huangyuxin
4 years ago
commit
2274a07235
@ -0,0 +1,3 @@
|
||||
TED_EnZh
|
||||
data
|
||||
exp
|
||||
@ -0,0 +1,16 @@
|
||||
|
||||
# TED En-Zh
|
||||
|
||||
## Dataset
|
||||
|
||||
| Data Subset | Duration in Frames |
|
||||
| --- | --- |
|
||||
| data/manifest.train | 94.2 ~ 6000 |
|
||||
| data/manifest.dev | 115.1 ~ 3900 |
|
||||
| data/manifest.test | 110 ~ 4274.6 |
|
||||
|
||||
## Transformer
|
||||
| Model | Params | Config | Val loss | Char-BLEU |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| FAT + Transformer+ASR MTL | 50.26M | conf/transformer_mtl_noam.yaml | 62.86 | 19.45 |
|
||||
| FAT + Transformer+ASR MTL with word reward | 50.26M | conf/transformer_mtl_noam.yaml | 62.86 | 20.80 |
|
||||
@ -0,0 +1,112 @@
|
||||
# https://yaml.org/type/float.html
|
||||
data:
|
||||
train_manifest: data/manifest.train.tiny
|
||||
dev_manifest: data/manifest.dev
|
||||
test_manifest: data/manifest.test
|
||||
min_input_len: 5.0 # frame
|
||||
max_input_len: 3000.0 # frame
|
||||
min_output_len: 0.0 # tokens
|
||||
max_output_len: 400.0 # tokens
|
||||
min_output_input_ratio: 0.01
|
||||
max_output_input_ratio: 20.0
|
||||
|
||||
collator:
|
||||
vocab_filepath: data/lang_char/vocab.txt
|
||||
unit_type: 'spm'
|
||||
spm_model_prefix: data/lang_char/bpe_unigram_8000
|
||||
mean_std_filepath: ""
|
||||
# augmentation_config: conf/augmentation.json
|
||||
batch_size: 10
|
||||
raw_wav: True # use raw_wav or kaldi feature
|
||||
spectrum_type: fbank #linear, mfcc, fbank
|
||||
feat_dim: 83
|
||||
delta_delta: False
|
||||
dither: 1.0
|
||||
target_sample_rate: 16000
|
||||
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: None
|
||||
cmvn_file_type: "json"
|
||||
# encoder related
|
||||
encoder: transformer
|
||||
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
|
||||
|
||||
# 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:
|
||||
asr_weight: 0.0
|
||||
ctc_weight: 0.0
|
||||
ctc_dropoutrate: 0.0
|
||||
ctc_grad_norm_type: null
|
||||
lsm_weight: 0.1 # label smoothing option
|
||||
length_normalized_loss: false
|
||||
|
||||
|
||||
training:
|
||||
n_epoch: 20
|
||||
accum_grad: 2
|
||||
global_grad_clip: 5.0
|
||||
optim: adam
|
||||
optim_conf:
|
||||
lr: 0.004
|
||||
weight_decay: 1e-06
|
||||
scheduler: warmuplr # pytorch v1.1.0+ required
|
||||
scheduler_conf:
|
||||
warmup_steps: 25000
|
||||
lr_decay: 1.0
|
||||
log_interval: 5
|
||||
checkpoint:
|
||||
kbest_n: 50
|
||||
latest_n: 5
|
||||
|
||||
|
||||
decoding:
|
||||
batch_size: 5
|
||||
error_rate_type: char-bleu
|
||||
decoding_method: fullsentence # 'fullsentence', 'simultaneous'
|
||||
alpha: 2.5
|
||||
beta: 0.3
|
||||
beam_size: 10
|
||||
word_reward: 0.7
|
||||
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.
