[speechx] fix nnet input and output name

.flake8

@@ -12,6 +12,8 @@ exclude =
     .git,
     # python cache
     __pycache__,
+    # third party
+    utils/compute-wer.py,
     third_party/,
 # Provide a comma-separate list of glob patterns to include for checks.
 filename =

paddlespeech/cli/asr/infer.py

@@ -40,6 +40,7 @@
 
 __all__ = ['ASRExecutor']
 
+
 @cli_register(
     name='paddlespeech.asr', description='Speech to text infer command.')
 class ASRExecutor(BaseExecutor):
@@ -148,7 +149,7 @@ def _init_from_path(self,
                 os.path.dirname(os.path.abspath(self.cfg_path)))
         logger.info(self.cfg_path)
         logger.info(self.ckpt_path)
-        
+
         #Init body.
         self.config = CfgNode(new_allowed=True)
         self.config.merge_from_file(self.cfg_path)
@@ -278,7 +279,8 @@ def infer(self, model_type: str):
             self._outputs["result"] = result_transcripts[0]
 
         elif "conformer" in model_type or "transformer" in model_type:
-            logger.info(f"we will use the transformer like model : {model_type}")
+            logger.info(
+                f"we will use the transformer like model : {model_type}")
             try:
                 result_transcripts = self.model.decode(
                     audio,

paddlespeech/s2t/models/u2/u2.py

@@ -279,7 +279,7 @@ def recognize(
             # TODO(Hui Zhang): if end_flag.sum() == running_size:
             if end_flag.cast(paddle.int64).sum() == running_size:
                 break
-            
+
             # 2.1 Forward decoder step
             hyps_mask = subsequent_mask(i).unsqueeze(0).repeat(
                 running_size, 1, 1).to(device)  # (B*N, i, i)

paddlespeech/s2t/modules/ctc.py

@@ -180,7 +180,7 @@ def _init_ext_scorer(self, beam_alpha, beam_beta, language_model_path,
         # init once
         if self._ext_scorer is not None:
             return
-        
+
         if language_model_path != '':
             logger.info("begin to initialize the external scorer "
                         "for decoding")

paddlespeech/server/README.md

@@ -47,4 +47,4 @@ paddlespeech_server start --config_file conf/ws_conformer_application.yaml
 
 ```
 paddlespeech_client asr_online  --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
-```
+```

paddlespeech/server/README_cn.md

@@ -48,4 +48,4 @@ paddlespeech_server start --config_file conf/ws_conformer_application.yaml
 
 ```
 paddlespeech_client asr_online  --server_ip 127.0.0.1 --port 8090 --input zh.wav
-```
+```

paddlespeech/server/engine/asr/online/ctc_search.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import defaultdict
+
 import paddle
+
 from paddlespeech.cli.log import logger
 from paddlespeech.s2t.utils.utility import log_add
 

paddlespeech/t2s/exps/synthesize.py

@@ -52,7 +52,7 @@ def evaluate(args):
     # acoustic model
     am_name = args.am[:args.am.rindex('_')]
     am_dataset = args.am[args.am.rindex('_') + 1:]
-    
+
     am_inference = get_am_inference(
         am=args.am,
         am_config=am_config,

paddlespeech/vector/cluster/diarization.py

@@ -20,11 +20,11 @@
 import argparse
 import copy
 import warnings
-from distutils.util import strtobool
 
 import numpy as np
 import scipy
 import sklearn
+from distutils.util import strtobool
 from scipy import linalg
 from scipy import sparse
 from scipy.sparse.csgraph import connected_components

speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc

@@ -34,10 +34,12 @@ DEFINE_int32(receptive_field_length,
 DEFINE_int32(downsampling_rate,
              4,
              "two CNN(kernel=5) module downsampling rate.");
+DEFINE_string(
+    model_input_names,
+    "audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
+    "model input names");
 DEFINE_string(model_output_names,
-              "save_infer_model/scale_0.tmp_1,save_infer_model/"
-              "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
-              "scale_3.tmp_1",
+              "softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
               "model output names");
 DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
 
