Automatic Speech Recognition
MLX
ONNX
GGUF
Rust
English
Chinese
audio8
streaming-asr
quantized
experimental
Instructions to use Reza2kn/Audio8-ASR-Infinite-Compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Reza2kn/Audio8-ASR-Infinite-Compressed with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Reza2kn/Audio8-ASR-Infinite-Compressed --local-dir Audio8-ASR-Infinite-Compressed
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,215 Bytes
fddfb10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | """Supplement GPU latency with ASR session boundaries, preserving the original score."""
from pathlib import Path
import argparse
import json
from verify_mlx_q3_memory_regression_v1 import timing_audit, sha
def main():
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--directory', type=Path, required=True)
args = p.parse_args()
root = args.directory
output = root / 'causal-alignment-audit.json'
assert not output.exists()
report = json.loads((root / 'run.json').read_text())
assert report['status'] == 'complete'
raw = root / 'run.jsonl'
obs = root / 'run.observed.jsonl'
assert sha(raw) == report['logs']['raw']['sha256']
assert sha(obs) == report['logs']['observed']['sha256']
rows = [json.loads(line) for line in raw.open()]
observations = [json.loads(line) for line in obs.open()]
# Derived times use the same recorded origin as the unchanged primary score.
for row, receipt in zip(rows, observations, strict=True):
assert row['kind'] == receipt['kind'] and row.get('index') == receipt['index']
receipt['received_seconds_since_feeder_start'] = (
receipt['received_monotonic_seconds'] - report['feeder']['feeder_started_monotonic'])
audit = timing_audit(dict(score=report['caption_score']), rows, observations)
result = dict(status='supplementary_audit_complete', audit=audit,
original_counts=report['caption_score']['counts'],
original_alignment_timing=report['caption_score']['aligned_word_timing'],
production_ready=False, whole_release_pass=False, not_a_replacement_score=True,
method='Textual matches wholly outside the emitting ASR session cannot identify the spoken word for latency. They remain unresolved recognition evidence, never zero-latency successes. Original global score and latency are retained.',
inputs={str(path): sha(path) for path in (root / 'run.json', raw, obs, Path(__file__),
Path(__file__).with_name('verify_mlx_q3_memory_regression_v1.py'))})
output.write_text(json.dumps(result, indent=2) + '\n')
print(json.dumps(audit))
if __name__ == '__main__':
main()
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