilton CG-753, en route to distress beacon Hotel India 44 on VHF 16.", |
| "callsigns": ["hamilton"], |
| "expected_entities": ["miami", "hamilton", "cg-753", "hotel", "india", "44", "16"], |
| }, |
| { |
| "id": "CG_09", |
| "text": "Cutter Midgett CG-757, Sector Honolulu, visual contact with fishing vessel Juliett Kilo 7.", |
| "callsigns": ["midgett"], |
| "expected_entities": ["midgett", "cg-757", "honolulu", "juliett", "kilo", "7"], |
| }, |
| { |
| "id": "CG_10", |
| "text": "Coast Guard Sector Key West, Cutter Mohawk CG-913, boarding motor vessel Lima Mike 303.", |
| "callsigns": ["mohawk"], |
| "expected_entities": ["mohawk", "cg-913", "key", "west", "lima", "mike", "303"], |
| }, |
| { |
| "id": "CG_11", |
| "text": "Sector Mobile, Cutter Alert CG-630, standing by on channel 21 Alpha near buoy November Oscar.", |
| "callsigns": ["alert"], |
| "expected_entities": ["mobile", "alert", "cg-630", "21", "alpha", "november", "oscar"], |
| }, |
| { |
| "id": "CG_12", |
| "text": "Cutter Bear CG-901, Sector Virginia, conducting search pattern Papa Quebec 5 at 12 knots.", |
| "callsigns": ["bear"], |
| "expected_entities": ["bear", "cg-901", "virginia", "papa", "quebec", "5", "12"], |
| }, |
| { |
| "id": "CG_13", |
| "text": "Sector San Diego, Cutter Active CG-618, rendezvous with patrol boat Romeo Sierra 8.", |
| "callsigns": ["active"], |
| "expected_entities": ["active", "cg-618", "san", "diego", "romeo", "sierra", "8"], |
| }, |
| { |
| "id": "CG_14", |
| "text": "Cutter Forward CG-911, Sector Delaware Bay, escorting tanker Tango Uniform 99 into channel.", |
| "callsigns": ["forward"], |
| "expected_entities": ["forward", "cg-911", "delaware", "bay", "tango", "uniform", "99"], |
| }, |
| { |
| "id": "CG_15", |
| "text": "Sector St. Petersburg, Cutter Resolute CG-620, secure towing operation for vessel Victor Whiskey 2.", |
| "callsigns": ["resolute"], |
| "expected_entities": ["resolute", "cg-620", "victor", "whiskey", "2"], |
| }, |
| { |
| "id": "CG_16", |
| "text": "Cutter Harriet Lane CG-903, Sector North Carolina, investigating radar contact X-ray Yankee 15.", |
| "callsigns": ["harriet lane"], |
| "expected_entities": ["harriet", "lane", "cg-903", "x-ray", "yankee", "15"], |
| }, |
| { |
| "id": "CG_17", |
| "text": "Sector Houston-Galveston, Cutter Dauntless CG-624, all clear at waypoint Zulu Alpha 01.", |
| "callsigns": ["dauntless"], |
| "expected_entities": ["dauntless", "cg-624", "houston", "zulu", "alpha", "01"], |
| }, |
| { |
| "id": "CG_18", |
| "text": "Coast Guard Rescue 6514, Cutter Seneca CG-905, survivor safely hoisted on channel 23 Alpha.", |
| "callsigns": ["seneca"], |
| "expected_entities": ["6514", "seneca", "cg-905", "23", "alpha"], |
| }, |
| { |
| "id": "CG_19", |
| "text": "Sector Jacksonville, Cutter Valiant CG-621, proceeding to coordinates Bravo Charlie 42.", |
| "callsigns": ["valiant"], |
| "expected_entities": ["valiant", "cg-621", "jacksonville", "bravo", "charlie", "42"], |
| }, |
| { |
| "id": "CG_20", |
| "text": "Cutter Legare CG-912, Sector New York, maintaining maritime security zone Delta Echo 8.", |
| "callsigns": ["legare"], |
| "expected_entities": ["legare", "cg-912", "new", "york", "delta", "echo", "8"], |
| }, |
| { |
| "id": "CG_21", |
| "text": "Sector Corpus Christi, Cutter Diligence CG-616, intercepting go-fast vessel Foxtrot Golf 99.", |