|
||||
@ -0,0 +1,112 @@
|
||||
# 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: 5.0 # frame
|
||||
max_input_len: 3000.0 # frame
|
||||
min_output_len: 0.0 # tokens
|
||||
max_output_len: 400.0 # tokens
|
||||
min_output_input_ratio: 0.01
|
||||
max_output_input_ratio: 20.0
|
||||
|
||||
collator:
|
||||
vocab_filepath: data/lang_char/vocab.txt
|
||||
unit_type: 'spm'
|
||||
spm_model_prefix: data/train_sp.en-zh-nlpr.zh-nlpr_bpe8000_tc
|
||||
mean_std_filepath: ""
|
||||
# augmentation_config: conf/augmentation.json
|
||||
batch_size: 10
|
||||
raw_wav: True # use raw_wav or kaldi feature
|
||||
spectrum_type: fbank #linear, mfcc, fbank
|
||||
feat_dim: 83
|
||||
delta_delta: False
|
||||
dither: 1.0
|
||||
target_sample_rate: 16000
|
||||
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: None
|
||||
cmvn_file_type: "json"
|
||||
# encoder related
|
||||
encoder: transformer
|
||||
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
|
||||
|
||||
# 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:
|
||||
asr_weight: 0.5
|
||||
ctc_weight: 0.3
|
||||
ctc_dropoutrate: 0.0
|
||||
ctc_grad_norm_type: null
|
||||
lsm_weight: 0.1 # label smoothing option
|
||||
length_normalized_loss: false
|
||||
|
||||
|
||||
training:
|
||||
n_epoch: 20
|
||||
accum_grad: 2
|
||||
global_grad_clip: 5.0
|
||||
optim: adam
|
||||
optim_conf:
|
||||
lr: 2.5
|
||||
weight_decay: 1e-06
|
||||
scheduler: noam
|
||||
scheduler_conf:
|
||||
warmup_steps: 25000
|
||||
lr_decay: 1.0
|
||||
log_interval: 5
|
||||
checkpoint:
|
||||
kbest_n: 50
|
||||
latest_n: 5
|
||||
|
||||
|
||||
decoding:
|
||||
batch_size: 5
|
||||
error_rate_type: char-bleu
|
||||
decoding_method: fullsentence # 'fullsentence', 'simultaneous'
|
||||
alpha: 2.5
|
||||
beta: 0.3
|
||||
beam_size: 10
|
||||
word_reward: 0.7
|
||||
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.
|
||||
@ -0,0 +1,97 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import argparse
|
||||
|
||||
import paddle
|
||||
import torch
|
||||
|
||||
from paddlespeech.s2t.utils.log import Log
|
||||
|
||||
logger = Log(__name__).getlog()
|
||||
|
||||
|
||||
def torch2paddle(args):
|
||||
paddle.set_device('cpu')
|
||||
paddle_model_dict = {}
|
||||
torch_model = torch.load(args.torch_ckpt, map_location='cpu')
|
||||
cnt = 0
|
||||
for k, v in torch_model['model'].items():
|
||||
# encoder.embed.* --> encoder.embed.*
|
||||
if k.startswith('encoder.embed'):
|
||||
if v.ndim == 2:
|
||||
v = v.transpose(0, 1)
|
||||
paddle_model_dict[k] = v.numpy()
|
||||
cnt += 1
|
||||
logger.info(
|
||||
f"Convert torch weight: {k} to paddlepaddle weight: {k}, shape is {v.shape}"
|
||||
)
|
||||
|
||||
# encoder.after_norm.* --> encoder.after_norm.*
|
||||
# encoder.after_norm.* --> decoder.after_norm.*
|
||||
# encoder.after_norm.* --> st_decoder.after_norm.*
|
||||
if k.startswith('encoder.after_norm'):
|
||||
paddle_model_dict[k] = v.numpy()
|
||||
cnt += 1
|
||||
paddle_model_dict[k.replace('en', 'de')] = v.numpy()
|
||||
logger.info(
|
||||
f"Convert torch weight: {k} to paddlepaddle weight: {k.replace('en','de')}, shape is {v.shape}"
|
||||
)
|
||||
paddle_model_dict['st_' + k.replace('en', 'de')] = v.numpy()
|
||||
logger.info(
|
||||
f"Convert torch weight: {k} to paddlepaddle weight: {'st_'+ k.replace('en','de')}, shape is {v.shape}"
|
||||
)
|
||||
cnt += 2
|
||||
|
||||
# encoder.encoders.* --> encoder.encoders.*
|
||||
# encoder.encoders.* (last six layers) --> decoder.encoders.* (first six layers)
|
||||
# encoder.encoders.* (last six layers) --> st_decoder.encoders.* (first six layers)
|
||||
if k.startswith('encoder.encoders'):
|
||||
if v.ndim == 2:
|
||||
v = v.transpose(0, 1)
|
||||
paddle_model_dict[k] = v.numpy()
|
||||
logger.info(
|
||||
f"Convert torch weight: {k} to paddlepaddle weight: {k}, shape is {v.shape}"
|
||||
)
|
||||
cnt += 1
|
||||
origin_k = k
|
||||
k_split = k.split('.')
