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egs/wsj/s5/local/run_rnnlms.sh
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#!/bin/bash
. ./cmd.sh
. ./path.sh
# This script demonstrates how you can train rnnlms, and how you can use them to
# rescore the n-best lists, or lattices.
# Be careful: appending things like "--mem 16G" to $decode_cmd won't always
# work, it depends what $decode_cmd is.
# Trains Tomas Mikolov's version, which takes roughly 5 days with the following
# parameter setting. We start from the dictionary directory without silence
# probabilities (with suffix "_nosp").
rm data/local/rnnlm.h300.voc40k/.error 2>/dev/null
local/wsj_train_rnnlms.sh --dict-suffix "_nosp" \
--cmd "$decode_cmd --mem 16G" \
--hidden 300 --nwords 40000 --class 400 \
--direct 2000 data/local/rnnlm.h300.voc40k \
|| touch data/local/rnnlm.h300.voc40k/.error &
# Trains Yandex's version, which takes roughly 10 hours with the following
# parameter setting. We start from the dictionary directory without silence
# probabilities (with suffix "_nosp").
num_threads_rnnlm=8
rm data/local/rnnlm-hs.nce20.h400.voc40k/.error 2>/dev/null
local/wsj_train_rnnlms.sh --dict-suffix "_nosp" \
--rnnlm_ver faster-rnnlm --threads $num_threads_rnnlm \
--cmd "$decode_cmd --mem 8G --num-threads $num_threads_rnnlm" \
--bptt 4 --bptt-block 10 --hidden 400 --nwords 40000 --direct 2000 \
--rnnlm-options "-direct-order 4 -nce 20" \
data/local/rnnlm-hs.nce20.h400.voc40k \
|| touch data/local/rnnlm-hs.nce20.h400.voc40k/.error &
wait;
# Rescoring. We demonstrate results on the TDNN models. Make sure you have
# finished running the following scripts:
# local/online/run_nnet2.sh
# local/online/run_nnet2_baseline.sh
# local/online/run_nnet2_discriminative.sh
for lm_suffix in tgpr bd_tgpr; do
graph_dir=exp/tri4b/graph_${lm_suffix}
for year in eval92 dev93; do
decode_dir=exp/nnet2_online/nnet_ms_a_online/decode_${lm_suffix}_${year}
# N-best rescoring with Tomas Mikolov's version.
steps/rnnlmrescore.sh \
--N 1000 --cmd "$decode_cmd --mem 16G" --inv-acwt 10 0.75 \
data/lang_test_${lm_suffix} data/local/rnnlm.h300.voc40k \
data/test_${year} ${decode_dir} \
${decode_dir}_rnnlm.h300.voc40k || exit 1;
# Lattice rescoring with Tomas Mikolov's version.
steps/lmrescore_rnnlm_lat.sh \
--weight 0.75 --cmd "$decode_cmd --mem 16G" --max-ngram-order 5 \
data/lang_test_${lm_suffix} data/local/rnnlm.h300.voc40k \
data/test_${year} ${decode_dir} \
${decode_dir}_rnnlm.h300.voc40k_lat || exit 1;
# N-best rescoring with Yandex's version.
steps/rnnlmrescore.sh --rnnlm_ver faster-rnnlm \
--N 1000 --cmd "$decode_cmd --mem 8G" --inv-acwt 10 0.75 \
data/lang_test_${lm_suffix} data/local/rnnlm-hs.nce20.h400.voc40k \
data/test_${year} ${decode_dir} \
${decode_dir}_rnnlm-hs.nce20.h400.voc40k || exit 1;
done
done
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