使用 ModelScope 的 snapshot_download 从本地缓存加载模型,而非直接从 HuggingFace 下载。
from modelscope import snapshot_download
model_dir = snapshot_download("damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch")模型本地路径:models/paraformer_tiny_commandword(项目内相对路径)或 ~/.cache/modelscope/hub/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch
assets/test.wav,时长约 4.08 秒wave + numpy 读取并预处理(兼容无 ffmpeg 环境)python inference.py依赖安装:
pip install -r requirements.txt温度调高| 指标 | 数值 |
|---|---|
| max_abs_error | 0.000994 |
| mean_abs_error | 0.000090 |
| relative_error | 0.061099% |
| cosine_similarity | 1.000000 |
| threshold | 1.0% |
| result | PASS |
| 指标 | 数值 |
|---|---|
| avg_latency_ms | 28.06 |
| min_latency_ms | 27.32 |
| max_latency_ms | 28.38 |
| p50_latency_ms | 28.21 |
| p90_latency_ms | 28.33 |
| p95_latency_ms | 28.36 |
| audio_duration_sec | 4.08 |
| real_time_factor | 0.0069 |
.
├── assets/
│ └── test.wav
├── logs/
│ ├── benchmark.log
│ ├── env_check.log
│ ├── eval_consistency.log
│ └── inference.log
├── screenshots/
│ └── self_verification.txt
├── model_utils.py
├── inference.py
├── eval_consistency.py
├── benchmark.py
├── requirements.txt
├── .gitignore
└── README.md依次执行:
python inference.py
python eval_consistency.py
python benchmark.py#NPU