|
||||
if int(k_split[2]) >= 6:
|
||||
k = k.replace(k_split[2], str(int(k_split[2]) - 6))
|
||||
paddle_model_dict[k.replace('en', 'de')] = v.numpy()
|
||||
logger.info(
|
||||
f"Convert torch weight: {origin_k} to paddlepaddle weight: {k.replace('en','de')}, shape is {v.shape}"
|
||||
)
|
||||
paddle_model_dict['st_' + k.replace('en', 'de')] = v.numpy()
|
||||
logger.info(
|
||||
f"Convert torch weight: {origin_k} to paddlepaddle weight: {'st_'+ k.replace('en','de')}, shape is {v.shape}"
|
||||
)
|
||||
cnt += 2
|
||||
logger.info(f"Convert {cnt} weights totally from torch to paddlepaddle")
|
||||
paddle.save(paddle_model_dict, args.paddle_ckpt)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument(
|
||||
'--torch_ckpt',
|
||||
type=str,
|
||||
default='/home/snapshot.ep.98',
|
||||
help="Path to torch checkpoint.")
|
||||
parser.add_argument(
|
||||
'--paddle_ckpt',
|
||||
type=str,
|
||||
default='paddle.98.pdparams',
|
||||
help="Path to save paddlepaddle checkpoint.")
|
||||
args = parser.parse_args()
|
||||
torch2paddle(args)
|
||||
@ -0,0 +1,112 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
dict_dir=data/lang_char
|
||||
|
||||
# bpemode (unigram or bpe)
|
||||
nbpe=8000
|
||||
bpemode=unigram
|
||||
bpeprefix="${dict_dir}/bpe_${bpemode}_${nbpe}"
|
||||
data_dir=./TED_EnZh
|
||||
|
||||
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh
|
||||
|
||||
TARGET_DIR=${MAIN_ROOT}/examples/dataset
|
||||
mkdir -p ${TARGET_DIR}
|
||||
mkdir -p data
|
||||
mkdir -p ${dict_dir}
|
||||
|
||||
|
||||
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
|
||||
if [ ! -e ${data_dir} ]; then
|
||||
echo "Error: Dataset is not avaiable. Please download and unzip the dataset"
|
||||
echo "Download Link: https://pan.baidu.com/s/18L-59wgeS96WkObISrytQQ Passwd: bva0"
|
||||
echo "The tree of the directory should be:"
|
||||
echo "."
|
||||
echo "|-- En-Zh"
|
||||
echo "|-- test-segment"
|
||||
echo " |-- tst2010"
|
||||
echo " |-- ..."
|
||||
echo "|-- train-split"
|
||||
echo " |-- train-segment"
|
||||
echo "|-- README.md"
|
||||
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# generate manifests
|
||||
python3 ${TARGET_DIR}/ted_en_zh/ted_en_zh.py \
|
||||
--manifest_prefix="data/manifest" \
|
||||
--src_dir="${data_dir}"
|
||||
|
||||
echo "Complete raw data pre-process."
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# compute mean and stddev for normalizer
|
||||
num_workers=$(nproc)
|
||||
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
|
||||
--manifest_path="data/manifest.train.raw" \
|
||||
--num_samples=-1 \
|
||||
--spectrum_type="fbank" \
|
||||
--feat_dim=80 \
|
||||
--delta_delta=false \
|
||||
--sample_rate=16000 \
|
||||
--stride_ms=10.0 \
|
||||
--window_ms=25.0 \
|
||||
--use_dB_normalization=False \
|
||||
--num_workers=${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 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# build vocabulary
|
||||
python3 ${MAIN_ROOT}/utils/build_vocab.py \
|
||||
--unit_type "spm" \
|
||||
--spm_vocab_size=${nbpe} \
|
||||
--spm_mode ${bpemode} \
|
||||
--spm_model_prefix ${bpeprefix} \
|
||||
--vocab_path="${dict_dir}/vocab.txt" \
|
||||
--text_keys 'text' 'text1' \
|
||||
--manifest_paths="data/manifest.train.raw"
|
||||
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Build vocabulary failed. Terminated."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# format manifest with tokenids, vocab size
|
||||
for set in train dev test; do
|
||||
{
|
||||
python3 ${MAIN_ROOT}/utils/format_triplet_data.py \
|
||||
--feat_type "raw" \
|
||||
--cmvn_path "data/mean_std.json" \
|
||||
--unit_type "spm" \
|
||||
--spm_model_prefix ${bpeprefix} \
|
||||
--vocab_path="${dict_dir}/vocab.txt" \
|
||||
--manifest_path="data/manifest.${set}.raw" \
|
||||
--output_path="data/manifest.${set}"
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Formt mnaifest failed. Terminated."
