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DeepSpeech
未验证 提交 82c1f4c5 编写于 作者: H Hui Zhang 提交者： GitHub
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Merge pull request #1997 from Jackwaterveg/develop_dev 

[ASR] refactor ds2, cli, server with espent datapipeline
上级 0d34c624 61e56518
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Showing with 747 addition and 1607 deletion
examples/aishell/asr0/conf/augmentation.json 已删除 100644 → 0
浏览文件 @ 0d34c624
[
{
"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": {
"W": 0,
"warp_mode": "PIL",
"F": 10,
"n_freq_masks": 2,
"T": 50,
"n_time_masks": 2,
"p": 1.0,
"adaptive_number_ratio": 0,
"adaptive_size_ratio": 0,
"max_n_time_masks": 20,
"replace_with_zero": true
},
"prob": 1.0
}
]
examples/aishell/asr0/conf/deepspeech2.yaml
浏览文件 @ 82c1f4c5
......@@ -15,50 +15,53 @@ max_output_input_ratio: .inf
###########################################
# Dataloader #
###########################################
batch_size: 64 # one gpu
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
delta_delta: False
stride_ms: 10.0
window_ms: 20.0
n_fft: None
max_freq: None
target_sample_rate: 16000
use_dB_normalization: True
target_dB: -20
dither: 1.0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 64
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
############################################
num_conv_layers: 2
num_rnn_layers: 3
num_rnn_layers: 5
rnn_layer_size: 1024
use_gru: True
share_rnn_weights: False
rnn_direction: bidirect # [forward, bidirect]
num_fc_layers: 0
fc_layers_size_list: -1,
use_gru: False
blank_id: 0
ctc_grad_norm_type: instance
###########################################
# Training #
###########################################
n_epoch: 80
n_epoch: 50
accum_grad: 1
lr: 2.0e-3
lr_decay: 0.83
lr: 5.0e-4
lr_decay: 0.93
weight_decay: 1.0e-6
global_grad_clip: 3.0
log_interval: 100
dist_sampler: False
log_interval: 1
checkpoint:
kbest_n: 50
latest_n: 5
examples/aishell/asr0/conf/deepspeech2_online.yaml
浏览文件 @ 82c1f4c5
......@@ -15,28 +15,26 @@ max_output_input_ratio: .inf
###########################################
# Dataloader #
###########################################
batch_size: 64 # one gpu
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear #linear, mfcc, fbank
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
delta_delta: False
stride_ms: 10.0
window_ms: 20.0
n_fft: None
max_freq: None
target_sample_rate: 16000
use_dB_normalization: True
target_dB: -20
dither: 1.0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 0
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 64
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
......@@ -54,12 +52,13 @@ blank_id: 0
###########################################
# Training #
###########################################
n_epoch: 65
n_epoch: 30
accum_grad: 1
lr: 5.0e-4
lr_decay: 0.93
weight_decay: 1.0e-6
global_grad_clip: 3.0
dist_sampler: False
log_interval: 100
checkpoint:
kbest_n: 50
......
examples/aishell/asr0/conf/preprocess.yaml 0 → 100644
浏览文件 @ 82c1f4c5
process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 161
n_shift: 160
win_length: 400
dither: 0.1
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/aishell/asr0/conf/tuning/decode.yaml
浏览文件 @ 82c1f4c5
......@@ -2,9 +2,9 @@ decode_batch_size: 128
error_rate_type: cer
decoding_method: ctc_beam_search
lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
alpha: 1.9
beta: 5.0
beam_size: 300
alpha: 2.2
beta: 4.3
beam_size: 500
cutoff_prob: 0.99
cutoff_top_n: 40
num_proc_bsearch: 10
examples/aishell/asr0/local/data.sh
浏览文件 @ 82c1f4c5
......@@ -33,12 +33,13 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
num_workers=$(nproc)
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
--manifest_path="data/manifest.train.raw" \
--spectrum_type="linear" \
--spectrum_type="fbank" \
--feat_dim=161 \
--delta_delta=false \
--stride_ms=10 \
--window_ms=20 \
--window_ms=25 \
--sample_rate=16000 \
--use_dB_normalization=True \
--use_dB_normalization=False \
--num_samples=2000 \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
......