| "callsigns": ["diligence"], |
| "expected_entities": ["diligence", "cg-616", "corpus", "christi", "foxtrot", "golf", "99"], |
| }, |
| { |
| "id": "CG_22", |
| "text": "Cutter Tampa CG-902, Sector Key West, interdicting migrant craft Hotel India 5.", |
| "callsigns": ["tampa"], |
| "expected_entities": ["tampa", "cg-902", "key", "west", "hotel", "india", "5"], |
| }, |
| { |
| "id": "CG_23", |
| "text": "Sector Los Angeles-Long Beach, Cutter Steadfast CG-623, tracking sailboat Juliett Kilo 18.", |
| "callsigns": ["steadfast"], |
| "expected_entities": ["steadfast", "cg-623", "juliett", "kilo", "18"], |
| }, |
| { |
| "id": "CG_24", |
| "text": "Cutter Campbell CG-909, Sector Boston, responding to EPIRB transmission Lima Mike 77.", |
| "callsigns": ["campbell"], |
| "expected_entities": ["campbell", "cg-909", "boston", "lima", "mike", "77"], |
| }, |
| { |
| "id": "CG_25", |
| "text": "Sector Anchorage, Icebreaker Healy WAGB-20, opening lead through sea ice near point November Oscar 3.", |
| "callsigns": ["healy"], |
| "expected_entities": ["healy", "wagb-20", "anchorage", "november", "oscar", "3"], |
| }, |
| { |
| "id": "CG_26", |
| "text": "Icebreaker Polar Star WAGB-10, Sector Juneau, navigating McMurdo channel at 8 knots.", |
| "callsigns": ["polar star"], |
| "expected_entities": ["polar", "star", "wagb-10", "juneau", "8"], |
| }, |
| { |
| "id": "CG_27", |
| "text": "Sector Guam, Cutter Alex Haley CG-39, underway for search and rescue area Papa Quebec 22.", |
| "callsigns": ["alex haley"], |
| "expected_entities": ["alex", "haley", "cg-39", "guam", "papa", "quebec", "22"], |
| }, |
| { |
| "id": "CG_28", |
| "text": "Cutter Decisive CG-629, Sector Mobile, anchor aweigh departing fairway Romeo Sierra 4.", |
| "callsigns": ["decisive"], |
| "expected_entities": ["decisive", "cg-629", "mobile", "romeo", "sierra", "4"], |
| }, |
| { |
| "id": "CG_29", |
| "text": "Sector Humboldt Bay, Cutter Dependable CG-626, hazardous bar crossing condition Tango Uniform 1.", |
| "callsigns": ["dependable"], |
| "expected_entities": ["dependable", "cg-626", "tango", "uniform", "1"], |
| }, |
| { |
| "id": "CG_30", |
| "text": "Cutter Confidence CG-619, Sector Maryland-NCR, safety zone established around Victor Whiskey 9.", |
| "callsigns": ["confidence"], |
| "expected_entities": ["confidence", "cg-619", "victor", "whiskey", "9"], |
| }, |
| { |
| "id": "CG_31", |
| "text": "Sector Puget Sound, Cutter Reliance CG-615, passing Point Wilson outbound channel 13.", |
| "callsigns": ["reliance"], |
| "expected_entities": ["reliance", "cg-615", "wilson", "13"], |
| }, |
| { |
| "id": "CG_32", |
| "text": "Cutter Vigorous CG-627, Sector Virginia, boarding commercial trawler X-ray Yankee 55.", |
| "callsigns": ["vigorous"], |
| "expected_entities": ["vigorous", "cg-627", "virginia", "x-ray", "yankee", "55"], |
| }, |
| { |
| "id": "CG_33", |
| "text": "Sector Boston, Cutter Escanaba CG-907, rendezvous with harbor pilot at station Zulu Alpha 10.", |
| "callsigns": ["escanaba"], |
| "expected_entities": ["escanaba", "cg-907", "boston", "zulu", "alpha", "10"], |
| }, |
| { |
| "id": "CG_34", |
| "text": "Cutter Tahoma CG-908, Sector New York, completing security sweep of Bravo Charlie 6.", |
| "callsigns": ["tahoma"], |
| "expected_entities": ["tahoma", "cg-908", "bravo", "charlie", "6"], |
| }, |
| { |
| "id": "CG_35", |
