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Merge pull request #739 from PaddlePaddle/k8

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Hui Zhang 3 years ago committed by GitHub
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3
examples/callcenter/s1/.gitignore vendored
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data
exp
*.profile

20
examples/callcenter/s1/README.md
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# MandarinK8
## Conformer
| Model | Params | Config | Augmentation| Test set | Decode method | Loss | CER |
| --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | attention | 2.1794936656951904 | 0.102304 |
| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 2.1794936656951904 | 0.084295 |
| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 2.1794936656951904 | 0.084340 |
| conformer | 45.73 M | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | 2.1794936656951904 | 0.081675 |
## Chunk Conformer
| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | CER |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16, -1 | 2.23287845 | 0.087982 |
| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16, -1 | 2.23287845 | 0.086962 |
| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16, -1 | 2.23287845 | 0.086741 |
| conformer | 45.73 M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16, -1 | 2.23287845 | 0.083495 |

34
examples/callcenter/s1/conf/augmentation.json
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[
{
"type": "speed",
"params": {
"min_speed_rate": 0.9,
"max_speed_rate": 1.1,
"num_rates": 3
},
"prob": 0.0
},
{
"type": "shift",
"params": {
"min_shift_ms": -5,
"max_shift_ms": 5
},
"prob": 1.0
},
{
"type": "specaug",
"params": {
"F": 10,
"T": 50,
"n_freq_masks": 2,
"n_time_masks": 2,
"p": 1.0,
"W": 80,
"adaptive_number_ratio": 0,
"adaptive_size_ratio": 0,
"max_n_time_masks": 20
},
"prob": 1.0
}
]

120
examples/callcenter/s1/conf/chunk_conformer.yaml
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5
max_input_len: 20.0 # second
min_output_len: 0.0
max_output_len: 400.0
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
specgram_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 8000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
causal: true
use_dynamic_chunk: true
cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
use_dynamic_left_chunk: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 240
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-6
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 128
error_rate_type: cer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.

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examples/callcenter/s1/conf/conformer.yaml
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5
max_input_len: 20.0 # second
min_output_len: 0.0
max_output_len: 400.0
min_output_input_ratio: 0.0
max_output_input_ratio: .inf
collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
specgram_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 8000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 100 # 50 will be lowest
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-6
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 128
error_rate_type: cer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

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examples/callcenter/s1/local/align.sh
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#! /usr/bin/env bash
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
device=gpu
if [ ${ngpu} == 0 ];then
device=cpu
fi
config_path=$1
ckpt_prefix=$2
ckpt_name=$(basename ${ckpt_prefxi})
mkdir -p exp
batch_size=1
output_dir=${ckpt_prefix}
mkdir -p ${output_dir}
# align dump in `result_file`
# .tier, .TextGrid dump in `dir of result_file`
python3 -u ${BIN_DIR}/alignment.py \
--device ${device} \
--nproc 1 \
--config ${config_path} \
--result_file ${output_dir}/${type}.align \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in ctc alignment!"
exit 1
fi
exit 0

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examples/callcenter/s1/local/data.sh
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#! /usr/bin/env bash
stage=-1
stop_stage=100
source ${MAIN_ROOT}/utils/parse_options.sh
mkdir -p data
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
for dataset in train dev test; do
mv data/manifest.${dataset} data/manifest.${dataset}.raw
done
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# download data, generate manifests
# build vocabulary
python3 ${MAIN_ROOT}/utils/build_vocab.py \
--unit_type="char" \
--count_threshold=0 \
--vocab_path="data/vocab.txt" \
--manifest_paths "data/manifest.train.raw"
if [ $? -ne 0 ]; then
echo "Build vocabulary failed. Terminated."
exit 1
fi
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# compute mean and stddev for normalizer
num_workers=$(nproc)
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
--manifest_path="data/manifest.train.raw" \
--specgram_type="fbank" \
--feat_dim=80 \
--delta_delta=false \
--stride_ms=10.0 \
--window_ms=25.0 \
--sample_rate=8000 \
--use_dB_normalization=False \
--num_samples=-1 \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
if [ $? -ne 0 ]; then
echo "Compute mean and stddev failed. Terminated."
exit 1
fi
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# format manifest with tokenids, vocab size
for dataset in train dev test; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "char" \
--vocab_path="data/vocab.txt" \
--manifest_path="data/manifest.${dataset}.raw" \
--output_path="data/manifest.${dataset}"
if [ $? -ne 0 ]; then
echo "Formt mnaifest failed. Terminated."
exit 1
fi
} &
done
wait
fi
echo "data preparation done."
exit 0