|
||||
exit 1
|
||||
fi
|
||||
}&
|
||||
done
|
||||
wait
|
||||
fi
|
||||
|
||||
echo "Ted En-Zh Data preparation done."
|
||||
exit 0
|
||||
@ -0,0 +1,19 @@
|
||||
#!/bin/bash
|
||||
|
||||
# download pytorch weight
|
||||
wget https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/snapshot.ep.98 --no-check-certificate
|
||||
|
||||
# convert pytorch weight to paddlepaddle
|
||||
python local/convert_torch_to_paddle.py \
|
||||
--torch_ckpt snapshot.ep.98 \
|
||||
--paddle_ckpt paddle.98.pdparams
|
||||
|
||||
# Or you can download converted weights
|
||||
# wget https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/paddle.98.pdparams --no-check-certificate
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in downloading and coverting!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,31 @@
|
||||
#! /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..."
|
||||
|
||||
config_path=$1
|
||||
ckpt_prefix=$2
|
||||
|
||||
for type in fullsentence; do
|
||||
echo "decoding ${type}"
|
||||
batch_size=32
|
||||
python3 -u ${BIN_DIR}/test.py \
|
||||
--nproc ${ngpu} \
|
||||
--config ${config_path} \
|
||||
--result_file ${ckpt_prefix}.${type}.rsl \
|
||||
--checkpoint_path ${ckpt_prefix} \
|
||||
--opts decoding.decoding_method ${type} \
|
||||
--opts decoding.batch_size ${batch_size}
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in evaluation!"
|
||||
exit 1
|
||||
fi
|
||||
done
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,39 @@
|
||||
#!/bin/bash
|
||||
|
||||
if [ $# != 3 ];then
|
||||
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ckpt_path"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
|
||||
echo "using $ngpu gpus..."
|
||||
|
||||
config_path=$1
|
||||
ckpt_name=$2
|
||||
ckpt_path=$3
|
||||
|
||||
mkdir -p exp
|
||||
|
||||
# seed may break model convergence
|
||||
seed=0
|
||||
if [ ${seed} != 0 ]; then
|
||||
export FLAGS_cudnn_deterministic=True
|
||||
fi
|
||||
|
||||
python3 -u ${BIN_DIR}/train.py \
|
||||
--nproc ${ngpu} \
|
||||
--config ${config_path} \
|
||||
--output exp/${ckpt_name} \
|
||||
--checkpoint_path ${ckpt_path} \
|
||||
--seed ${seed}
|
||||
|
||||
if [ ${seed} != 0 ]; then
|
||||
unset FLAGS_cudnn_deterministic
|
||||
fi
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in training!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,15 @@
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# 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=${LD_LIBRARY_PATH}:/usr/local/lib/
|
||||
|
||||
|
||||
MODEL=u2_st
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin
|
||||
@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0,1,2,3
|
||||
stage=1
|
||||
stop_stage=4
|
||||
conf_path=conf/transformer_mtl_noam.yaml
|
||||
ckpt_path=paddle.98
|
||||
avg_num=5
|
||||
data_path=./TED_EnZh # path to unzipped data
|
||||
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 --data_dir ${data_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# download pretrained
|
||||
bash ./local/download_pretrain.sh || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# train model, all `ckpt` under `exp` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train_finetune.sh ${conf_path} ${ckpt} ${ckpt_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# avg n best model
|
||||
avg.sh best exp/${ckpt}/checkpoints ${avg_num}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
# test ckpt avg_n
|
||||
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
|
||||
fi
|
||||
Loading…
Reference in new issue