| "text": "Sector St. Petersburg, Cutter Thetis CG-910, towing disabled vessel Delta Echo 23.", |
| "callsigns": ["thetis"], |
| "expected_entities": ["thetis", "cg-910", "delta", "echo", "23"], |
| }, |
| { |
| "id": "CG_36", |
| "text": "Cutter Northland CG-904, Sector Charleston, resuming offshore patrol sector Foxtrot Golf 7.", |
| "callsigns": ["northland"], |
| "expected_entities": ["northland", "cg-904", "charleston", "foxtrot", "golf", "7"], |
| }, |
| { |
| "id": "CG_37", |
| "text": "Sector Miami, Response Boat Medium 45601, on scene with overturned skiff Hotel India 88.", |
| "callsigns": ["rbm-45601"], |
| "expected_entities": ["45601", "miami", "hotel", "india", "88"], |
| }, |
| { |
| "id": "CG_38", |
| "text": "Coast Guard Defender 25, Sector Key West, high speed pursuit of contact Juliett Kilo 14.", |
| "callsigns": ["defender-25"], |
| "expected_entities": ["25", "key", "west", "juliett", "kilo", "14"], |
| }, |
| { |
| "id": "CG_39", |
| "text": "Sector Columbia River, Rescue 6042, hoist operations completed at coordinates Lima Mike 50.", |
| "callsigns": ["rescue-6042"], |
| "expected_entities": ["6042", "columbia", "river", "lima", "mike", "50"], |
| }, |
| { |
| "id": "CG_40", |
| "text": "Cutter Bertholf CG-7501, Sector San Juan, mission accomplished, returning to base on VHF 16.", |
| "callsigns": ["bertholf"], |
| "expected_entities": ["bertholf", "cg-7501", "san", "juan", "16"], |
| }, |
| ] |
|
|
| CODECS_GRID: List[Tuple[str, str, int]] = [ |
| ("none", "Clean 16kHz Control", 16000), |
| ("g711_mulaw", "G.711 mu-law (8kHz)", 8000), |
| ("g711_alaw", "G.711 A-law (8kHz)", 8000), |
| ("amr_nb", "AMR-NB 12.2k (8kHz)", 8000), |
| ("gsm_fr", "GSM-FR 13k (8kHz)", 8000), |
| ("opus_nb", "Opus NB 12k (8kHz)", 8000), |
| ] |
|
|
| SNR_GRID: List[Tuple[Optional[float], str]] = [ |
| (None, "clean"), |
| (20.0, "snr20"), |
| (10.0, "snr10"), |
| (0.0, "snr0"), |
| ] |
|
|
| HALLUCINATION_CONDITIONS = [ |
| "digital_silence", |
| "ambient_silence", |
| "squelch_burst", |
| "noise_white", |
| "noise_pink", |
| "noise_brown", |
| "noise_engine", |
| "noise_babble", |
| "speech_control", |
| ] |
|
|
|
|
| def synthesize_clean_speech(text: str, sample_rate: int = 16000) -> np.ndarray: |
| """Synthesize clean speech via espeak-ng, falling back to harmonic formant synthesis.""" |
| espeak_bin = shutil.which("espeak-ng") or shutil.which("espeak") |
| if espeak_bin is not None: |
| try: |
| with tempfile.TemporaryDirectory() as td: |
| wav_path = os.path.join(td, "speech.wav") |
| cmd = [espeak_bin, "-v", "en-us", "-s", "155", "-w", wav_path, text] |
| subprocess.run(cmd, check=True, capture_output=True) |
| sr, data = wavfile.read(wav_path) |
| audio = ensure_mono(data) |
| return resample_audio(audio, sr, sample_rate) |
| except Exception: |
| pass |
|
|
| |
| duration_sec = max(1.5, len(text.split()) * 0.4) |
| n_samples = int(duration_sec * sample_rate) |
| t = np.arange(n_samples, dtype=np.float32) / float(sample_rate) |
| sig = np.zeros(n_samples, dtype=np.float32) |
| f0 = 130.0 |
| for h in range(1, 12): |
| sig += (0.5 / h) * np.sin(2.0 * np.pi * f0 * h * t) |
| |
| envelope = 0.5 * (1.0 + np.sin(2.0 * np.pi * 3.5 * t)) |
| sig = (sig * envelope).astype(np.float32) |
| sig = sig / max(1e-6, np.max(np.abs(sig))) * 0.6 |
| return sig |
|
|
|
|