examples/aishell/asr0/local/export.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path model_type"
if [ $# != 3 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path"
exit -1
fi
......@@ -11,14 +11,12 @@ echo "using $ngpu gpus..."
config_path=$1
ckpt_path_prefix=$2
jit_model_export_path=$3
model_type=$4
python3 -u ${BIN_DIR}/export.py \
--ngpu ${ngpu} \
--config ${config_path} \
--checkpoint_path ${ckpt_path_prefix} \
--export_path ${jit_model_export_path} \
--model_type ${model_type}
--export_path ${jit_model_export_path}
if [ $? -ne 0 ]; then
echo "Failed in export!"
......
examples/aishell/asr0/local/test.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
......@@ -13,7 +13,6 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
ckpt_prefix=$3
model_type=$4
# download language model
bash local/download_lm_ch.sh
......@@ -23,7 +22,7 @@ fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# format the reference test file
python utils/format_rsl.py \
python3 utils/format_rsl.py \
--origin_ref data/manifest.test.raw \
--trans_ref data/manifest.test.text
......@@ -32,8 +31,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--config ${config_path} \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix} \
--model_type ${model_type}
--checkpoint_path ${ckpt_prefix}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
......@@ -41,25 +39,25 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
fi
# format the hyp file
python utils/format_rsl.py \
python3 utils/format_rsl.py \
--origin_hyp ${ckpt_prefix}.rsl \
--trans_hyp ${ckpt_prefix}.rsl.text
python utils/compute-wer.py --char=1 --v=1 \
data/manifest.test.text ${ckpt_prefix}.rsl.text > ${ckpt_prefix}.error
python3 utils/compute-wer.py --char=1 --v=1 \
data/manifest.test.text ${ckpt_prefix}.rsl.text > ${ckpt_prefix}.error
fi
if [ ${stage} -le 101 ] && [ ${stop_stage} -ge 101 ]; then
python utils/format_rsl.py \
python3 utils/format_rsl.py \
--origin_ref data/manifest.test.raw \
--trans_ref_sclite data/manifest.test.text.sclite
python utils/format_rsl.py \
--origin_hyp ${ckpt_prefix}.rsl \
--trans_hyp_sclite ${ckpt_prefix}.rsl.text.sclite
python3 utils/format_rsl.py \
--origin_hyp ${ckpt_prefix}.rsl \
--trans_hyp_sclite ${ckpt_prefix}.rsl.text.sclite
mkdir -p ${ckpt_prefix}_sclite
sclite -i wsj -r data/manifest.test.text.sclite -h ${ckpt_prefix}.rsl.text.sclite -e utf-8 -o all -O ${ckpt_prefix}_sclite -c NOASCII
mkdir -p ${ckpt_prefix}_sclite
sclite -i wsj -r data/manifest.test.text.sclite -h ${ckpt_prefix}.rsl.text.sclite -e utf-8 -o all -O ${ckpt_prefix}_sclite -c NOASCII
fi
exit 0
examples/aishell/asr0/local/test_export.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
......@@ -11,7 +11,6 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
jit_model_export_path=$3
model_type=$4
# download language model
bash local/download_lm_ch.sh > /dev/null 2>&1
......@@ -24,8 +23,7 @@ python3 -u ${BIN_DIR}/test_export.py \
--config ${config_path} \
--decode_cfg ${decode_config_path} \
--result_file ${jit_model_export_path}.rsl \
--export_path ${jit_model_export_path} \
--model_type ${model_type}
--export_path ${jit_model_export_path}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
......
examples/aishell/asr0/local/test_wav.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 5 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type audio_file"
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
exit -1
fi
......@@ -11,8 +11,7 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
ckpt_prefix=$3
model_type=$4
audio_file=$5
audio_file=$4
mkdir -p data
wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
......@@ -37,7 +36,6 @@ python3 -u ${BIN_DIR}/test_wav.py \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix} \
--model_type ${model_type} \
--audio_file ${audio_file}
if [ $? -ne 0 ]; then
......
examples/aishell/asr0/local/train.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 3 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name model_type"
if [ $# != 2 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
exit -1
fi
......@@ -10,7 +10,6 @@ echo "using $ngpu gpus..."
config_path=$1
ckpt_name=$2
model_type=$3
mkdir -p exp
......@@ -25,14 +24,12 @@ python3 -u ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--seed ${seed}
else
python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--seed ${seed}
fi
......
examples/aishell/asr0/run.sh
浏览文件 @ 82c1f4c5
......@@ -7,8 +7,7 @@ stage=0
stop_stage=100
conf_path=conf/deepspeech2.yaml #conf/deepspeech2.yaml or conf/deepspeech2_online.yaml
decode_conf_path=conf/tuning/decode.yaml
avg_num=1
model_type=offline # offline or online
avg_num=10
audio_file=data/demo_01_03.wav
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
......@@ -25,7 +24,7 @@ fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} ${model_type}
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
......@@ -35,21 +34,21 @@ fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type}|| exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}|| exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test export ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit|| exit -1
fi
# Optionally, you can add LM and test it with runtime.
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
fi
examples/librispeech/asr0/conf/augmentation.json 已删除 100644 → 0
浏览文件 @ 0d34c624
[
{
"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": {
"W": 0,
"warp_mode": "PIL",
"F": 10,
"n_freq_masks": 2,
"T": 50,
"n_time_masks": 2,
"p": 1.0,
"adaptive_number_ratio": 0,
"adaptive_size_ratio": 0,
"max_n_time_masks": 20,
"replace_with_zero": true
},
"prob": 1.0
}
]
examples/librispeech/asr0/conf/deepspeech2.yaml
浏览文件 @ 82c1f4c5
......@@ -15,51 +15,51 @@ max_output_input_ratio: .inf
###########################################
# Dataloader #
###########################################
batch_size: 20
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear
feat_dim:
target_sample_rate: 16000
max_freq: None
n_fft: None
vocab_filepath: data/lang_char/vocab.txt
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
stride_ms: 10.0
window_ms: 20.0
delta_delta: False
dither: 1.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 64
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
############################################
num_conv_layers: 2
num_rnn_layers: 3
rnn_layer_size: 2048
num_rnn_layers: 5
rnn_layer_size: 1024
rnn_direction: bidirect
num_fc_layers: 0
fc_layers_size_list: -1
use_gru: False
share_rnn_weights: True
blank_id: 0
###########################################
# Training #
###########################################
n_epoch: 50
n_epoch: 15
accum_grad: 1
lr: 1.0e-3
lr_decay: 0.83
lr: 5.0e-4
lr_decay: 0.93
weight_decay: 1.0e-6
global_grad_clip: 5.0
log_interval: 100
dist_sampler: False
log_interval: 1
checkpoint:
kbest_n: 50
latest_n: 5
examples/librispeech/asr0/conf/deepspeech2_online.yaml
浏览文件 @ 82c1f4c5
......@@ -15,39 +15,36 @@ max_output_input_ratio: .inf
###########################################
# Dataloader #
###########################################
batch_size: 15
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear
feat_dim:
target_sample_rate: 16000
max_freq: None
n_fft: None
vocab_filepath: data/lang_char/vocab.txt
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
stride_ms: 10.0
window_ms: 20.0
delta_delta: False
dither: 1.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 0
