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#!/usr/bin/env bash
set -e
# Please download the torchscript model from
# https://huggingface.co/pfluo/k2fsa-zipformer-bilingual-zh-en-t
if [ ! -d bilingual-small ]; then
mkdir -p bilingual-small
pushd bilingual-small
ln -s ~/open-source/icefall-models/k2fsa-zipformer-bilingual-zh-en-t/exp/pretrained.pt epoch-99.pt
ln -s ~/open-source/icefall-models/k2fsa-zipformer-bilingual-zh-en-t/data .
popd
fi
./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
--lang-dir ./bilingual-small/data/lang_char_bpe \
--exp-dir ./bilingual-small \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
--decode-chunk-len 96 \
--num-encoder-layers "2,2,2,2,2" \
--feedforward-dims "768,768,768,768,768" \
--nhead "4,4,4,4,4" \
--encoder-dims "256,256,256,256,256" \
--attention-dims "192,192,192,192,192" \
--encoder-unmasked-dims "192,192,192,192,192" \
--zipformer-downsampling-factors "1,2,4,8,2" \
--cnn-module-kernels "31,31,31,31,31" \
--decoder-dim 512 \
--joiner-dim 512
cd bilingual-small
pnnx encoder_jit_trace-pnnx.pt
pnnx decoder_jit_trace-pnnx.pt
pnnx joiner_jit_trace-pnnx.pt
# modify encoder_jit_trace-pnnx.ncnn.param to support sherpa-ncnn
# The following is the diff
# $ diff -uN ./encoder_jit_trace-pnnx.ncnn.param-before encoder_jit_trace-pnnx.ncnn.param
# --- ./encoder_jit_trace-pnnx.ncnn.param-before 2023-02-16 10:40:18.000000000 +0800
# +++ encoder_jit_trace-pnnx.ncnn.param 2023-02-16 10:43:21.000000000 +0800
# @@ -1,5 +1,6 @@
# 7767517
# -1423 1762
# +1424 1762
# +SherpaMetaData sherpa_meta_data1 0 0 0=2 1=96 2=4 3=7 -23316=5,2,2,2,2,2 -23317=5,256,256,256,256,256 -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) 1423 is changed to 1424 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=96, attribute 1, 96 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=5,2,2,2,2,2, 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.
# 2,2,2,2,2 is the value of --num-encoder-layers
# (9) -23317=5,256,256,256,256,256, attribute 17. 256,256,256,256,256 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
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