| def build_hallucination_clip(condition: str, duration_sec: float, sample_rate: int, seed: int) -> np.ndarray: |
| """Build acoustic clip for E1 hallucination split.""" |
| if condition == "digital_silence": |
| return generate_silence(duration_sec, sample_rate, kind="digital") |
| elif condition == "ambient_silence": |
| return generate_silence(duration_sec, sample_rate, kind="ambient", ambient_dbfs=-55.0, seed=seed) |
| elif condition == "squelch_burst": |
| burst = generate_squelch_tail(duration_sec=min(duration_sec, 0.12), sample_rate=sample_rate, burst_type="bandpass", amplitude=0.4, seed=seed) |
| pad_len = max(0, int(duration_sec * sample_rate) - len(burst)) |
| left = pad_len // 2 |
| right = pad_len - left |
| return np.pad(burst, (left, right)) |
| elif condition.startswith("noise_"): |
| n_kind = condition.replace("noise_", "") |
| return generate_noise(duration_sec, sample_rate, kind=n_kind, amplitude=0.25, seed=seed) |
| elif condition == "speech_control": |
| control_text = "Coast Guard Sector Seattle, this is rescue craft Alpha 1 on channel 16." |
| audio = synthesize_clean_speech(control_text, sample_rate=sample_rate) |
| target_len = int(duration_sec * sample_rate) |
| if len(audio) < target_len: |
| audio = np.pad(audio, (0, target_len - len(audio))) |
| else: |
| audio = audio[:target_len] |
| return audio |
| else: |
| raise ValueError(f"Unknown condition: {condition}") |
|
|
|
|
| def generate_dataset( |
| output_dir: Path, |
| num_e1_seeds: int = 10, |
| num_e2_utterances: int = 40, |
| sample_rate: int = 16000, |
| ) -> Tuple[Path, Path]: |
| """Generate audio files and manifest files for Hugging Face TRCS dataset.""" |
| output_dir = Path(output_dir).resolve() |
| manifests_dir = output_dir / "manifests" |
| audio_dir = output_dir / "audio" |
| data_dir = output_dir / "data" |
| data_hal_dir = data_dir / "hallucination" |
| data_grid_dir = data_dir / "telephone_grid" |
| data_default_dir = data_dir / "default" |
|
|
| for d in [manifests_dir, audio_dir, data_hal_dir, data_grid_dir, data_default_dir]: |
| d.mkdir(parents=True, exist_ok=True) |
|
|
| |
| for old_wav in audio_dir.glob("*.wav"): |
| try: |
| old_wav.unlink() |
| except OSError: |
| pass |
|
|
| e1_manifest_path = manifests_dir / "e1_hallucination.jsonl" |
| e2_manifest_path = manifests_dir / "e2_telephone_grid.jsonl" |
| data_hal_path = data_hal_dir / "test.jsonl" |
| data_grid_path = data_grid_dir / "test.jsonl" |
| data_default_path = data_default_dir / "test.jsonl" |
|
|
| print(f"Generating TRCS Hugging Face dataset in {output_dir}...") |
|
|
| |
| e1_records = [] |
| e1_duration = 2.5 |
| for cond in HALLUCINATION_CONDITIONS: |
| for s_idx in range(num_e1_seeds): |
| seed = 100 + s_idx |
| clip_id = f"e1_{cond}_s{seed}" |
| wav_filename = f"{clip_id}.wav" |
| wav_path = audio_dir / wav_filename |
|
|
| audio_data = build_hallucination_clip(cond, e1_duration, sample_rate, seed) |
| pcm16 = np.clip(audio_data * 32767.0, -32768.0, 32767.0).astype(np.int16) |
| wavfile.write(str(wav_path), sample_rate, pcm16) |
|
|
| is_speech = (cond == "speech_control") |
| ref_text = "Coast Guard Sector Seattle, this is rescue craft Alpha 1 on channel 16." if is_speech else "" |
| entities = ["seattle", "alpha", "1", "16"] if is_speech else [] |
|
|
| record = { |
| "id": clip_id, |
| "split": "hallucination", |
| "condition": cond, |
| "codec": "none", |
| "snr_db": None, |
