diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..c017c7b147cffb75f9683f8177ffd00ca8bf397e --- /dev/null +++ b/LICENSE @@ -0,0 +1,7 @@ +CC BY 4.0 +https://creativecommons.org/licenses/by/4.0/legalcode + +LibriTTS-R: Yuma Koizumi and colleagues, 2023. https://www.openslr.org/141/ +otoSpeech-full-duplex-processed-141h: otoearth, 2026. +https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h +Changes and processing are described in README.md and reports/. diff --git a/MANIFEST.json b/MANIFEST.json new file mode 100644 index 0000000000000000000000000000000000000000..4f1826e22d958bf0eb87ebe2cef4b6c936a2868b --- /dev/null +++ b/MANIFEST.json @@ -0,0 +1,328 @@ +{ + "files": [ + { + "bytes": 134766737, + "path": "alignment/test-00000.parquet", + "sha256": "0eb86a4dd7d26d96b44a2605da943415691c480a88835e85d6f3696db76e2268" + }, + { + "bytes": 134764261, + "path": "alignment/test-00001.parquet", + "sha256": 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cc-by-4.0 +task_categories: +- automatic-speech-recognition +- text-to-speech +- audio-to-audio +tags: +- nano-duplex +- conversational-speech +- full-duplex +- mimi +configs: +- config_name: alignment + default: true + data_files: + - split: train + path: alignment/train-*.parquet + - split: validation + path: alignment/validation-*.parquet + - split: test + path: alignment/test-*.parquet +- config_name: interaction + data_files: + - split: train + path: interaction/train-*.parquet + - split: validation + path: interaction/validation-*.parquet + - split: test + path: interaction/test-*.parquet +--- + +# nano-duplex data v1 + +English speech alignment and a first natural-conversation collection for nano-duplex research. +All audio is embedded in Parquet as PCM16 FLAC at 24 kHz. No external audio paths are required. + +| Configuration | Split | Examples | Audio hours | Speakers | +| --- | --- | ---: | ---: | ---: | +| alignment | train | 26,478 | 32.377 | 247 | +| alignment | validation | 4,708 | 5.817 | 40 | +| alignment | test | 3,624 | 4.428 | 38 | +| interaction | train | 946 | 15.240 | 57 | +| interaction | validation | 122 | 1.978 | 11 | +| interaction | test | 116 | 1.877 | 11 | + +## Sources and processing + +**Alignment:** [LibriTTS-R](https://www.openslr.org/141/), by Yuma Koizumi and colleagues. +The complete official `train_clean_100`, `dev_clean`, and `test_clean` archives passed the +official MD5 checks before extraction. Keep complete mono 24 kHz PCM16 utterances from +1 through 10 seconds with normalized transcripts. Preserve official splits and original IDs. +Reject missing transcripts, out-of-range durations, duplicate audio, and entirely silent clips. +WAV-to-FLAC conversion is lossless; original WAV and decoded PCM SHA256 are recorded. +Original and normalized text are included; the source transcript hash refers to the original +normalized-text file, before trimming its surrounding whitespace. RMS and clipping metrics +are diagnostics, not human quality judgments. This is a filtered subset, not all hours implied +by the upstream archive names. Citation: Koizumi et al., "LibriTTS-R: A Restored Multi-Speaker +Text-to-Speech Corpus," 2023. + +**Interaction:** [otoearth/otoSpeech-full-duplex-processed-141h](https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h). +Use eight complete upstream tar shards, selected by lexicographic filename order, at revision +`c635c29ad083570fe2cadbafc75770e76eded2da`. Every shard passed its pinned Hub SHA256. +The selection contains 73 complete sessions. Resample each complete stereo session from +44.1 kHz to 24 kHz using SciPy polyphase filtering, then split into consecutive 60-second +windows (including the shorter final window). Both channels retain the shared clock. +Preserve overlapping speech and silence, and re-mute the original redacted intervals after +resampling. The two `speaker_ids` are ordered by audio channel. Source-relative timestamps +allow exact reassembly. Speaker-connected components stay within one split; there is no +reported speaker-ID or session overlap across train/validation/test. Speaker identities beyond +the IDs reported