File size: 2,940 Bytes
69aa30d 075112a 69aa30d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
#!/usr/bin/env bash
# If you want to prepare the model in ncnn format,
# please clone the repo https://huggingface.co/desh2608/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-small
# and then follow the code below.
# The model hyper-parameters are shown in the following command.
exp_dir=pruned_transducer_stateless7_streaming/exp-20M
python ./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
--bpe-model $bpe_model \
--exp-dir $exp_dir \
--use-averaged-model False \
--epoch 99 \
--avg 1 \
--decode-chunk-len 32 \
--num-encoder-layers "2,2,2,2,2" \
--feedforward-dims "768,768,768,768,768" \
--nhead "8,8,8,8,8" \
--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
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
|