iic-SenseVoiceSmall-ASR-NPU:基于 Ascend NPU 的语音识别项目

Adapt iic/SenseVoiceSmall ASR model for Ascend NPU

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iic/SenseVoiceSmall - 昇腾NPU语音识别

模型信息

  • 模型:SenseVoiceSmall(iic/SenseVoiceSmall)
  • 架构:FunASR(带SAN-M transformer的非自回归编码器)
  • 任务:自动语音识别
  • 语言:多语言(中/英/日/粤/韩)
  • 采样率:16kHz

文件说明

  • inference.py - 在NPU上运行语音识别推理
  • eval_accuracy.py - 比较CPU与NPU输出精度
  • benchmark.py - 性能基准测试
  • model_utils.py - 模型加载工具

使用方法

# Inference
python inference.py

# Accuracy check
python eval_accuracy.py

# Benchmark
python benchmark.py

推理输出示例

Task: Automatic Speech Recognition
Model: iic/SenseVoiceSmall
Device: npu:0
Input: test_audio.wav (7.36s)
Transcript: <|en|><|EMO_UNKNOWN|><|Speech|><|withitn|>The quick brown f jumps over the lazy dog,
            this is a test of automatic speech recognition.

精度验证(CPU 与 NPU)

=== CPU-NPU ASR Transcription Accuracy Check ===
model             iic/SenseVoiceSmall
cpu_text          <|en|><|emo_unknown|><|speech|><|withitn|>the quick brown f jumps over the la.
npu_text          <|en|><|emo_unknown|><|speech|><|withitn|>the quick brown f jumps over the la.
edit_distance     0
relative_error    0.00%
exact_match       1.0
result            PASS

性能

  • NPU 推理:7.36 秒音频耗时约 225 毫秒(实时率 RTF=0.031)
  • CPU 推理:7.36 秒音频耗时约 6257 毫秒(实时率 RTF=0.85)
  • NPU 速度约为 CPU 的 28 倍

项目介绍

Adapt iic/SenseVoiceSmall ASR model for Ascend NPU

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