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When using keyword spotter from C-API example Does it something internal from onnx or something we have control over?
➜ sherpa-onnx git:(v1.10.28) ✗ ./build/bin/keywords-spotter-buffered-tokens-keywords-c-api /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/c-api/c-api.cc:SherpaOnnxCreateKeywordSpotter:710 KeywordSpotterConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80, low_freq=20, high_freq=-400, dither=0), model_config=OnlineModelConfig(transducer=OnlineTransducerModelConfig(encoder="sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/encoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx", decoder="sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/decoder-epoch-12-avg-2-chunk-16-left-64.int8.onnx", joiner="sherpa-onnx-kws-zipformer-gigaspeech-3.3M-2024-01-01/joiner-epoch-12-avg-2-chunk-16-left-64.int8.onnx"), paraformer=OnlineParaformerModelConfig(encoder="", decoder=""), wenet_ctc=OnlineWenetCtcModelConfig(model="", chunk_size=16, num_left_chunks=4), zipformer2_ctc=OnlineZipformer2CtcModelConfig(model=""), nemo_ctc=OnlineNeMoCtcModelConfig(model=""), provider_config=ProviderConfig(device=0, provider="coreml", cuda_config=CudaConfig(cudnn_conv_algo_search=1), trt_config=TensorrtConfig(trt_max_workspace_size=2147483647, trt_max_partition_iterations=10, trt_min_subgraph_size=5, trt_fp16_enable="True", trt_detailed_build_log="False", trt_engine_cache_enable="True", trt_engine_cache_path=".", trt_timing_cache_enable="True", trt_timing_cache_path=".",trt_dump_subgraphs="False" )), tokens="", num_threads=1, warm_up=0, debug=True, model_type="", modeling_unit="cjkchar", bpe_vocab=""), max_active_paths=4, num_trailing_blanks=1, keywords_score=3, keywords_threshold=0.1, keywords_file="") /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-transducer-model.cc:GetModelType:52 num_heads=4,4,4,8,4,4 num_encoder_layers=1,1,1,1,1,1 cnn_module_kernels=31,31,15,15,15,31 model_type=zipformer2 T=45 model_author=k2-fsa version=1 comment=streaming zipformer2 left_context_len=64,32,16,8,16,32 decode_chunk_len=32 value_head_dims=12,12,12,12,12,12 encoder_dims=128,128,128,128,128,128 onnx.infer=onnxruntime.quant query_head_dims=32,32,32,32,32,32 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:InitEncoder:100 ---encoder--- num_heads=4,4,4,8,4,4 num_encoder_layers=1,1,1,1,1,1 cnn_module_kernels=31,31,15,15,15,31 model_type=zipformer2 T=45 model_author=k2-fsa version=1 comment=streaming zipformer2 left_context_len=64,32,16,8,16,32 decode_chunk_len=32 value_head_dims=12,12,12,12,12,12 encoder_dims=128,128,128,128,128,128 onnx.infer=onnxruntime.quant query_head_dims=32,32,32,32,32,32 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 encoder_dims: 128 128 128 128 128 128 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 query_head_dims: 32 32 32 32 32 32 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 value_head_dims: 12 12 12 12 12 12 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 num_heads: 4 4 4 8 4 4 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 num_encoder_layers: 1 1 1 1 1 1 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 cnn_module_kernels: 31 31 15 15 15 31 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:operator():122 left_context_len: 64 32 16 8 16 32 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:InitEncoder:131 T: 45 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:InitEncoder:132 decode_chunk_len_: 32 /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:InitDecoder:153 ---decoder--- vocab_size=500 context_size=2 onnx.infer=onnxruntime.quant /Volumes/Internal/sherpa-rs/sys/sherpa-onnx/sherpa-onnx/csrc/online-zipformer2-transducer-model.cc:InitJoiner:178 ---joiner--- onnx.infer=onnxruntime.quant joiner_dim=320 sample rate: 16000, num samples: 267440, duration: 16.72 s Context leak detected, msgtracer returned -1 0:FOREVER
Related thewh1teagle/sherpa-rs#23
The text was updated successfully, but these errors were encountered:
the logs look normal.
could you describe the issue you have?
Sorry, something went wrong.
the logs look normal. could you describe the issue you have?
It shows the following warning in the logs as you can see: Context leak detected, msgtracer returned -1
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When using keyword spotter from C-API example
Does it something internal from onnx or something we have control over?
Related
thewh1teagle/sherpa-rs#23
The text was updated successfully, but these errors were encountered: