NINI
341 lines
18 KiB
Python
341 lines
18 KiB
Python
"""Produce pinned local Qwen Korean voice assets; validation never loads a model."""
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import argparse
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from datetime import datetime, timezone
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import hashlib
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import importlib.metadata
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import json
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from pathlib import Path
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import re
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import subprocess
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import sys
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import time
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import uuid
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import numpy as np
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import soundfile as sf
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MODEL_ID = 'Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice'
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def digest(value):
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return hashlib.sha256(json.dumps(value, ensure_ascii=False, sort_keys=True,
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separators=(',', ':')).encode('utf-8')).hexdigest()
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def file_hash(path):
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return hashlib.sha256(Path(path).read_bytes()).hexdigest()
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def load_json(path):
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return json.loads(Path(path).read_text(encoding='utf-8'))
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def write_json(path, value):
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Path(path).write_text(json.dumps(value, ensure_ascii=False, indent=2) + '\n', encoding='utf-8')
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def validate_inputs(catalog, profile):
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if catalog.get('schemaVersion') != 1 or profile.get('schemaVersion') != 1:
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raise ValueError('catalog/profile schemaVersion must be 1')
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clips = catalog.get('clips')
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if not isinstance(clips, list) or not clips:
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raise ValueError('catalog must contain clips')
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ids = set()
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for clip in clips:
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clip_id = clip.get('id', '')
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if not re.fullmatch(r'[a-z][a-z0-9_]*', clip_id) or clip_id in ids:
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raise ValueError(f'{clip_id}: invalid or duplicate ID')
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ids.add(clip_id)
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if not isinstance(clip.get('text'), str) or not clip['text'].strip():
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raise ValueError(f'{clip_id}: text is empty')
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if not re.search('[가-힣]', clip['text']) or re.search(r'[0-9]', clip['text']):
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raise ValueError(f'{clip_id}: use a complete Korean utterance, not numeral digits')
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if not isinstance(clip.get('required'), bool) or not clip.get('usage'):
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raise ValueError(f'{clip_id}: required/usage missing')
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if not isinstance(clip.get('stageIds'), list) or not clip['stageIds'] or any(
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type(n) is not int or n < 1 or n > 30 for n in clip['stageIds']):
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raise ValueError(f'{clip_id}: stageIds must be within 1..30')
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for field in ('modelRevision', 'instruct', 'device', 'dtype', 'attention', 'generationParameters', 'seed'):
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if field not in profile:
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raise ValueError(f'profile: missing {field}')
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if profile.get('modelId') != MODEL_ID or profile.get('language') != 'Korean' or profile.get('speaker') != 'Sohee':
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raise ValueError('profile must retain approved model/Korean/Sohee')
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if not re.fullmatch('[0-9a-f]{40}', profile['modelRevision']):
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raise ValueError('profile must pin a full model revision')
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if profile['device'] != 'cuda:0' or profile['dtype'] not in ('bfloat16', 'float16') or profile['attention'] not in ('sdpa', 'eager'):
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raise ValueError('profile requires approved CUDA configuration')
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if not isinstance(profile['generationParameters'], dict) or type(profile['seed']) is not int:
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raise ValueError('invalid generation settings')
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def select_clips(catalog, ids=None, required_only=False):
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by_id = {c['id']: c for c in catalog['clips']}
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if ids is None:
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return [c for c in catalog['clips'] if not required_only or c['required']]
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missing = set(ids) - by_id.keys()
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if missing:
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raise ValueError('unknown IDs: ' + ', '.join(sorted(missing)))
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if not ids or len(ids) != len(set(ids)):
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raise ValueError('requested IDs must be nonempty and unique')
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return [by_id[key] for key in ids]
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def waveform_metadata(path):
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samples, sample_rate = sf.read(path, dtype='float32', always_2d=True)
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if sample_rate <= 0 or samples.size == 0 or not np.isfinite(samples).all():
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raise ValueError('empty or nonfinite waveform')
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peak = float(np.max(np.abs(samples)))
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return {'sha256': file_hash(path), 'sampleRate': sample_rate, 'channels': samples.shape[1],
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'frames': samples.shape[0], 'duration': samples.shape[0] / sample_rate,
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'peak': peak, 'rms': float(np.sqrt(np.mean(samples ** 2))),
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'qualityWarnings': (['near silence'] if peak < .001 else []) +
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(['clipping candidate'] if peak >= .999 else [])}
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def load_run_inputs(manifest, root):
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run_id = manifest.get('runId', '')
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if not isinstance(run_id, str) or not re.fullmatch(r'[A-Za-z0-9_]+', run_id):
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raise ValueError('invalid input snapshot runId')
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references = manifest.get('inputSnapshots', {'catalog': 'catalog.json', 'profile': 'profile.json'})
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if references != {'catalog': 'catalog.json', 'profile': 'profile.json'}:
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raise ValueError('input snapshots must reside in their immutable run directory')
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run_dir = Path(root) / 'generated' / run_id
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catalog = load_json(run_dir / references['catalog'])
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profile = load_json(run_dir / references['profile'])
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if not isinstance(catalog, dict) or not isinstance(profile, dict):
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raise ValueError('input snapshots must contain JSON objects')
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if digest(catalog) != manifest.get('catalogHash') or digest(profile) != manifest.get('profileHash'):
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raise ValueError('input snapshot hash differs from manifest')
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validate_inputs(catalog, profile)
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if profile['modelRevision'] != manifest.get('modelRevision'):
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raise ValueError('input snapshot model revision differs from manifest')
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clips = {clip['id']: clip for clip in catalog['clips']}
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for entry in manifest.get('clips', []):
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clip = clips.get(entry.get('id'))
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if not clip or entry.get('textHash') != digest(clip['text']) or entry.get('settingsHash') != digest(profile):
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raise ValueError(f"{entry.get('id')}: input snapshot differs from clip text/settings hashes")
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return catalog, profile
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def clip_errors(entry, clip, profile, root):
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errors = []
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if entry.get('result') not in ('generated', 'reused'):
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return [f"{clip['id']}: no successful generation"]
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if entry.get('textHash') != digest(clip['text']) or entry.get('settingsHash') != digest(profile):
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errors.append(f"{clip['id']}: current text/profile differs")
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try:
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path = (root / entry['file']).resolve()
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if not path.is_relative_to(root.resolve()):
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raise ValueError('waveform escapes source directory')
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actual = waveform_metadata(path)
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for key in ('sha256', 'sampleRate', 'channels', 'frames', 'duration'):
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if actual[key] != entry.get(key):
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errors.append(f"{clip['id']}: {key} mismatch")
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if actual['channels'] != 1:
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errors.append(f"{clip['id']}: expected mono")
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run_id = entry['originalRunId']
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if not isinstance(run_id, str) or not re.fullmatch(r'[A-Za-z0-9_]+', run_id):
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raise ValueError('invalid original generation runId')
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origin = load_json(root / 'generated' / run_id / 'manifest.json')
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if origin.get('runId') != run_id:
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raise ValueError('original manifest runId mismatch')
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load_run_inputs(origin, root)
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original_entries = [c for c in origin['clips'] if c.get('id') == clip['id']]
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if len(original_entries) != 1 or original_entries[0].get('result') != 'generated':
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raise ValueError('missing successful original generation entry')
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for key in ('file', 'sha256', 'textHash', 'settingsHash'):
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if original_entries[0].get(key) != entry.get(key):
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raise ValueError(f'original generation {key} differs')
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except (KeyError, OSError, ValueError, sf.LibsndfileError) as error:
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errors.append(f"{clip['id']}: invalid source artifact: {error}")
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return errors
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def validate_manifest(manifest, catalog, profile, root, ids=None):
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validate_inputs(catalog, profile)
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requested = select_clips(catalog, ids, required_only=ids is None)
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errors = []
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try:
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load_run_inputs(manifest, root)
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except (KeyError, OSError, ValueError, TypeError) as error:
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errors.append(f'input snapshots invalid: {error}')
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if manifest.get('schemaVersion') != 1 or manifest.get('modelRevision') != profile['modelRevision']:
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errors.append('manifest schema/revision differs')
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if manifest.get('catalogHash') != digest(catalog) or manifest.get('profileHash') != digest(profile):
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errors.append('manifest does not describe current catalog/profile')
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entries = manifest.get('clips', [])
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by_id = {c.get('id'): c for c in entries}
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if len(by_id) != len(entries):
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errors.append('manifest contains duplicate IDs')
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for clip in requested:
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if clip['id'] not in by_id:
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errors.append(f"{clip['id']}: missing required/requested ID")
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else:
