[fix] remove useless runtime

docs/runtime.md

@@ -8,7 +8,6 @@ The Wespeaker runtime supports the following platforms.
     - [TensorRT GPU](https://github.com/wenet-e2e/wespeaker/tree/master/runtime/server/x86_gpu)
 
 - Device
-    - [Horizon X3 PI](https://github.com/wenet-e2e/wespeaker/tree/master/runtime/horizonbpu)
     - [Onnxruntime](https://github.com/wenet-e2e/wespeaker/tree/master/runtime/onnxruntime)
         - linux_x86_cpu
         - linux_x86_gpu
@@ -85,109 +84,6 @@ onnx_dir=your_model_dir
     --embedding_size 256
 ```
 
-## Horizonbpu
-
-* Step 1. Setup environment (install horizon packages and cross compile tools) in the PC.
-
-```sh
-# Conda env (This conda env is only used for converting bpu models, not for training torch models,
-#   It's OK to install cpu-version pytorch)
-conda create -n horizonbpu python=3.8
-conda activate horizonbpu
-git clone https://github.com/wenet-e2e/wespeaker.git
-cd wespeaker/runtime/horizonbpu
-pip install -r ../../requirements.txt -i https://mirrors.aliyun.com/pypi/simple
-pip install torch==1.13.0 torchaudio==0.13.0 torchvision==0.14.0 onnx onnxruntime -i https://mirrors.aliyun.com/pypi/simple
-
-# Horizon packages
-wget https://gitee.com/xcsong-thu/toolchain_pkg/releases/download/resource/wheels.tar.gz
-tar -xzf wheels.tar.gz
-pip install wheels/* -i https://mirrors.aliyun.com/pypi/simple
-
-# Cross compile tools
-sudo apt-get install gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
-```
-
-* Step 2. Build extract_emb_main. It requires cmake 3.14 or above. and Send the binary/libraries to Horizon X3PI.
-
-```bash
-# Assume current dir is `wespeaker/runtime/horizonbpu`
-cmake -B build -DBPU=ON -DONNX=OFF -DCMAKE_TOOLCHAIN_FILE=toolchains/aarch64-linux-gnu.toolchain.cmake
-cmake --build build
-
-# Send binary and libraries
-export BPUIP=xxx.xxx.xxx.xxx
-export DEMO_PATH_ON_BOARD=/path/to/demo
-scp -r build/bin/ root@$BPUIP:$DEMO_PATH_ON_BOARD
-scp fc_base/easy_dnn-src/dnn/*j3*/*/*/lib/libdnn.so root@$BPUIP:$DEMO_PATH_ON_BOARD
-scp fc_base/easy_dnn-src/easy_dnn/*j3*/*/*/lib/libeasy_dnn.so root@$BPUIP:$DEMO_PATH_ON_BOARD
-scp fc_base/easy_dnn-src/hlog/*j3*/*/*/lib/libhlog.so root@$BPUIP:$DEMO_PATH_ON_BOARD
-```
-
-* Step 3. Export model to ONNX and convert ONNX to Horizon .bin and Send the model/test_wav to Horizon X3PI.
-
-
-```bash
-# stage 1: export onnx
-export WESPEAKER_DIR=$PWD/../../
-export PYTHONPATH=$WESPEAKER_DIR:$PYTHONPATH
-mkdir -p output
-python $WESPEAKER_DIR/wespeaker/bin/export_onnx_bpu.py \
-  --config the_path_train_yaml \
-  --checkpoint the_path_pt_model \
-  --output_model the_path_onnx_model \
-  --num_frames num_frames
-# egs:
-# python $WESPEAKER_DIR/wespeaker/bin/export_onnx_bpu.py \
-#   --config resnet_34/train.yaml \
-#   --checkpoint resnet_34/avg_model.pt \
-#   --output_model resnet_34/resnet34.onnx \
-#   --num_frames 198
-
-# stage 2: export bin model
-python $WESPEAKER_DIR/tools/onnx2horizonbin.py \
-  --config the_path_train_yaml \
-  --output_dir output_dir \
-  --cali_datalist data_list \
-  --onnx_path fp_onnx_path \
-  --cali_data_type "shard/raw" \
-  --input_name input_name \
-  --input_shape input_shape
-
-# egs:
-# python $WESPEAKER_DIR/tools/onnx2horizonbin.py \
-#   --config resnet_34/config.yaml \
-#   --output_dir output/ \
-#   --cali_datalist cali_data/raw.list \
-#   --onnx_path resnet_34/resnet34.onnx \
-#   --cali_data_type "raw" \
-#   --input_name "feats" \
-#   --input_shape "1x1x198x80"
-
-# scp test wav file
-scp test_wav.wav root@$BPUIP:$DEMO_PATH_ON_BOARD
-# scp bpu models
-scp ./output/hb_makertbin_output_speaker/speaker.bin root@$BPUIP:$DEMO_PATH_ON_BOARD
-```
-
-* Step 4. Testing on X3PI, the RTF(real time factor) is shown in Horizon X3PI's console.
-
-```bash
-cd /path/to/demo
-export LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH \
-export GLOG_logtostderr=1
-export GLOG_v=2
-wav_scp=your_test_wav_scp
-embed_out=your_embedding_txt
-./build/bin/extract_emb_main \
-  --wav_scp $wav_scp \
-  --result $embed_out \
-  --speaker_model_path speaker.bin \
-  --embedding_size 256 \
-  --SamplesPerChunk  32000  # 2s
-```
-
-
 ## Server (tensorrt gpu)
 
 ### Introduction

runtime/core/bin/extract_emb_main.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include <fstream>
+#include <iomanip>
 #include <iostream>
 #include <sstream>
 #include <string>

runtime/core/speaker/CMakeLists.txt

@@ -6,16 +6,12 @@ if(NOT ONNX AND NOT BPU)
 endif()
 if(ONNX)
   list(APPEND speaker_srcs onnx_speaker_model.cc)
-else()
-  list(APPEND speaker_srcs bpu_speaker_model.cc)
 endif()
 
 add_library(speaker STATIC ${speaker_srcs})
 target_link_libraries(speaker PUBLIC frontend)
 
 if(ONNX)
   target_link_libraries(speaker PUBLIC onnxruntime)
-else()
-  target_link_libraries(speaker PUBLIC easy_dnn dnn)
 endif()
 

runtime/core/speaker/speaker_engine.cc

@@ -20,8 +20,6 @@
 
 #ifdef USE_ONNX
 #include "speaker/onnx_speaker_model.h"
-#elif USE_BPU
-#include "speaker/bpu_speaker_model.h"
 #endif
 
 namespace wespeaker {