| # Here are the model hyper-parameters required for model exporting | |
| exp_dir=pruned_transducer_stateless7_streaming/exp-14M | |
| python ./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \ | |
| --lang-dir data/lang_char \ | |
| --exp-dir $exp_dir \ | |
| --use-averaged-model True \ | |
| --iter 400000 \ | |
| --avg 8 \ | |
| --decode-chunk-len 32 \ | |
| --num-encoder-layers "2,3,2,2,3" \ | |
| --feedforward-dims "320,320,640,640,320" \ | |
| --nhead "4,4,4,4,4" \ | |
| --encoder-dims "160,160,160,160,160" \ | |
| --attention-dims "96,96,96,96,96" \ | |
| --encoder-unmasked-dims "128,128,128,128,128" \ | |
| --decoder-dim 320 \ | |
| --joiner-dim 320 | |
| pushd $exp_dir | |
| pnnx encoder_jit_trace-pnnx.pt | |
| pnnx decoder_jit_trace-pnnx.pt | |
| pnnx joiner_jit_trace-pnnx.pt | |
| popd | |
| # modify encoder_jit_trace-pnnx.ncnn.param to support sherpa-ncnn | |
| # The following is the diff | |
| # --- encoder_jit_trace-pnnx.ncnn.param-before 2023-02-14 20:48:52.000000000 +0800 | |
| # +++ encoder_jit_trace-pnnx.ncnn.param 2023-02-14 20:50:15.000000000 +0800 | |
| # @@ -1,5 +1,6 @@ | |
| # 7767517 | |
| # -2028 2547 | |
| # +2029 2547 | |
| # +SherpaMetaData sherpa_meta_data1 0 0 0=2 1=32 2=4 3=7 -23316=5,2,4,3,2,4 -23317=5,384,384,384,384,384 -23318=5,192,192,192,192,192 -23319=5,1,2,4,8,2 -23320=5,31,31,31,31,31 | |
| # Input in0 0 1 in0 | |
| # Input in1 0 1 in1 | |
| # Split splitncnn_0 1 2 in1 2 3 | |
| # | |
| #------ | |
| # Explanation: | |
| # | |
| # (1) 2028 is changed to 2029 as an extra layer SherpaMetaData is added | |
| # (2) SherpaMetaData is the layer type | |
| # (3) sherpa_meta_data1 is the name of this layer. Must be sherpa_meta_data1 | |
| # (4) 0 0 means this layer has no input or output | |
| # (5) 1=32, attribute 1, 32 is the value of --decode-chunk-len | |
| # (6) 2=4, attribute 2, 4 is the value of --num-left-chunks | |
| # (7) 3=7, attribute 3, 7 is the pad length. The first subsampling layer is using (x_len - 7) // 2, so we use 7 here | |
| # (8) -23316=5,2,4,3,2,4, attribute 16, this is an array attribute. It is attribute 16 since -23300 - (-23316) = 16 | |
| # the first element of the array is the length of the array, which is 5 in our case. | |
| # 2,4,3,2,4 is the value of --num-encoder-layers | |
| # (9) -23317=5,384,384,384,384,384, attribute 17. 384,384,384,384,384 is the value of --encoder-dims | |
| # (10) -23318=5,192,192,192,192,192, attribute 18, 192,192,192,192,192 is the value of --attention-dims | |
| # (11) -23319=5,1,2,4,8,2, attribute 19, 1,2,4,8,2 is the value of --zipformer-downsampling-factors | |
| # (12) -23320=5,31,31,31,31,31, attribute 20, 31,31,31,31,31 is the value of --cnn-module-kernels |