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
Download sources/scripts/audit_gpu_caption_cross_reset_v1.py from Reza2kn/Audio8-ASR-Infinite-Compressed: direct link, hf CLI and curl.
- Browser
- Download file 2.22 kB
-
https://huggingface.co/Reza2kn/Audio8-ASR-Infinite-Compressed/resolve/main/sources/scripts/audit_gpu_caption_cross_reset_v1.py
- Command line
-
hf download hf://Reza2kn/Audio8-ASR-Infinite-Compressed/sources/scripts/audit_gpu_caption_cross_reset_v1.py
-
curl -L -o audit_gpu_caption_cross_reset_v1.py https://huggingface.co/Reza2kn/Audio8-ASR-Infinite-Compressed/resolve/main/sources/scripts/audit_gpu_caption_cross_reset_v1.py
2.22 kB
| """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() | |