import argparse import json import sys import time from pathlib import Path import numpy as np sys.path.insert(0, str(Path(__file__).resolve().parents[1])) from h_gtcrn_core_sdk.inference import ModelSession from h_gtcrn_core_sdk.postprocess import postprocess from h_gtcrn_core_sdk.preprocess import preprocess def main(): parser = argparse.ArgumentParser(description="H-GTCRN-core inference example") parser.add_argument("--model", required=True, help="AXMODEL 路径") parser.add_argument("--input", nargs="+", required=True, help="输入 npy(每输入一个,与 model_meta 顺序一致)") parser.add_argument("--output-dir", default="output", help="输出目录") parser.add_argument("--bench", type=int, default=1, help="统计推理次数") parser.add_argument("--audio-seconds", type=float, default=10.0, help="用于 RTF 统计的音频时长") args = parser.parse_args() arrays = [np.load(p).astype(np.float32) for p in args.input] session = ModelSession(args.model) feeds = preprocess(*arrays) start = time.perf_counter() raw = session.run_named(feeds) first_ms = (time.perf_counter() - start) * 1000.0 bench_ms = first_ms if args.bench > 1: start = time.perf_counter() for _ in range(args.bench): raw = session.run_named(feeds) bench_ms = (time.perf_counter() - start) * 1000.0 / args.bench result = postprocess(raw) out_dir = Path(args.output_dir) out_dir.mkdir(parents=True, exist_ok=True) for i, arr in enumerate(raw.values()): np.save(out_dir / f"output_{i}.npy", np.asarray(arr, dtype=np.float32)) try: json.dumps(result) (out_dir / "result.json").write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8") except TypeError: np.save(out_dir / "result.npy", np.asarray(result, dtype=np.float32)) print("backend:", session.backend) print("inputs:", session.input_names) print("outputs:", session.output_names) print("first_run_ms: %.3f" % first_ms) print("bench_avg_ms: %.3f" % bench_ms) print("core_rtf: %.6f" % (bench_ms / 1000.0 / args.audio_seconds)) print("saved to:", out_dir) if __name__ == "__main__": main()