"""Build a bounded, offline, audited HF-ready pilot release; never publishes. Only explicit source allowlists are used. No microphone/session directory, Robo reference voiceprint, raw audio, third-party model weights or caches enter the release. Existing release directories are verified, never overwritten. """ from __future__ import annotations import argparse import hashlib import importlib.metadata import json from pathlib import Path import re import shutil import sys import zipfile import numpy as np try: from .publish_pilot import audit_release except ImportError: from publish_pilot import audit_release HERE = Path(__file__).resolve().parent ROOT = HERE.parent BUNDLE = Path('/home/c4r33u19/.cache/robo-tyger/hub/models--nineninesix--tyger/snapshots/a5d4d73bb74fa4a7082dcd4387e44a1e5ac151b6') CODE = ['__init__.py','models.py','inference.py','train.py','evaluate.py','select.py', 'compile_report.py','prepare_pilot.py','prepare_emotion_pilot.py','audit_pilot.py', 'test_dataset_pipeline.py','test_predictor.py'] REPORTS = ['report.html','context_ablation_test.json','ood_centered_v2.json','ood_v1_v2.json'] RUNS = ['results','results_centered','results_v2_raw','results_v2_centered','results_v2_gru'] MODEL_KEYS = {'model','config','seed','epoch','validation','activity_threshold','format_version', 'feature_source','teacher','warning','verification_threshold','test', 'selection_validation','selection_protocol'} NPZ_KEYS = {'features','frame_end_s','speaker_idx','valid_mask','speech_fraction','ambiguity'} def write(path, content): path.parent.mkdir(parents=True, exist_ok=True) path.write_text(content) def dump(path, value): write(path, json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False)) def clean(value): """Retain public source provenance but strip machine-local cache locations.""" if isinstance(value, list): return [clean(v) for v in value] if isinstance(value, dict): out = {} for key, item in value.items(): if key in {'source_cache'}: continue if key == 'path' and isinstance(item, str) and item.startswith('/'): out['source_audio_not_included'] = True continue out[key] = clean(item) return out if isinstance(value, str): value = value.replace(str(ROOT)+'/', '').replace(str(HERE)+'/', '') value = re.sub(r'/home/[^/\s"<>]+/[^\s"<>]*', '[local-cache]', value) value = re.sub(r'/mnt/nvme/[^\s"<>]*', '[local-workspace]', value) return value def copy_text(source, destination): if source.suffix == '.json': dump(destination, clean(json.loads(source.read_text()))) else: write(destination, clean(source.read_text())) def add_licenses(destination, model=False): # Existing grants are copied, not invented. Code and weights/data are distinct. cc = ROOT.parent/'repository-update'/'Humaneness-Voice'/'LICENSE' shutil.copy2(cc, destination/'LICENSE-CC-BY-4.0.txt') shutil.copy2(ROOT/'LICENSE', destination/'LICENSE-APACHE-2.0.txt') third = destination/'third_party' third.mkdir() for name in ('LICENSE.md','NOTICE.md'): shutil.copy2(BUNDLE/name, third/('TYGER-'+name)) shutil.copy2(BUNDLE/'licenses'/'OpenMDW-1.1.txt', third/'OpenMDW-1.1.txt') write(destination/'NOTICE', '''TYGER V2 public-synthetic research pilot, 2026. Original integration and predictor code: Apache License 2.0. Selected public-synthetic derived features, provenance and our original trained student weights: Creative Commons Attribution 4.0 International. Credit LAION, Humaneness Labs, and Christoph Schuhmann; these are new derived research artifacts. Source speech: Christoph Schuhmann and LAION, laion/laion-voice-profiles-annotated, revision 88ea857d6a6f20839674dfde30936d8fd6b23c62, CC BY 4.0. Audio is not included. Frozen feature extractor: nineninesix/tyger, revision a5d4d73bb74fa4a7082dcd4387e44a1e5ac151b6; Nemotron source revision ea30d66debe3740a08b573244286791d423d6b3e; OpenMDW 1.1. The original TYGER/NVIDIA license and origin notices are retained under third_party. OpenMDW 1.1 explicitly leaves sharing model outputs unrestricted. No TYGER or Nemotron parameters/configuration are relicensed under CC BY 4.0 or included here. Teacher: speechbrain/spkrec-ecapa-voxceleb, revision 0f99f2d0ebe89ac095bcc5903c4dd8f72b367286, separate upstream Apache 2.0 model/code terms. No teacher or classifier weights are included. Public synthetic teacher outputs are included for reproducibility, not private enrolled-user voiceprints. No original TYGER GRU, TTS weights, microphone recordings, camera frames, uploaded reference audio, session identity memory, or enrolled-user embeddings are included. Student cues are not secure authentication. Pilot reliability checks failed; default identity filtering remains OFF. ''') def add_code(destination): code = destination/'code'/'speaker_v2' code.mkdir(parents=True) for name in CODE: shutil.copy2(HERE/name, code/name) write(destination/'code'/'LICENSE', (ROOT/'LICENSE').read_text()) versions = {name: importlib.metadata.version(name) for name in ['numpy','torch','torchaudio','safetensors','huggingface_hub','requests','scipy','pyarrow','speechbrain']} dump(destination/'environment.json', versions) write(destination/'requirements.txt', '# Exact observed CUDA build suffixes are recorded in environment.json.