5c9eda4fbd
asr-transcribe-to-text: - Add local MLX transcription path (macOS Apple Silicon, 15-27x realtime) - Add bundled script transcribe_local_mlx.py with max_tokens=200000 - Add local_mlx_guide.md with benchmarks and truncation trap docs - Auto-detect platform and recommend local vs remote mode - Fix audio extraction format (MP3 → WAV 16kHz mono PCM) - Add Step 5: recommend transcript-fixer after transcription transcript-fixer: - Optimize SKILL.md from 289 → 153 lines (best practices compliance) - Move FALSE_POSITIVE_RISKS (40 lines) to references/false_positive_guide.md - Move Example Session to references/example_session.md - Improve description for better triggering (226 → 580 chars) - Add handoff to meeting-minutes-taker skill-creator: - Add "Pipeline Handoff" pattern to Skill Writing Guide - Add pipeline check reminder in Step 4 (Edit the Skill) Pipeline handoffs added to 8 skills forming 6 chains: - youtube-downloader → asr-transcribe-to-text → transcript-fixer → meeting-minutes-taker → pdf/ppt-creator - deep-research → fact-checker → pdf/ppt-creator - doc-to-markdown → docs-cleaner / fact-checker - claude-code-history-files-finder → continue-claude-work Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = ["mlx-audio>=0.3.1"]
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# ///
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"""
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Local ASR transcription using mlx-audio + Qwen3-ASR on Apple Silicon.
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Usage:
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uv run scripts/transcribe_local_mlx.py INPUT_AUDIO [INPUT_AUDIO2 ...] [--output-dir DIR]
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CRITICAL: max_tokens defaults to 200000. The upstream mlx-audio default (8192)
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silently truncates audio longer than ~40 minutes. This was discovered empirically:
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123 minutes of Chinese speech requires ~24,000 tokens. 8192 only covers the first
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~40 minutes before the token budget is exhausted and remaining chunks are skipped.
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"""
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import argparse
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import os
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import platform
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import sys
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import time
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def check_platform():
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if sys.platform != "darwin" or platform.machine() not in ("arm64", "aarch64"):
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print("ERROR: Local MLX transcription requires macOS on Apple Silicon (M1+).", file=sys.stderr)
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print("Use the remote API mode instead.", file=sys.stderr)
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sys.exit(1)
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def main():
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parser = argparse.ArgumentParser(description="Transcribe audio/video using local MLX Qwen3-ASR")
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parser.add_argument("inputs", nargs="+", help="Audio/video file paths")
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parser.add_argument("--output-dir", default=None, help="Output directory (default: same as input)")
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parser.add_argument("--model", default="mlx-community/Qwen3-ASR-1.7B-8bit",
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help="HuggingFace model ID (default: mlx-community/Qwen3-ASR-1.7B-8bit)")
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parser.add_argument("--max-tokens", type=int, default=200000,
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help="Max tokens for generation (default: 200000, covers ~3 hours of speech)")
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args = parser.parse_args()
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check_platform()
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from mlx_audio.stt.generate import load_model
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print(f"Loading model {args.model}...", file=sys.stderr, flush=True)
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t0 = time.time()
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model = load_model(args.model)
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load_time = time.time() - t0
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print(f"Model loaded in {load_time:.1f}s", file=sys.stderr, flush=True)
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for audio_path in args.inputs:
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if not os.path.exists(audio_path):
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print(f"SKIP: {audio_path} not found", file=sys.stderr)
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continue
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name = os.path.splitext(os.path.basename(audio_path))[0]
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out_dir = args.output_dir or os.path.dirname(audio_path) or "."
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output_path = os.path.join(out_dir, f"{name}.txt")
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print(f"\nTranscribing: {os.path.basename(audio_path)}", file=sys.stderr, flush=True)
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t1 = time.time()
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result = model.generate(audio_path, max_tokens=args.max_tokens, verbose=True)
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elapsed = time.time() - t1
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text = result.text if hasattr(result, "text") else str(result)
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gen_tokens = result.generation_tokens if hasattr(result, "generation_tokens") else "N/A"
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(text)
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print(f"Done: {elapsed:.1f}s, {len(text)} chars, {gen_tokens} tokens → {output_path}",
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file=sys.stderr, flush=True)
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total = time.time() - t0
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print(f"\nAll done. Total: {total:.1f}s", file=sys.stderr, flush=True)
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if __name__ == "__main__":
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main()
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