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errors.extend(clip_errors(by_id[clip['id']], clip, profile, root))
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return errors
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def reusable_entries(root, catalog, profile):
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result = {}
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by_id = {c['id']: c for c in catalog['clips']}
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for path in sorted((root / 'generated').glob('*/manifest.json'), reverse=True):
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try:
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manifest = load_json(path)
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load_run_inputs(manifest, root)
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for entry in manifest.get('clips', []):
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clip = by_id.get(entry.get('id'))
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if clip and clip['id'] not in result and not clip_errors(entry, clip, profile, root):
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result[clip['id']] = entry
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except (OSError, ValueError, TypeError):
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continue
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return result
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def reuse_environment(profile, entries, root):
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environment = {'python': sys.version, 'packages': {name: importlib.metadata.version(name) for name in
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('qwen-tts', 'torch', 'torchaudio', 'transformers', 'soundfile', 'huggingface-hub')},
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'modelLoad': 'not_needed', 'requestedDevice': profile['device'], 'sourceGenerations': []}
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try:
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probe = subprocess.run(['nvidia-smi', '-i', '0', '--query-gpu=name', '--format=csv,noheader'],
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check=True, capture_output=True, text=True, timeout=5)
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environment['device'] = probe.stdout.strip()
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environment['deviceProbe'] = 'nvidia-smi; no model or CUDA inference executed'
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except (OSError, subprocess.SubprocessError) as error:
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environment['deviceProbeError'] = str(error)
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for run_id in sorted({entry['originalRunId'] for entry in entries.values()}):
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manifest_path = Path(root) / 'generated' / run_id / 'manifest.json'
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source = load_json(manifest_path)
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environment['sourceGenerations'].append({'runId': run_id,
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'manifest': manifest_path.relative_to(root).as_posix(),
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'manifestSha256': file_hash(manifest_path),
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'environment': source['environment']})
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return environment
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def generate(catalog, profile, root, ids=None, synthesize=None, environment=None, regenerate=False):
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validate_inputs(catalog, profile)
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selected = select_clips(catalog, ids)
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root = Path(root)
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previous = {} if regenerate else reusable_entries(root, catalog, profile)
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run_id = datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ_') + uuid.uuid4().hex[:8]
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run_dir = root / 'generated' / run_id
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run_dir.mkdir(parents=True)
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write_json(run_dir / 'catalog.json', catalog)
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write_json(run_dir / 'profile.json', profile)
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manifest = {'schemaVersion': 1, 'runId': run_id, 'createdUtc': datetime.now(timezone.utc).isoformat(),
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'modelRevision': profile['modelRevision'], 'catalogHash': digest(catalog),
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'profileHash': digest(profile), 'scope': 'selected' if ids else 'all',
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'regenerateRequested': regenerate,
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'inputSnapshots': {'catalog': 'catalog.json', 'profile': 'profile.json'},
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'requestedIds': [c['id'] for c in selected], 'environment': environment or {}, 'clips': []}
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try:
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for clip in selected:
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clip_id = clip['id']
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if clip_id in previous:
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entry = dict(previous[clip_id], result='reused')
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else:
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entry = {'id': clip_id, 'textHash': digest(clip['text']), 'settingsHash': digest(profile),
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'originalRunId': run_id, 'file': f'generated/{run_id}/{clip_id}.wav',
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'review': 'not_listened'}
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started = time.perf_counter()
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try:
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if synthesize is None:
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raise ValueError('no synthesizer available')
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samples, rate = synthesize(clip)
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samples = np.asarray(samples, dtype=np.float32)
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if samples.ndim != 1 or not samples.size or not np.isfinite(samples).all() or rate <= 0:
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raise ValueError('synthesizer returned empty/nonfinite/nonmono audio')
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path = root / entry['file']
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sf.write(path, samples, rate, subtype='FLOAT')
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entry.update(waveform_metadata(path), result='generated', generationSeconds=time.perf_counter() - started)
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except Exception as error:
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entry.update(result='failed', error=f'{type(error).__name__}: {error}')
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manifest['clips'].append(entry)
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write_json(run_dir / 'manifest.json', manifest)
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print(f"{clip_id}: {entry['result']}", flush=True)
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finally:
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write_json(run_dir / 'manifest.json', manifest)
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return manifest
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def load_synthesizer(profile):
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import torch
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from huggingface_hub import snapshot_download
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from qwen_tts import Qwen3TTSModel
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if not torch.cuda.is_available():
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raise RuntimeError('CUDA unavailable; refusing an unapproved CPU fallback')
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probe = torch.arange(8, device=profile['device'], dtype=torch.float32).square().sum().item()
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if probe != 140:
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raise RuntimeError('CUDA arithmetic probe failed')
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snapshot = snapshot_download(repo_id=profile['modelId'], revision=profile['modelRevision'])
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started = time.perf_counter()
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torch.cuda.reset_peak_memory_stats()
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model = Qwen3TTSModel.from_pretrained(snapshot, device_map=profile['device'],
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dtype=getattr(torch, profile['dtype']), attn_implementation=profile['attention'])
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model_device = str(next(model.model.parameters()).device)
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if model_device != profile['device']:
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raise RuntimeError(f'loaded model device {model_device} differs from requested CUDA device')
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if 'sohee' not in [s.lower() for s in model.get_supported_speakers()] or 'korean' not in [s.lower() for s in model.get_supported_languages()]:
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raise RuntimeError('Sohee/Korean not supported by loaded model')
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environment = {'python': sys.version, 'packages': {name: importlib.metadata.version(name) for name in
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('qwen-tts', 'torch', 'torchaudio', 'transformers', 'soundfile', 'huggingface-hub')},
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'device': torch.cuda.get_device_name(0), 'cuda': torch.version.cuda,
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'modelDevice': model_device,
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'cudaProbe': probe, 'modelLoadSeconds': time.perf_counter() - started,
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'modelPeakVramBytes': torch.cuda.max_memory_allocated(),
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'modelDefaults': model.generate_defaults,
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'effectiveGenerationParameters': model._merge_generate_kwargs(**profile['generationParameters'])}
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def synthesize(clip):
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torch.manual_seed(profile['seed'])
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torch.cuda.manual_seed_all(profile['seed'])
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waves, rate = model.generate_custom_voice(text=clip['text'], language=profile['language'],
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speaker=profile['speaker'], instruct=profile['instruct'],
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non_streaming_mode=True, **profile['generationParameters'])
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torch.cuda.synchronize()
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environment['peakVramBytes'] = torch.cuda.max_memory_allocated()
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return waves[0], rate
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return synthesize, environment
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--catalog', required=True, type=Path)
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parser.add_argument('--profile', required=True, type=Path)
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parser.add_argument('--ids', help='comma separated IDs; absent means all catalog clips')
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parser.add_argument('--validate-only', action='store_true')
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parser.add_argument('--manifest', type=Path)
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parser.add_argument('--regenerate', action='store_true', help='create a new take for selected IDs, preserving original runs')
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args = parser.parse_args()
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ids = args.ids.split(',') if args.ids else None
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try:
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catalog, profile = load_json(args.catalog), load_json(args.profile)
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validate_inputs(catalog, profile)
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root = args.catalog.parent
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if args.validate_only:
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if not args.manifest:
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raise ValueError('--validate-only requires --manifest')
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manifest = load_json(args.manifest)
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errors = validate_manifest(manifest, catalog, profile, root, ids)
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else:
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selected = select_clips(catalog, ids)
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reusable = reusable_entries(root, catalog, profile)
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if not args.regenerate and all(c['id'] in reusable for c in selected):
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synthesize = None
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environment = reuse_environment(profile, {c['id']: reusable[c['id']] for c in selected}, root)
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else:
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synthesize, environment = load_synthesizer(profile)
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manifest = generate(catalog, profile, root, ids, synthesize, environment, args.regenerate)
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print('Manifest: ' + str(root / 'generated' / manifest['runId'] / 'manifest.json'))
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errors = validate_manifest(manifest, catalog, profile, root, ids)
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print(json.dumps({'validationScope': 'selected' if ids else 'all_required', 'ids': ids,
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'errors': errors}, ensure_ascii=False))
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return 1 if errors else 0
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except Exception as error:
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print(f'{type(error).__name__}: {error}', file=sys.stderr)
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return 1
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if __name__ == '__main__':
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sys.exit(main())
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