\n' '# Select an appropriate official PyTorch CPU/CUDA package index locally.\n' +'\n'.join(f'{k}=={v.split("+")[0]}' for k,v in versions.items())+'\n') def add_reports(destination): reports = destination/'reports' reports.mkdir() for name in REPORTS: copy_text(HERE/name, reports/name) for folder in RUNS: for name in ['results.json','report.html']: copy_text(HERE/folder/name, reports/folder/name) def dataset_release(destination): destination.mkdir() add_code(destination) add_licenses(destination) copy_text(HERE/'DATASET_CARD.md', destination/'METHODS.md') counts = {} for source_name, release_name in [('pilot','v1'),('pilot_v2','v2')]: source, target = HERE/source_name, destination/'data'/release_name target.mkdir(parents=True) original = json.loads((source/'manifest.json').read_text()) assert len(original['conversations']) == 64 and len(original['speakers']) == 32 for record in original['conversations']: relative = Path(record['path']) assert not relative.is_absolute() and '..' not in relative.parts src, dest = source/relative, target/relative with np.load(src, allow_pickle=False) as f: assert set(f.files) == NPZ_KEYS assert f['features'].shape == (300,1024) and np.isfinite(f['features']).all() dest.parent.mkdir(parents=True, exist_ok=True) shutil.copy2(src, dest) copy_text(src.with_suffix('.json'), dest.with_suffix('.json')) for name in ['manifest.json','speakers.json','audit.json','teacher_quality.json']: copy_text(source/name, target/name) for name in ['teacher_embeddings.npy','probe_teacher_embeddings.npz']: shutil.copy2(source/name, target/name) counts[release_name] = {'conversations':64, 'decision_hops':4800, 'audio_seconds':1536, 'identities':32, 'feature_dimensions':1024} copy_text(HERE/'pilot'/'global_split.json', destination/'global_split.json') copy_text(HERE/'pilot_v2'/'retrieval.json', destination/'v2_retrieval.json') dump(destination/'summary.json', counts) write(destination/'README.md', '''--- license: cc-by-4.0 language: [de, en] tags: [speaker-verification, synthetic, tyger, nemotron, experimental, features] pretty_name: TYGER V2 public-synthetic speaker predictor pilot --- # TYGER V2: public-synthetic frozen-feature pilot **Research artifact; failed reliability gate. Not an authentication dataset.** Two 32-identity corpora each contain 64 procedural 24-second conversations and 4,800 decision hops of 320 ms. Each has 24 train, 4 validation and 4 test identities, with no speaker overlap. The global 480/10/10 split is retained for future work. Only public synthetic source-derived features/provenance are released: no raw audio, microphone recordings, private uploads or enrolled-user voiceprints. ## Files and use `data/v1/` and `data/v2/` contain the training-compatible manifests, compact conversation NPZ arrays, normalized public-synthetic ECAPA teacher targets and full-utterance teacher probes. Every NPZ has Nemotron layer 15 features `[T,1024]` at 80 ms, frame times, identity/activity labels, confidence masks and overlap flags. See `METHODS.md`, the audit JSONs, source hashes and timestamped event provenance. This custom NPZ feature collection is not an automatically playable audio viewer. Before upload, the Hugging Face URLs below are **intended publication targets**, not evidence that repositories already exist. Use local paths from the unpacked offline ZIP. Only after actual publication should `snapshot_download` be used. ```python from pathlib import Path import numpy as np root = Path('./dataset') # run from the unpacked ZIP root # After publication only: # from huggingface_hub import snapshot_download # root = Path(snapshot_download('laion/tyger-v2-speaker-pilot', repo_type='dataset')) example = np.load(root/'data/v2/conversations/test-028-0.npz', allow_pickle=False) ``` Run training with the included Apache 2.0 code from the unpacked ZIP root: `PYTHONPATH=./dataset/code python -m speaker_v2.train --data ./dataset/data/v1 --out ./results --epochs 30 --device cpu`. For the broader-emotion corpus use `--data ./dataset/data/v2`. Model/library versions are recorded in `environment.json`; rebuild source data using the bounded-download scripts and an independently installed frozen TYGER service, not bundled weights. ## Controls and limitations Two complete long enrollment utterances per identity are disjoint from all student source utterances. V2 student recordings are also disjoint from V1 while the teacher targets are bitwise