@@ -76,6 +78,7 @@ int main(int argc, char* argv[]) {
     model_opts.model_path = model_path;
     model_opts.params_path = model_params;
     model_opts.cache_shape = FLAGS_model_cache_names;
+    model_opts.input_names = FLAGS_model_input_names;
     model_opts.output_names = FLAGS_model_output_names;
     std::shared_ptr<ppspeech::PaddleNnet> nnet(
         new ppspeech::PaddleNnet(model_opts));

speechx/examples/ds2_ol/decoder/run.sh

@@ -48,7 +48,6 @@ if [ ! -f $lm ]; then
     popd
 fi
 
-
 feat_wspecifier=$exp_dir/feats.ark
 cmvn=$exp_dir/cmvn.ark
 
@@ -57,7 +56,7 @@ export GLOG_logtostderr=1
 # dump json cmvn to kaldi
 cmvn-json2kaldi \
     --json_file  $ckpt_dir/data/mean_std.json \
-    --cmvn_write_path $exp_dir/cmvn.ark \
+    --cmvn_write_path $cmvn \
     --binary=false
 echo "convert json cmvn to kaldi ark."
 
@@ -66,7 +65,7 @@ echo "convert json cmvn to kaldi ark."
 linear-spectrogram-wo-db-norm-ol \
     --wav_rspecifier=scp:$data/wav.scp \
     --feature_wspecifier=ark,t:$feat_wspecifier \
-    --cmvn_file=$exp_dir/cmvn.ark
+    --cmvn_file=$cmvn
 echo "compute linear spectrogram feature."
 
 # run ctc beam search decoder as streaming

speechx/examples/ds2_ol/decoder/wfst-decoder-ol.cc

@@ -37,10 +37,12 @@ DEFINE_int32(receptive_field_length,
 DEFINE_int32(downsampling_rate,
              4,
              "two CNN(kernel=5) module downsampling rate.");
+DEFINE_string(
+    model_input_names,
+    "audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
+    "model input names");
 DEFINE_string(model_output_names,
-              "save_infer_model/scale_0.tmp_1,save_infer_model/"
-              "scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
-              "scale_3.tmp_1",
+              "softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
               "model output names");
 DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
 
@@ -79,6 +81,7 @@ int main(int argc, char* argv[]) {
     model_opts.model_path = model_graph;
     model_opts.params_path = model_params;
     model_opts.cache_shape = FLAGS_model_cache_names;
+    model_opts.input_names = FLAGS_model_input_names;
     model_opts.output_names = FLAGS_model_output_names;
     std::shared_ptr<ppspeech::PaddleNnet> nnet(
         new ppspeech::PaddleNnet(model_opts));

speechx/examples/ds2_ol/feat/CMakeLists.txt

@@ -9,4 +9,4 @@ target_link_libraries(${bin_name} frontend kaldi-util kaldi-feat-common gflags g
 set(bin_name cmvn-json2kaldi)
 add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
 target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
-target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog)
+target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog ${DEPS})

speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc

@@ -14,68 +14,72 @@
 
 // Note: Do not print/log ondemand object.
 
+#include "base/common.h"
 #include "base/flags.h"
 #include "base/log.h"
 #include "kaldi/matrix/kaldi-matrix.h"
 #include "kaldi/util/kaldi-io.h"
 #include "utils/file_utils.h"
-#include "utils/simdjson.h"
+// #include "boost/json.hpp"
+#include <boost/json/src.hpp>
 
 DEFINE_string(json_file, "", "cmvn json file");
 DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
 DEFINE_bool(binary, true, "write cmvn in binary (true) or text(false)");
 
-using namespace simdjson;
+using namespace boost::json;  // from <boost/json.hpp>
 
 int main(int argc, char* argv[]) {
     gflags::ParseCommandLineFlags(&argc, &argv, false);
     google::InitGoogleLogging(argv[0]);
 
     LOG(INFO) << "cmvn josn path: " << FLAGS_json_file;
 
-    try {
-        padded_string json = padded_string::load(FLAGS_json_file);
-
-        ondemand::parser parser;
-        ondemand::document doc = parser.iterate(json);
-        ondemand::value val = doc;
+    auto ifs = std::ifstream(FLAGS_json_file);
+    std::string json_str = ppspeech::ReadFile2String(FLAGS_json_file);
+    auto value = boost::json::parse(json_str);
+    if (!value.is_object()) {
+        LOG(ERROR) << "Input json file format error.";
+    }
 
