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f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 f722fb4 819e690 | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | """RNNoise 48k 实时降噪演示（AX650 / AX620E 双芯）。
用法:
python3 demo.py --chip ax650 # 默认处理 python/sample_speech.pcm
python3 demo.py --chip ax620e --input in.pcm
输入格式：48kHz f32le PCM（16-bit 等价域，±32768，不做归一化）；也可传 16-bit WAV。
"""
import argparse
import sys
import wave
from pathlib import Path
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
CHIP_MODELS = {
"ax650": ROOT / "rnnoise_ax650" / "model.axmodel",
"ax620e": ROOT / "rnnoise_ax620e" / "model.axmodel",
}
def load_pcm(path: Path) -> np.ndarray:
if path.suffix.lower() == ".wav":
with wave.open(str(path), "rb") as w:
assert w.getframerate() == 48000, "仅支持 48kHz WAV"
assert w.getsampwidth() == 2, "仅支持 16-bit PCM WAV"
raw = w.readframes(w.getnframes())
return np.frombuffer(raw, dtype="<i2").astype(np.float32)
return np.fromfile(path, dtype=np.float32)
def main() -> None:
parser = argparse.ArgumentParser(description="RNNoise 48k 实时降噪（AX650/AX620E）")
parser.add_argument("--chip", choices=["ax650", "ax620e"], default="ax650",
help="目标芯片，决定使用哪个 axmodel")
parser.add_argument("--input", default=str(ROOT / "python" / "sample_speech.pcm"),
help="48k f32 PCM 或 16-bit WAV")
parser.add_argument("--output-dir", default="output")
args = parser.parse_args()
try:
import axengine # noqa: F401
AX_AVAILABLE = True
except Exception:
AX_AVAILABLE = False
if not AX_AVAILABLE:
print("当前主机没有 AX 芯片（pyaxengine 不可用），无法运行 NPU 推理。")
print("请在对应 AX 板端执行：python3 python/demo.py --chip ax650|ax620e")
return
sys.path.insert(0, str(ROOT / "python"))
from rnnoise_sdk import RNNoiseDenoiser, dsp
model = CHIP_MODELS[args.chip]
if not model.is_file():
print(f"模型不存在: {model}（请确认仓库完整）")
sys.exit(1)
pcm = load_pcm(Path(args.input))
print(f"chip: {args.chip} | model: {model.name} | "
f"input: {pcm.size / 48000:.2f}s ({pcm.size // dsp.FRAME_SIZE} 帧)")
denoiser = RNNoiseDenoiser(str(model))
out, vads = denoiser.process(pcm)
out_dir = Path(args.output_dir)
out_dir.mkdir(exist_ok=True)
out.astype(np.float32).tofile(out_dir / "out.pcm")
np.save(out_dir / "vad.npy", vads)
print(f"backend: {denoiser.backend}")
print(f"语音存在比例: {float((vads > 0.5).mean()):.2f}")
print(f"输出已保存: {out_dir / 'out.pcm'}")
if __name__ == "__main__":
main()
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