identical. Procedural scenes include 1–5 speakers, non-hop-aligned boundaries, pauses, overlaps, noise, reverb, telephone filtering and known-delay synthetic echo. No third-party music is used. V1's 192 source clips are balanced at 98 DE / 94 EN but cover only fatigue/background conditions. V2's 383 source clips include 338 DE / 45 EN and explicit source conditions anger, amusement, elation and sadness; this is not balanced English emotion validation. The extra BKGN/very_high condition is not a clean neutral control. Whispering is not covered. Synthetic identity drift and machine alignments are imperfect labels. The packaged speaker student still has 25.3% held-out EER; large-scale training was deliberately not started after 15 exploratory runs failed the quality gate. ## Attribution and distinct licenses Derived dataset artifacts: CC BY 4.0, credit **Christoph Schuhmann, LAION and Humaneness Labs**. Source: [LAION annotated voice profiles](https://huggingface.co/datasets/laion/laion-voice-profiles-annotated), revision 88ea857d6a6f20839674dfde30936d8fd6b23c62, CC BY 4.0. Integration/training code: Apache 2.0. Feature extractor: nineninesix/tyger and NVIDIA Nemotron, OpenMDW 1.1; its outputs are unrestricted by that agreement. Teacher: SpeechBrain ECAPA, separate upstream Apache 2.0 terms. **No third-party model weights are included or relicensed**. Read `NOTICE`, both LICENSE files and `third_party/`. ''') def model_release(destination): import torch from safetensors.torch import save_file, load_file destination.mkdir() add_code(destination) add_licenses(destination, model=True) add_reports(destination) copy_text(HERE/'README.md', destination/'METHODS.md') source = HERE/'artifacts'/'best.pt' state = torch.load(source, map_location='cpu', weights_only=False) assert set(state) <= MODEL_KEYS, 'Unexpected checkpoint fields; manual privacy review required' assert all(isinstance(v,torch.Tensor) and torch.isfinite(v).all() for v in state['model'].values()) assert sum(v.numel() for v in state['model'].values()) < 1_000_000 save_file({k:v.detach().cpu().contiguous() for k,v in state['model'].items()}, str(destination/'model.safetensors')) assert set(load_file(str(destination/'model.safetensors'))) == set(state['model']) # Original trusted research checkpoint is retained for the existing local # loader; safetensors + explicit config is preferred for downloaded weights. torch.save(state, destination/'best.pt') selection = json.loads((HERE/'artifacts'/'selection.json').read_text()) dump(destination/'selection.json', clean(selection)) dump(destination/'config.json', {**state['config'], 'input_dim':1024, 'output_dim':192, 'frame_ms':80, 'hop_ms':320, 'activity_threshold':state['activity_threshold'], 'verification_threshold':state['verification_threshold'], 'recommended_enabled':False, 'pilot_pass':False, 'format':'safetensors state_dict; original custom PyTorch architecture'}) write(destination/'README.md', '''--- license: cc-by-4.0 tags: [speaker-verification, tyger, nemotron, experimental, pytorch, failed-quality-gate] language: [de, en] --- # TYGER V2 experimental speaker-embedding student **Do not enable identity filtering by default. This pilot failed reliability checks and is not authentication.** The original trained student has 470,147 parameters: centered MLP, 640-ms feature context, 1024D frozen Nemotron layer 15 inputs and 192D normalized ECAPA-like outputs, with an auxiliary speech/silence/ overlap head. It runs alongside—not instead of—the original TYGER turn-taking GRU. ## Measured results and nonclaims Chosen on the broader corpus's **validation** identities: EER 18.0%. Unseen test identities: EER 25.3%; the frozen conservative threshold rejects about 90% of genuine short probes. Warmed CPU inference is about 0.32 ms/update. Fifteen actual training runs were completed; no 100,000-conversation expansion was started. The long-clean ECAPA teacher's 2.5% EER is not an equal-context comparison. V2 is German-heavy; results do not establish real microphones, broad English emotion robustness, reliable overlap detection or secure multi-user filtering. `reports/report.html` contains the combined English report, and linked per-run reports/JSONs contain every comparison and its limitations. `selection.json` retains the validation-only choice and original thresholds, with `pilot_pass:false`. ## Load without pickle Before upload, Hugging Face links and repository IDs in this card are **intended publication targets**, not a claim that they are online. The example works with the offline ZIP; enable its commented download lines only after publication. ```python import json, sys, torch from pathlib import Path from safetensors.torch import load_file sys.dont_write_bytecode = True # keep the audited offline release unchanged root = Path('./model') # run from the unpacked ZIP root # After publication only: # from huggingface_hub import snapshot_download # root = Path(snapshot_download('laion/tyger-v2-speaker-predictor-pilot')) sys.path.insert(0, str(root/'code')) # review this small custom architecture first from speaker_v2.models import SpeakerPredictor c = json.loads((root/'config.json').read_text()) m = SpeakerPredictor(c['architecture'], c['width'], centered=c['centered_target']) m.load_state_dict(load_file(str(root/'model.safetensors'))) m.eval() with torch.inference_mode(): embedding, activity = m(torch.zeros(1,c['context'],1024)) # shape example only ``` Supply only frozen Nemotron ASR 3.5 **layer 15** states, four newly produced frames per 320 ms; do not repeat the entire rolling ASR window. Install TYGER/Nemotron and its separate OpenMDW 1.1 weights independently. The included original `best.pt` supports the existing `speaker_v2.inference.Predictor`; it uses a custom PyTorch checkpoint and should only be loaded after verifying provenance/checksums. Safetensors avoids pickle for the weight-loading path above. ## Reproduction and licensing The paired offline features are in `./dataset/data/v1` and `./dataset/data/v2`. Their [intended Hugging Face dataset target](https://huggingface.co/datasets/laion/tyger-v2-speaker-pilot) is usable only after publication. Together with `code/speaker_v2/`, these reproduce training and evaluation. Library versions are in `environment.json`. See `METHODS.md` and dataset source provenance. Our original student weights: CC BY 4.0, LAION/Humaneness Labs. Original code: Apache 2.0. Source speech: Christoph Schuhmann and LAION, CC BY 4.0. Frozen TYGER/ NVIDIA Nemotron: separate OpenMDW 1.1; frozen SpeechBrain ECAPA: separate upstream Apache 2.0 terms. These teachers/encoders produced training targets/features but **none of their weights are distributed or relicensed here**. Their original license and origin notices are retained separately. No microphone recordings, private references, enrolled-user embeddings, or Robo-reference voiceprints are included. Speaker cues require consent and are never secure authentication. ''') def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--out', type=Path, default=HERE/'release') args = parser.parse_args() output = args.out.resolve() if output.exists(): print(json.dumps(audit_release(output), indent=2)) return if shutil.disk_usage(output.parent).free < 500_000_000: raise RuntimeError('Need500MBfree for bounded offline export') output.mkdir() dataset_release(output/'dataset') model_release(output/'model') for name in ('export_pilot.py','publish_pilot.py'): shutil.copy2(HERE/name, output/name) write(output/'PUBLISHING.md', '''# Offline audited release This directory is prepared locally. **Nothing has been uploaded to Hugging Face.** The configured credential previously failed authentication; this build never reads credentials or probes the network. `dataset/` and `model/` are separate HF-ready repositories. Data/student weights are CC BY 4.0; code is Apache 2.0; upstream model notices remain separate. No raw audio, third-party model weights, private microphone recordings, uploaded references, Robo voiceprint, or session enrollment memory are included. Local audit only, no network or token access: `python publish_pilot.py --release .` After a properly scoped configured HF credential and explicit publication authorization are available: `python publish_pilot.py --release . --publish` Publication validates `whoami` LAION write/admin membership and token repository write scope before creating or uploading either repository. It fails closed if metadata is insufficient. Existing targets need explicit `--allow-existing`; remote files are never deleted. Credentials come only from normal HF config or the Git credential helper, remain in memory, and are never printed or stored. The ZIP contains only the manifest-checked allowlist. A server may expose this specific ZIP path; never serve the workspace, source cache, logs or runtime. The research student failed its quality gate; identity filtering remains OFF. ''') files = {} for p in output.rglob('*'): if p.is_file(): files[p.relative_to(output).as_posix()] = {'bytes':p.stat().st_size, 'sha256':hashlib.sha256(p.read_bytes()).hexdigest()} dump(output/'MANIFEST.json', {'schema_version':1, 'public_synthetic_only':True, 'private_recordings':0, 'third_party_weights':False, 'files':files}) audit_release(output) with zipfile.ZipFile(output/'tyger-v2-public-pilot.zip', 'w', compression=zipfile.ZIP_DEFLATED, compresslevel=3) as archive: for name in sorted([*files, 'MANIFEST.json']): archive.write(output/name, arcname=name) result = audit_release(output) result['network_used'] = False print(json.dumps(result, indent=2)) if __name__ == '__main__': main()