by the source have not been independently verified. Omit optional surveys +and demographic profiles. Citation: otoearth, "otoSpeech-full-duplex-processed-141h," 2026. + +## Scope + +Natural conversations have no assistant tool traces, cancelled playback plans, certified +prefix-causal action targets, or transcripts in this release. `causal_verified=false` and +`has_transcript=false` are explicit. These recordings support interaction preparation and +analysis; they are not ready-made Stage B-D causal trajectories. No synthetic speech is +substituted for real recordings. Tool, vision, and motion supervision are outside this release. + +`codec-pilot/` contains real Mimi ASR features and eight-codebook TTS targets for 773 training +utterances (57.301 minutes), drawn from `alignment/train`. This pilot has no independent +evaluation split. Its JSONL manifests reference tensor files packed in `tensors.tar`; +`cache-index.json` records every member's size and SHA256. The server entry expands and +verifies them automatically. The full +alignment and interaction collections are processed audio; only this pilot is codec-encoded. +The exact Mimi repository, revision, checkpoint digest and cache dimensions are recorded. +Tensor files must be loaded with `torch.load(..., weights_only=True)` as nano-duplex does. +The provided `codec-pilot/asr.toml` and `codec-pilot/tts.toml` select matching pinned assets. + +## Loading + +```python +from datasets import load_dataset, Audio + +alignment = load_dataset("JonesLin/nano-duplex-data-v1", "alignment", token=True) +interaction = load_dataset("JonesLin/nano-duplex-data-v1", "interaction", token=True) +# Decode with soundfile without requiring torchcodec: +audio = alignment["train"].cast_column("audio", Audio(decode=False))[0]["audio"] +from io import BytesIO +import soundfile as sf +pcm, sample_rate = sf.read(BytesIO(audio["bytes"])) +``` + +From the nano-duplex project checkout, install `requirements-speech.txt` and +`requirements-data.txt`, then use the server entry (a copy is included at +`tools/hf_training.py`; it belongs in the project's `nano_duplex` package): + +```bash +# Download only the small pre-encoded pilot, verify and expand it, then train ASR/TTS. +python -m nano_duplex.hf_training --profile pilot --steps 400 + +# Full Stage A: download alignment data, encode each split once, train, evaluate. +python -m nano_duplex.hf_training --profile full --nproc 4 --steps 20000 +``` + +The first command resolves a concrete Hub commit and writes `dataset-lock.json`. +Alternatively pass `--revision` with an exact commit. `--snapshot-dir`, `--work-dir`, +and `--run-dir` select server storage. `--phase prepare` stops before training; +`--phase train --resume` reuses data caches and resumes existing task checkpoints. +An unstarted second task starts fresh. Use `--dry-run` to inspect commands without +network access or file writes. H200 distributed execution has not been run here; +20,000 steps is a starting budget, not a measured convergence requirement. + +The full profile still performs one Mimi encoding pass over the complete alignment +collection on first use. Subsequent runs reuse checked prepared manifests. Use +`tools/materialize_alignment.py` separately if only mono WAV/raw JSONL export is needed. +The interaction configuration is available through `load_dataset`, but is not fed into +the causal duplex trainer without the additional labels described above. +Changing a dataset manifest requires a new training run, not resuming an old optimizer state. + +## Provenance and license + +Both source corpora are provided under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). +This derivative retains that license and the source attributions above. The source-provided +gated dataset access was obtained through the collecting user's own Hugging Face account. +Source receipts, split statistics, exclusions, processing parameters, and full validation +results are in `reports/`. `MANIFEST.json` inventories the exact release files with SHA256. +The validation reads every packaged audio example, checks its PCM hash, duration, channel +count, session continuity, redaction masks, and split isolation. 