| "duration_sec": float(e1_duration), |
| "is_speech": is_speech, |
| "text": ref_text, |
| "entities": entities, |
| "audio_path": f"audio/{wav_filename}", |
| "seed": seed, |
| } |
| e1_records.append(record) |
|
|
| |
| e2_records = [] |
| target_utterances = TACTICAL_UTTERANCES[:num_e2_utterances] |
| clean_audio_cache = {} |
| for u in target_utterances: |
| clean_audio_cache[u["id"]] = synthesize_clean_speech(u["text"], sample_rate=sample_rate) |
|
|
| rng = np.random.default_rng(42) |
| for u in target_utterances: |
| clean_audio = clean_audio_cache[u["id"]] |
| for codec_key, codec_name, _ in CODECS_GRID: |
| codec_audio = apply_codec(clean_audio, sample_rate, codec=codec_key, return_sample_rate=sample_rate) |
|
|
| for snr_val, snr_label in SNR_GRID: |
| cond_id = f"{codec_key}_{snr_label}" |
| clip_id = f"e2_{u['id'].lower()}_{cond_id}" |
| wav_filename = f"{clip_id}.wav" |
| wav_path = audio_dir / wav_filename |
|
|
| if snr_val is not None: |
| dur = len(codec_audio) / float(sample_rate) |
| noise = generate_noise(dur, sample_rate=sample_rate, kind="pink", seed=int(rng.integers(0, 1000000))) |
| degraded, _ = mix_at_snr(codec_audio, noise, target_snr_db=snr_val) |
| else: |
| degraded = np.copy(codec_audio) |
|
|
| pcm16 = np.clip(degraded * 32767.0, -32768.0, 32767.0).astype(np.int16) |
| wavfile.write(str(wav_path), sample_rate, pcm16) |
|
|
| record = { |
| "id": clip_id, |
| "split": "telephone_grid", |
| "utterance_id": u["id"], |
| "condition": cond_id, |
| "codec": codec_key, |
| "snr_db": float(snr_val) if snr_val is not None else None, |
| "duration_sec": float(len(degraded) / sample_rate), |
| "is_speech": True, |
| "text": u["text"], |
| "callsigns": u.get("callsigns", []), |
| "entities": u["expected_entities"], |
| "audio_path": f"audio/{wav_filename}", |
| } |
| e2_records.append(record) |
|
|
| |
| with open(e1_manifest_path, "w", encoding="utf-8") as f1, open(data_hal_path, "w", encoding="utf-8") as f2: |
| for r in e1_records: |
| line = json.dumps(r) + "\n" |
| f1.write(line) |
| f2.write(line) |
| print(f"✓ Wrote {len(e1_records)} E1 hallucination records to {e1_manifest_path} and {data_hal_path}") |
|
|
| with open(e2_manifest_path, "w", encoding="utf-8") as f1, open(data_grid_path, "w", encoding="utf-8") as f2: |
| for r in e2_records: |
| line = json.dumps(r) + "\n" |
| f1.write(line) |
| f2.write(line) |
| print(f"✓ Wrote {len(e2_records)} E2 telephone grid records to {e2_manifest_path} and {data_grid_path}") |
|
|
| |
| all_records = e2_records + e1_records |
| with open(data_default_path, "w", encoding="utf-8") as f: |
| for r in all_records: |
| f.write(json.dumps(r) + "\n") |
| print(f"✓ Wrote {len(all_records)} default split records to {data_default_path}") |
|
|
| |
| prov_files = [] |
| total_audio_duration_sec = 0.0 |
| total_bytes = 0 |
| prov_path = output_dir / "provenance_manifest.json" |
|
|
| for r in sorted(all_records, key=lambda x: x["audio_path"]): |
| abs_p = output_dir / r["audio_path"] |
| raw_bytes = abs_p.read_bytes() |
| sha256 = hashlib.sha256(raw_bytes).hexdigest() |
| sz = len(raw_bytes) |
| total_bytes += sz |
|
|
| with wave.open(str(abs_p), "rb") as w: |
| sr = w.getframerate() |
| n_ch = w.getnchannels() |
| n_frames = w.getnframes() |
| dur = round(n_frames / float(sr), 2) |
| total_audio_duration_sec += dur |
|
|
| |