21
examples/callcenter/s1/local/download_lm_ch.sh
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#!/bin/bash
. ${MAIN_ROOT}/utils/utility.sh
DIR=data/lm
mkdir -p ${DIR}
URL='https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm'
MD5="29e02312deb2e59b3c8686c7966d4fe3"
TARGET=${DIR}/zh_giga.no_cna_cmn.prune01244.klm
echo "Download language model ..."
download $URL $MD5 $TARGET
if [ $? -ne 0 ]; then
echo "Fail to download the language model!"
exit 1
fi
exit 0

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examples/callcenter/s1/local/export.sh
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#! /usr/bin/env bash
if [ $# != 3 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_path_prefix=$2
jit_model_export_path=$3
device=gpu
if [ ${ngpu} == 0 ];then
device=cpu
fi
python3 -u ${BIN_DIR}/export.py \
--device ${device} \
--nproc ${ngpu} \
--config ${config_path} \
--checkpoint_path ${ckpt_path_prefix} \
--export_path ${jit_model_export_path}
if [ $? -ne 0 ]; then
echo "Failed in export!"
exit 1
fi
exit 0

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examples/callcenter/s1/local/test.sh
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#! /usr/bin/env bash
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
device=gpu
if [ ${ngpu} == 0 ];then
device=cpu
fi
config_path=$1
ckpt_prefix=$2
ckpt_name=$(basename ${ckpt_prefxi})
mkdir -p exp
# download language model
#bash local/download_lm_ch.sh
#if [ $? -ne 0 ]; then
# exit 1
#fi
for type in attention ctc_greedy_search; do
echo "decoding ${type}"
batch_size=1
output_dir=${ckpt_prefix}
mkdir -p ${output_dir}
python3 -u ${BIN_DIR}/test.py \
--device ${device} \
--nproc 1 \
--config ${config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
exit 1
fi
done
for type in ctc_prefix_beam_search attention_rescoring; do
echo "decoding ${type}"
batch_size=1
output_dir=${ckpt_prefix}
mkdir -p ${output_dir}
python3 -u ${BIN_DIR}/test.py \
--device ${device} \
--nproc 1 \
--config ${config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
exit 1
fi
done
exit 0

33
examples/callcenter/s1/local/train.sh
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#! /usr/bin/env bash
if [ $# != 2 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_name=$2
device=gpu
if [ ${ngpu} == 0 ];then
device=cpu
fi
echo "using ${device}..."
mkdir -p exp
python3 -u ${BIN_DIR}/train.py \
--device ${device} \
--nproc ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name}
if [ $? -ne 0 ]; then
echo "Failed in training!"
exit 1
fi
exit 0

14
examples/callcenter/s1/path.sh
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export MAIN_ROOT=${PWD}/../../../
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
MODEL=u2
export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin

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examples/callcenter/s1/run.sh
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#!/bin/bash
set -e
source path.sh
stage=0
stop_stage=100
conf_path=conf/conformer.yaml
avg_num=20
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
echo "checkpoint name ${ckpt}"
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
bash ./local/data.sh || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=0,1,2,3 ./local/train.sh ${conf_path} ${ckpt}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# avg n best model
avg.sh exp/${ckpt}/checkpoints ${avg_num}
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=4 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# ctc alignment of test data
CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi

5
examples/librispeech/s1/README.md
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@ -17,9 +17,8 @@
| --- | --- | --- | --- | --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention | 16, -1 | 7.01250648 | 0.069548 | | conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention | 16, -1 | 7.01250648 | 0.069548 |
| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 16, -1 | 7.01250648 | 0.094753 | | conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 16, -1 | 7.01250648 | 0.094753 |
| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 16, -1 | 7.01250648 | | | conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 16, -1 | 7.01250648 | - |
| conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 16, -1 | 7.01250648 | | | conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 16, -1 | 7.01250648 | - |
## Transformer ## Transformer

2
examples/librispeech/s1/conf/chunk_conformer.yaml
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@ -110,7 +110,7 @@ decoding:
cutoff_top_n: 0 cutoff_top_n: 0
num_proc_bsearch: 8 num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode. ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: 16 # decoding chunk size. Defaults to -1. decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk. # <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set. # >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here. # 0: used for training, it's prohibited here.

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