benchmark

funasr/runtime/python/benchmark_onnx.md

@@ -1,6 +1,8 @@
-Benchmark [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) based on Aishell1 test set , the total audio duration is 36108.919 seconds.
+# Benchmark 
 
-(Note: The service has been fully warm up.)
+Aishell1 test set , the total audio duration is 36108.919 seconds. (Note: The service has been fully warm up.)
+
+## [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) 
 
  ### Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz   16core-32processor    with avx512_vnni
 
@@ -46,4 +48,6 @@ Benchmark [Paraformer-large](https://www.modelscope.cn/models/damo/speech_parafo
 |  64 (onnx fp32)  |        153         | 0.0042 |    235.9     |
 |  64 (onnx int8)  |        103         | 0.0029 |    349.9     |
 |  96 (onnx fp32)  |        146         | 0.0041 |    247.0     |
-|  96 (onnx int8)  |        108         | 0.0030 |    334.1     |
+|  96 (onnx int8)  |        108         | 0.0030 |    334.1     |
+
+## [Paraformer](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)

funasr/runtime/python/utils/test_rtf.py

@@ -2,17 +2,24 @@
 import time
 import sys
 import librosa
-backend=sys.argv[1]
-model_dir=sys.argv[2]
-wav_file=sys.argv[3]
+
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_dir', type=str, required=True)
+parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]')
+parser.add_argument('--wav_file', type=int, default=0, help='amp fallback number')
+parser.add_argument('--quantize', type=bool, default=False, help='quantized model')
+args = parser.parse_args()
+
 
 from torch_paraformer import Paraformer
-if backend == "onnxruntime":
+if args.backend == "onnxruntime":
 	from rapid_paraformer import Paraformer
 	
-model = Paraformer(model_dir, batch_size=1, device_id="-1")
+model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize)
 
-wav_file_f = open(wav_file, 'r')
+wav_file_f = open(args.wav_file, 'r')
 wav_files = wav_file_f.readlines()
 
 # warm-up

funasr/runtime/python/utils/test_rtf.sh

@@ -5,28 +5,31 @@ nj=64
 backend=libtorch
 model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/libtorch"
 tag=${backend}_fp32
+quantize='False'
 !
 
 :<<!
 backend=libtorch
 model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/libtorch_fb20"
 tag=${backend}_amp_fb20
+quantize='True'
 !
 
 :<<!
 backend=onnxruntime
 model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/onnx"
 tag=${backend}_fp32
+quantize='False'
 !
 
 :<<!
 backend=onnxruntime
 model_dir="/nfs/zhifu.gzf/export/damo/amp_int8/onnx_dynamic"
 tag=${backend}_fp32
+quantize='True'
 !
 
-#scp=/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp
-scp="/nfs/zhifu.gzf/data_debug/test/wav_1500.scp"
+scp=/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp
 local_scp_dir=/nfs/zhifu.gzf/data_debug/test/${tag}/split$nj
 
 rtf_tool=test_rtf.py
@@ -45,7 +48,7 @@ perl ../../../egs/aishell/transformer/utils/split_scp.pl $scp ${split_scps}
 for JOB in $(seq ${nj}); do
   {
     core_id=`expr $JOB - 1`
-    taskset -c ${core_id} python ${rtf_tool} ${backend} ${model_dir} ${local_scp_dir}/wav.$JOB.scp &> ${local_scp_dir}/log.$JOB.txt
+    taskset -c ${core_id} python ${rtf_tool} --backend ${backend} --model_dir ${model_dir} --wav_file ${local_scp_dir}/wav.$JOB.scp --quantize ${quantize} &> ${local_scp_dir}/log.$JOB.txt
   }&
 
 done