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diff --git a/codec-pilot/tts.toml b/codec-pilot/tts.toml new file mode 100644 index 0000000000000000000000000000000000000000..357478a43d38ab0e2356a7e51d93fc073e656d5f --- /dev/null +++ b/codec-pilot/tts.toml @@ -0,0 +1,28 @@ +policy = "Qwen/Qwen3-0.6B-Base" +policy_revision = "da87bfb608c14b7cf20ba1ce41287e8de496c0cd" +mimi_repo = "kyutai/moshiko-pytorch-bf16" +mimi_revision = "2bfc9ae6e89079a5cc7ed2a68436010d91a3d289" +device = "cuda" +audio_dim = 512 +vision_dim = 768 +codebooks = 8 +codebook_size = 2048 +audio_head_width = 256 +audio_head_layers = 2 +audio_head_attention_heads = 4 +max_context_tokens = 2048 +precision = "bfloat16" +freeze_policy = true +batch_size = 8 +gradient_accumulation = 1 +learning_rate = 0.0003 +weight_decay = 0.01 +steps = 400 +save_every = 400 +dataset = "codec-pilot/tts-train.jsonl" +output_dir = "runs/hf-pilot-tts" +stage = "alignment_tts" + +# Opt-in: use a new run; effective batch differs from the baseline. +alignment_batching = "batched" 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"whole-session stereo polyphase 44100-to-24000 Hz; PCM16 FLAC; original redactions re-muted", + "window_seconds": 60 +} diff --git a/reports/processing.json b/reports/processing.json new file mode 100644 index 0000000000000000000000000000000000000000..666ff7fde918ba43c9df9375d61b698796fefa7b --- /dev/null +++ b/reports/processing.json @@ -0,0 +1,36 @@ +{ + "codec_packaging": { + "archive_bytes": 95825920, + "tensor_files": 1546 + }, + "interaction_resampling": { + "down": 147, + "padtype": "constant", + "quantization": "round to nearest PCM16; saturate to [-32768,32767]", + "redaction": "re-mute floor(start*24000):ceil(end*24000) after resampling", + "scope": "whole stereo session", + "up": 80, + "window": [ + "kaiser", + 5.0 + ] + }, + "sample_rate": 24000, + "scripts": { + "build_hf_release.py": "4a394197f89135c0e9b6caf799e81d95236cfae2df62763b186119562c9f17c7", + "download_release_sources.py": "f8593b1dcab80a0a07bba6e027b071c18e251268b94d1e3433979f43461f3e40", + "materialize_alignment.py": "3b0fc0c711ea96ab78ed6c26c14feee1064f37789247cadf29f5e51e390e0ae9", + "publish_hf_release.py": "0d019b6be8b1fca76c71cd50b80d76d7231d262a01d9d5e350aa32fc70052cd6" + }, + "server_entry_sha256": "a7c17dfba679cbda48e3d02df6cb28ea97de4c8fd7f8bfc0a1531f7f89c95306", + "versions": { + "datasets": "4.4.1", + "huggingface-hub": "0.36.0", + "moshi": "0.2.13", + "numpy": "2.2.6", + "pyarrow": "26.0.1", + "scipy": "1.18.1", + "soundfile": "0.14.0", + "torch": "2.9.1+cu128" + } +} diff --git a/reports/validation.json b/reports/validation.json new file mode 100644 index 0000000000000000000000000000000000000000..faf2498c0511a4ac80229bf3244324fce47a9cda --- /dev/null +++ b/reports/validation.json @@ -0,0 +1,58 @@ +{ + "all_audio_examples_checked": true, + "checks": [ + "lossless_pcm_sha256", + "sample_rate", + "channel_count", + "duration", + "unique_example_id", + "alignment_pcm_deduplication", + "speaker_split_isolation", + "session_split_isolation", + "continuous_session_clock", + "silent_redaction_masks" + ], + "configs": { + "alignment": { + "test": { + "duration_seconds": 15940.32, + "rows": 3624, + "sessions": 0, + "speakers": 38 + }, + "train": { + "duration_seconds": 116558.76, + "rows": 26478, + "sessions": 0, + "speakers": 247 + }, + "validation": { + "duration_seconds": 20939.44, + "rows": 4708, + "sessions": 0, + "speakers": 40 + } + }, + "interaction": { + "test": { + "duration_seconds": 6756.068, + "rows": 116, + "sessions": 7, + "speakers": 11 + }, + "train": { + "duration_seconds": 54862.802, + "rows": 946, + "sessions": 58, + "speakers": 57 + }, + "validation": { + "duration_seconds": 7121.24, + "rows": 122, + "sessions": 8, + "speakers": 11 + } + } + }, + "status": "passed" +} diff --git a/tools/hf_training.py b/tools/hf_training.py new file mode 100644 index 0000000000000000000000000000000000000000..2e9b6e7a3ffe964667efa6b8aff4eb170d7e5b25 --- /dev/null +++ b/tools/hf_training.py @@ -0,0 +1,291 @@ +"""Pinned Hub data -> verified