-        ondemand::array mean_stat = val["mean_stat"];
-        std::vector<kaldi::BaseFloat> mean_stat_vec;
-        for (double x : mean_stat) {
-            mean_stat_vec.push_back(x);
+    for (auto obj : value.as_object()) {
+        if (obj.key() == "mean_stat") {
+            LOG(INFO) << "mean_stat:" << obj.value();
         }
-        // LOG(INFO) << mean_stat; this line will casue
-        // simdjson::simdjson_error("Objects and arrays can only be iterated
-        // when
-        // they are first encountered")
-
-        ondemand::array var_stat = val["var_stat"];
-        std::vector<kaldi::BaseFloat> var_stat_vec;
-        for (double x : var_stat) {
-            var_stat_vec.push_back(x);
+        if (obj.key() == "var_stat") {
+            LOG(INFO) << "var_stat: " << obj.value();
         }
-
-        kaldi::int32 frame_num = uint64_t(val["frame_num"]);
-        LOG(INFO) << "nframe: " << frame_num;
-
-        size_t mean_size = mean_stat_vec.size();
-        kaldi::Matrix<double> cmvn_stats(2, mean_size + 1);
-        for (size_t idx = 0; idx < mean_size; ++idx) {
-            cmvn_stats(0, idx) = mean_stat_vec[idx];
-            cmvn_stats(1, idx) = var_stat_vec[idx];
+        if (obj.key() == "frame_num") {
+            LOG(INFO) << "frame_num: " << obj.value();
         }
-        cmvn_stats(0, mean_size) = frame_num;
-        LOG(INFO) << cmvn_stats;
+    }
+
+    boost::json::array mean_stat = value.at("mean_stat").as_array();
+    std::vector<kaldi::BaseFloat> mean_stat_vec;
+    for (auto it = mean_stat.begin(); it != mean_stat.end(); it++) {
+        mean_stat_vec.push_back(it->as_double());
+    }
 
-        kaldi::WriteKaldiObject(
-            cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary);
-        LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path;
-        LOG(INFO) << "Binary: " << FLAGS_binary;
-    } catch (simdjson::simdjson_error& err) {
-        LOG(ERROR) << err.what();
+    boost::json::array var_stat = value.at("var_stat").as_array();
+    std::vector<kaldi::BaseFloat> var_stat_vec;
+    for (auto it = var_stat.begin(); it != var_stat.end(); it++) {
+        var_stat_vec.push_back(it->as_double());
     }
 
+    kaldi::int32 frame_num = uint64_t(value.at("frame_num").as_int64());
+    LOG(INFO) << "nframe: " << frame_num;
+
+    size_t mean_size = mean_stat_vec.size();
+    kaldi::Matrix<double> cmvn_stats(2, mean_size + 1);
+    for (size_t idx = 0; idx < mean_size; ++idx) {
+        cmvn_stats(0, idx) = mean_stat_vec[idx];
+        cmvn_stats(1, idx) = var_stat_vec[idx];
+    }
+    cmvn_stats(0, mean_size) = frame_num;
+    LOG(INFO) << cmvn_stats;
 
+    kaldi::WriteKaldiObject(cmvn_stats, FLAGS_cmvn_write_path, FLAGS_binary);
+    LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path;
+    LOG(INFO) << "Binary: " << FLAGS_binary;
     return 0;
 }

speechx/examples/ngram/zh/local/text_to_lexicon.py

@@ -2,6 +2,7 @@
 import argparse
 from collections import Counter
 
+
 def main(args):
     counter = Counter()
     with open(args.text, 'r') as fin, open(args.lexicon, 'w') as fout:
@@ -12,29 +13,24 @@ def main(args):
                 words = text.split()
             else:
                 words = line.split()
-            
+
             counter.update(words)
 
         for word in counter:
             val = " ".join(list(word))
             fout.write(f"{word}\t{val}\n")
             fout.flush()
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         description='text(line:utt1 中国 人) to lexicon（line:中国 中 国).')
     parser.add_argument(
-        '--has_key',
-        default=True,
-        help='text path, with utt or not')
+        '--has_key', default=True, help='text path, with utt or not')
     parser.add_argument(
-        '--text',
-        required=True,
-        help='text path. line: utt1 中国 人 or 中国 人')
+        '--text', required=True, help='text path. line: utt1 中国 人 or 中国 人')
     parser.add_argument(
-        '--lexicon',
-        required=True,
-        help='lexicon path. line:中国 中 国')
+        '--lexicon', required=True, help='lexicon path. line:中国 中 国')
     args = parser.parse_args()
     print(args)