| if r["split"] == "hallucination": |
| c = r["condition"] |
| if c == "digital_silence": |
| spec = "Digital Zero PCM Silence (ITU-T G.711 / AES67 silence reference)" |
| elif c == "ambient_silence": |
| spec = "TRCS Acoustic Synthesizer (DSP/Additive Noise Mixing)" |
| elif c == "squelch_burst": |
| spec = "TRCS Tactical VHF Carrier Squelch Tail (EIA/TIA-603)" |
| elif c == "speech_control": |
| spec = "TRCS Clean Reference Control (16 kHz Linear PCM)" |
| else: |
| spec = f"TRCS Acoustic Synthesizer (DSP/Additive Noise Mixing: {c})" |
| else: |
| codec = r.get("codec", "none") |
| if codec == "none": |
| spec = "Clean Reference Linear PCM (16 kHz, 16-bit Mono)" |
| elif codec == "g711_mulaw": |
| spec = "ITU-T G.711 mu-law PCM (8 kHz, 64 kbps Narrowband)" |
| elif codec == "g711_alaw": |
| spec = "ITU-T G.711 A-law PCM (8 kHz, 64 kbps Narrowband)" |
| elif codec == "amr_nb": |
| spec = "3GPP AMR-NB (8 kHz, 12.2 kbps Mode 7 ETSI TS 126 071)" |
| elif codec == "gsm_fr": |
| spec = "ETSI GSM 06.10 Full Rate (8 kHz, 13 kbps Telephony)" |
| elif codec == "opus_nb": |
| spec = "IETF RFC 6716 Opus Narrowband (8 kHz, 12 kbps VOIP Mode)" |
| else: |
| spec = "Narrowband Codec Simulation" |
|
|
| entry = { |
| "filename": Path(r["audio_path"]).name, |
| "relative_path": r["audio_path"], |
| "sha256": sha256, |
| "size_bytes": sz, |
| "duration_sec": dur, |
| "sample_rate": sr, |
| "channels": n_ch, |
| "split": r["split"], |
| "condition": r["condition"], |
| "codec": r.get("codec", "none"), |
| "snr_db": r.get("snr_db"), |
| "standard_specification": spec, |
| "text": r["text"], |
| "entities": r.get("entities", []), |
| } |
| prov_files.append(entry) |
|
|
| prov_data = { |
| "schema_version": "1.0.0", |
| "title": "Open TRCS Benchmark Audio Provenance & Cryptographic Ledger", |
| "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"), |
| "license": "Code: Apache-2.0. Audio fixtures: CC-BY-4.0 / Public Domain US Government tactical transcripts.", |
| "total_files": len(prov_files), |
| "splits_summary": { |
| "hallucination": len(e1_records), |
| "telephone_grid": len(e2_records), |
| }, |
| "total_audio_duration_sec": round(total_audio_duration_sec, 2), |
| "total_bytes": total_bytes, |
| "files": prov_files, |
| } |
| with open(prov_path, "w", encoding="utf-8") as f: |
| json.dump(prov_data, f, indent=2) |
| print(f"✓ Wrote provenance manifest ({len(prov_files)} files) to {prov_path}") |
|
|
| return e1_manifest_path, e2_manifest_path |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser(description="TRCS Hugging Face dataset generator") |
| parser.add_argument("--output-dir", type=str, default="data/trcs_hf", help="Base directory for HF dataset") |
| parser.add_argument("--num-e1-seeds", type=int, default=10, help="Number of seeds per E1 condition") |
| parser.add_argument("--num-e2-utterances", type=int, default=40, help="Number of tactical utterances for E2 grid") |
| parser.add_argument("--sample-rate", type=int, default=16000, help="Audio sample rate (Hz)") |
| args = parser.parse_args() |
|
|
| out_dir = Path(args.output_dir) |
| generate_dataset( |
| output_dir=out_dir, |
| num_e1_seeds=args.num_e1_seeds, |
| num_e2_utterances=args.num_e2_utterances, |
| sample_rate=args.sample_rate, |
| ) |
| print("Dataset generation complete!") |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| sys.exit(main()) |
|
|