caches -> independent Stage A ASR/TTS training. + +The pilot profile uses already encoded training data. The full profile encodes +the official alignment splits once and reuses verified preparation manifests. +Natural interaction data is not mislabeled as a causal duplex training cache. +""" +from __future__ import annotations + +import argparse +from dataclasses import replace +from fnmatch import fnmatch +import hashlib +import json +import os +from pathlib import Path +import re +import runpy +import shutil +import subprocess +import sys +import tarfile + +from .config import Config +from .data import AlignmentDataset, load_jsonl, write_jsonl + + +def digest(path): + value = hashlib.sha256() + with Path(path).open('rb') as handle: + for block in iter(lambda: handle.read(4 * 1024**2), b''): + value.update(block) + return value.hexdigest() + + +def save_json(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + '.tmp') + temporary.write_text(json.dumps(value, indent=2, sort_keys=True) + '\n', encoding='utf-8') + temporary.replace(path) + + +def patterns(profile): + common = ['README.md', 'LICENSE', 'MANIFEST.json', 'codec-pilot/*.toml', 'reports/codec-pilot.json'] + return common + (['codec-pilot/**'] if profile == 'pilot' else + ['alignment/**', 'reports/alignment.json', 'tools/materialize_alignment.py']) + + +def verify_snapshot(root, profile): + root = Path(root).resolve() + manifest = json.loads((root / 'MANIFEST.json').read_text(encoding='utf-8')) + if manifest.get('status') != 'validated' or manifest.get('release') != 'nano-duplex-data-v1': + raise ValueError('Expected a validated nano-duplex data release') + expected = [item for item in manifest['files'] if any(fnmatch(item['path'], pattern) for pattern in patterns(profile))] + if not expected: + raise ValueError('Release has no files for this profile') + for item in expected: + path = (root / item['path']).resolve() + if not path.is_relative_to(root) or path.is_symlink(): + raise ValueError('Snapshot path escapes its dataset root') + if not path.is_file() or path.stat().st_size != item['bytes'] or digest(path) != item['sha256']: + raise ValueError(f'Snapshot file failed verification: {item["path"]}') + return {'manifest_sha256': digest(root / 'MANIFEST.json'), 'verified_files': len(expected)} + + +def snapshot(args): + lock_path = args.work_dir / 'dataset-lock.json' + old = json.loads(lock_path.read_text()) if lock_path.exists() else None + if old and old['repo'] != args.repo: + raise ValueError('This work directory is bound to another repository') + if old and args.revision and old['revision'] != args.revision: + raise ValueError('Use a new work directory when changing the dataset revision') + revision = args.revision or (old['revision'] if old else None) + if not args.offline and args.phase in {'download', 'all'}: + from huggingface_hub import HfApi, snapshot_download + revision = revision or HfApi().dataset_info(args.repo).sha + snapshot_download(args.repo, repo_type='dataset', revision=revision, local_dir=args.snapshot_dir, + allow_patterns=patterns(args.profile), max_workers=8) + checked = verify_snapshot(args.snapshot_dir, args.profile) + revision = revision or ('local:' + checked['manifest_sha256']) + if old and old['manifest_sha256'] != checked['manifest_sha256']: + raise ValueError('Dataset contents changed inside an existing work directory') + lock = {'repo': args.repo, 'revision': revision, **checked} + save_json(lock_path, lock) + return lock + + +def dataset_path(args, task, split='train'): + folder = args.work_dir / 'codec-pilot' if args.profile == 'pilot' else args.work_dir / 'prepared' + return folder / f'{task}-{split}.jsonl' + + +def configurations(args): + result = {} + for task in ('asr', 'tts'): + base = Config.load(args.snapshot_dir / 'codec-pilot' / f'{task}.toml') + config = replace(base, dataset=str(dataset_path(args, task)), output_dir=str(args.run_dir / task), + device=args.device, batch_size=args.batch_size, steps=args.steps, + save_every=min(base.save_every, args.steps)).validate() + path = args.work_dir / 'configs' / f'{task}.toml' + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(''.join(f'{key} = {json.dumps(value, ensure_ascii=True, allow_nan=False)}\n' + for key, value in config.to_dict().items() if value is not None), encoding='utf-8') + result[task] = config + return result + + +def train_command(args, task): + prefix = [sys.executable] + if args.nproc > 1: + prefix += ['-m', 'torch.distributed.run', '--standalone', f'--nproc_per_node={args.nproc}'] + command = prefix + ['-m', 'nano_duplex.train', '--config', str(args.work_dir / 'configs' / f'{task}.toml')] + checkpoint = args.run_dir / task / 'latest.pt' + if args.resume and checkpoint.is_file(): + command += ['--resume', str(checkpoint)] + return command + + +def cache_present(path, config, task): + data = AlignmentDataset(path, config, f'alignment_{task}') + for row in data.manifest_rows: + if not (Path(path).parent / row['cache']['path']).is_file(): + raise FileNotFoundError('A prepared tensor cache is missing') + return len(data) + + +def expand_pilot(snapshot_dir, destination): + """Expand only indexed regular tensor files, checking each digest before promotion.""" + source, destination = Path(snapshot_dir) / 'codec-pilot', Path(destination).resolve() + index = json.loads((source / 'cache-index.json').read_text(encoding='utf-8')) + expected = {item['path']: item for item in index['files']} + if len(expected) != len(index['files']): + raise ValueError('Duplicate archive index paths') + destination.mkdir(parents=True, exist_ok=True) + seen = set() + with tarfile.open(source / 'tensors.tar', 'r') as archive: + for member in archive: + item = expected.get(member.name) + if (not member.isfile() or item is None or member.name in seen + or member.size != item['bytes'] or not re.fullmatch(r'cache/[0-9a-f]{64}\.pt', member.name)): + raise ValueError('Unexpected tensor archive member') + seen.add(member.name) + candidate = destination / member.name + path = candidate.resolve() + if not path.is_relative_to(destination) or candidate.is_symlink(): + raise ValueError('Tensor archive path escapes its output') + if path.is_file() and digest(path) == item['sha256']: + continue + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix('.pt.tmp') + with archive.extractfile(member) as reader, temporary.open('wb') as target: + shutil.copyfileobj(reader, target, 1024**2) + if temporary.stat().st_size != item['bytes'] or digest(temporary) != item['sha256']: + raise ValueError('Expanded tensor checksum mismatch') + temporary.replace(path) + if seen != set(expected): + raise ValueError('Tensor archive is missing indexed files') + for task in ('asr', 'tts'): + shutil.copyfile(source / f'{task}-train.jsonl', destination / f'{task}-train.jsonl') + return len(expected) + + +def prepare(args, configs, lock): + if args.profile == 'pilot': + expand_pilot(args.snapshot_dir, args.work_dir / 'codec-pilot') + return {task: cache_present(dataset_path(args, task), configs[task], task) for task in configs} + state_path = args.work_dir / 'preparation.json' + binding = {'manifest_sha256': lock['manifest_sha256'], + 'codec': {key: getattr(configs['asr'], key) for key in + ('mimi_repo', 'mimi_revision', 'sample_rate', 'tick_ms', 'audio_dim', 'codebooks', 'codebook_size')}} + state = json.loads(state_path.read_text()) if state_path.exists() else {**binding, 'completed': {}} + if any(state.get(key) != value for key, value in binding.items()): + raise ValueError('Cached preparation belongs to a different dataset or codec; use a new work directory') + save_json(state_path, state) + # This is the release's own materializer, checked against the pinned manifest above. + export = runpy.run_path(str(args.snapshot_dir / 'tools/materialize_alignment.py'))['materialize'] + for split in ('train', 'validation', 'test'): + paths = {task: dataset_path(args, task, split) for task in configs} + if split in state['completed']: + for task, path in paths.items(): + if digest(path) != state['completed'][split][task]: + raise ValueError('A prepared manifest changed; refusing to silently reuse it') + cache_present(path, configs[task], task) + print(json.dumps({'preparation': split, 'status': 'reused'}), flush=True) + continue + raw = args.work_dir / 'raw' / f'{split}.jsonl' + if not raw.exists(): + export(args.snapshot_dir, raw.parent, split) + for row in load_jsonl(raw): + if digest(raw.parent / row['audio']) != row['source_sha256']: + raise ValueError('A materialized source WAV changed before encoding') + command = [sys.executable, '-m', 'nano_duplex.data', 'prepare-pair', + '--config', str(args.work_dir / 'configs/asr.toml'), + '--tts-config', str(args.work_dir / 'configs/tts.toml'), '--manifest', str(raw), + '--asr-output', str(paths['asr']), '--tts-output', str(paths['tts'])] + subprocess.run(command, check=True) + for path in paths.values(): + rows = load_jsonl(path) + for row in rows: + row['audio'] = Path(os.path.relpath((raw.parent / row['audio']).resolve(), path.parent)).as_posix() + write_jsonl(path, rows) + state['completed'][split] = {task: digest(path) for task, path in paths.items()} + save_json(state_path, state) + return {task: cache_present(dataset_path(args, task), configs[task], task) for task in configs} + + +def options(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--repo', default='JonesLin/nano-duplex-data-v1') + parser.add_argument('--revision', help='Exact dataset commit; otherwise resolve once and lock it locally') + parser.add_argument('--profile', choices=['pilot', 'full'], default='pilot') + parser.add_argument('--phase', choices=['download', 'prepare', 'train', 'evaluate', 'all'], default='all') + parser.add_argument('--snapshot-dir', type=Path, default=Path('data/hf-server/snapshot')) + parser.add_argument('--work-dir', type=Path, default=Path('data/hf-server/work')) + parser.add_argument('--run-dir', type=Path) + parser.add_argument('--nproc', type=int, default=1) + parser.add_argument('--steps', type=int, default=400) + parser.add_argument('--batch-size', type=int, default=8) + parser.add_argument('--device', default='cuda') + parser.add_argument('--resume', action='store_true') + parser.add_argument('--offline', action='store_true', help='Use and verify an existing local snapshot') + parser.add_argument('--dry-run', action='store_true', help='Print the workflow without network access or file changes') + args = parser.parse_args(argv) + if args.revision and not re.fullmatch('[0-9a-f]{40}', args.revision): + parser.error('--revision must be a full 40-character commit SHA') + if min(args.nproc, args.steps, args.batch_size) <= 0: + parser.error('GPU processes, steps and batch size must be positive') + if args.profile == 'pilot' and args.phase == 'evaluate': + parser.error('The codec pilot is training-only; use the full profile for held-out evaluation') + args.run_dir = args.run_dir or Path(f'runs/hf-stage-a-{args.profile}') + for name in ('snapshot_dir', 'work_dir', 'run_dir'): + setattr(args, name, getattr(args, name).resolve()) + return args + + +def main(argv=None): + args = options(argv) + if args.dry_run: + print(json.dumps({'profile': args.profile, 'phase': args.phase, 'repo': args.repo, + 'revision': args.revision or 'resolve once and lock', 'download_patterns': patterns(args.profile), + 'prepare': 'reuse included Mimi tensors' if args.profile == 'pilot' else + 'export verified WAV/JSONL; encode train/validation/test once; reuse on later runs', + 'training_commands': {task: train_command(args, task) for task in ('asr', 'tts')}, + 'scope': 'independent Stage A alignment; no duplex-policy training'}, indent=2)) + return 0 + if args.phase in {'train', 'all'}: + found_checkpoint = False + for task in ('asr', 'tts'): + exists = (args.run_dir / task / 'latest.pt').exists() + found_checkpoint |= exists + if exists and not args.resume: + raise FileExistsError('Existing checkpoints require --resume or a new --run-dir') + if args.resume and not found_checkpoint: + raise FileNotFoundError('--resume requires at least one existing task checkpoint') + lock = snapshot(args) + configs = configurations(args) + status = {'status': 'running', 'profile': args.profile, 'phase': args.phase, 'dataset': lock, 'completed': []} + status_path = args.work_dir / 'pipeline-status.json' + save_json(status_path, status) + try: + if args.phase in {'prepare', 'all'}: + status['cache_rows'] = prepare(args, configs, lock) + status['completed'].append('prepare') + save_json(status_path, status) + if args.phase in {'train', 'all'}: + for task in configs: + cache_present(dataset_path(args, task), configs[task], task) + subprocess.run(train_command(args, task), check=True) + status['completed'].append(f'train_{task}') + save_json(status_path, status) + if args.phase == 'evaluate' or (args.phase == 'all' and args.profile == 'full'): + for task in configs: + subprocess.run([sys.executable, '-m', 'nano_duplex.alignment_eval', + '--checkpoint', str(args.run_dir / task / 'latest.pt'), + '--manifest', str(dataset_path(args, task, 'validation')), + '--train-manifest', str(dataset_path(args, task)), '--split', 'heldout', + '--device', args.device, '--max-samples', '8', '--max-new-tokens', '96', + '--output-dir', str(args.run_dir / 'evaluation' / task)], check=True) + status['completed'].append(f'evaluate_{task}') + status['status'] = 'completed' + except Exception as exc: + status.update(status='failed', error=f'{type(exc).__name__}: {exc}') + raise + finally: + save_json(status_path, status) + print(json.dumps(status), flush=True) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/materialize_alignment.py b/tools/materialize_alignment.py new file mode 100644 index 0000000000000000000000000000000000000000..8cae7ad645c017bc910ee18d35ae9d7369ce2b1b --- /dev/null +++ b/tools/materialize_alignment.py @@ -0,0 +1,82 @@ +"""Export this release's alignment Parquet files to nano-duplex raw WAV/JSONL. + +Example: python materialize_alignment.py --snapshot ./nano-duplex-data-v1 --output ./raw --split train +Dependencies: numpy, pyarrow, soundfile. No torchcodec or remote dataset code is required. +""" +from __future__ import annotations + +import argparse +import hashlib +from io import BytesIO +import json +from pathlib import Path +import re + +import numpy as np +import pyarrow.parquet as pq +import soundfile as sf + + +def materialize(snapshot, output, split, limit=None): + snapshot, output = Path(snapshot), Path(output) + files = sorted((snapshot / 'alignment').glob(f'{split}-*.parquet')) + if not files: + raise ValueError('No alignment shards for the requested split') + if limit is not None and limit <= 0: + raise ValueError('Limit must be positive') + output.mkdir(parents=True, exist_ok=True) + audio_directory = output / 'audio' / split + audio_directory.mkdir(parents=True, exist_ok=True) + manifest = output / f'{split}.jsonl' + if manifest.exists(): + raise FileExistsError('Use a fresh output directory to preserve existing training manifests') + temporary = manifest.with_suffix('.jsonl.tmp') + count = 0 + with temporary.open('w', encoding='utf-8', newline='\n') as target: + for file in files: + for batch in pq.ParquetFile(file).iter_batches(batch_size=16): + for row in batch.to_pylist(): + if not re.fullmatch(r'\d+_\d+_\d+_\d+', row['original_id']): + raise ValueError('Unexpected original utterance identifier') + pcm, rate = sf.read(BytesIO(row['audio']['bytes']), dtype='int16') + digest = hashlib.sha256(np.asarray(pcm, dtype='= limit: + break + if limit is not None and count >= limit: + break + if limit is not None and count >= limit: + break + temporary.replace(manifest) + return {'manifest': str(manifest), 'rows': count} + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--snapshot', required=True) + parser.add_argument('--output', required=True) + parser.add_argument('--split', choices=['train', 'validation', 'test'], default='train') + parser.add_argument('--limit', type=int) + args = parser.parse_args() + print(json.dumps(materialize(args.snapshot, args.output, args.split, args.limit))) diff --git a/tools/requirements-data.txt b/tools/requirements-data.txt new file mode 100644 index 0000000000000000000000000000000000000000..9629c03661f4c301d6ee6425212a6247f6bae085 --- /dev/null +++ b/tools/requirements-data.txt @@ -0,0 +1,7 @@ +# Used to acquire, process, validate, and publish the audio data release. +datasets==4.4.1 +pyarrow==26.0.1 +scipy==1.18.1 +numpy==2.2.6 +soundfile==0.14.0 +huggingface-hub==0.36.0