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 64
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
############################################
num_conv_layers: 2
num_rnn_layers: 3
rnn_layer_size: 2048
num_rnn_layers: 5
rnn_layer_size: 1024
rnn_direction: forward
num_fc_layers: 2
fc_layers_size_list: 512, 256
num_fc_layers: 0
fc_layers_size_list: -1
use_gru: False
blank_id: 0
......@@ -55,13 +52,13 @@ blank_id: 0
###########################################
# Training #
###########################################
n_epoch: 50
accum_grad: 4
lr: 1.0e-3
lr_decay: 0.83
n_epoch: 65
accum_grad: 1
lr: 5.0e-4
lr_decay: 0.93
weight_decay: 1.0e-6
global_grad_clip: 5.0
log_interval: 100
log_interval: 1
checkpoint:
kbest_n: 50
latest_n: 5
examples/librispeech/asr0/conf/preprocess.yaml 0 → 100644
浏览文件 @ 82c1f4c5
process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 161
n_shift: 160
win_length: 400
dither: 0.1
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/librispeech/asr0/local/data.sh
浏览文件 @ 82c1f4c5
......@@ -49,12 +49,13 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
--manifest_path="data/manifest.train.raw" \
--num_samples=2000 \
--spectrum_type="linear" \
--spectrum_type="fbank" \
--feat_dim=161 \
--delta_delta=false \
--sample_rate=16000 \
--stride_ms=10 \
--window_ms=20 \
--use_dB_normalization=True \
--window_ms=25 \
--use_dB_normalization=False \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
......
examples/librispeech/asr0/local/export.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path model_type"
if [ $# != 3 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path"
exit -1
fi
......@@ -11,14 +11,12 @@ echo "using $ngpu gpus..."
config_path=$1
ckpt_path_prefix=$2
jit_model_export_path=$3
model_type=$4
python3 -u ${BIN_DIR}/export.py \
--ngpu ${ngpu} \
--config ${config_path} \
--checkpoint_path ${ckpt_path_prefix} \
--export_path ${jit_model_export_path} \
--model_type ${model_type}
--export_path ${jit_model_export_path}
if [ $? -ne 0 ]; then
echo "Failed in export!"
......
examples/librispeech/asr0/local/test.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
stage=0
stop_stage=100
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
......@@ -11,7 +13,6 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
ckpt_prefix=$3
model_type=$4
# download language model
bash local/download_lm_en.sh
......@@ -19,17 +20,43 @@ if [ $? -ne 0 ]; then
exit 1
fi
python3 -u ${BIN_DIR}/test.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix} \
--model_type ${model_type}
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# format the reference test file
python3 utils/format_rsl.py \
--origin_ref data/manifest.test-clean.raw \
--trans_ref data/manifest.test-clean.text
python3 -u ${BIN_DIR}/test.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
exit 1
fi
python3 utils/format_rsl.py \
--origin_hyp ${ckpt_prefix}.rsl \
--trans_hyp ${ckpt_prefix}.rsl.text
python3 utils/compute-wer.py --char=1 --v=1 \
data/manifest.test-clean.text ${ckpt_prefix}.rsl.text > ${ckpt_prefix}.error
fi
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
exit 1
if [ ${stage} -le 101 ] && [ ${stop_stage} -ge 101 ]; then
python3 utils/format_rsl.py \
--origin_ref data/manifest.test-clean.raw \
--trans_ref_sclite data/manifest.test.text-clean.sclite
python3 utils/format_rsl.py \
--origin_hyp ${ckpt_prefix}.rsl \
--trans_hyp_sclite ${ckpt_prefix}.rsl.text.sclite
mkdir -p ${ckpt_prefix}_sclite
sclite -i wsj -r data/manifest.test-clean.text.sclite -h ${ckpt_prefix}.rsl.text.sclite -e utf-8 -o all -O ${ckpt_prefix}_sclite -c NOASCII
fi
......
examples/librispeech/asr0/local/test_wav.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 5 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type audio_file"
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
exit -1
fi
......@@ -11,8 +11,7 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
ckpt_prefix=$3
model_type=$4
audio_file=$5
audio_file=$4
mkdir -p data
wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
......@@ -37,7 +36,6 @@ python3 -u ${BIN_DIR}/test_wav.py \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix} \
--model_type ${model_type} \
--audio_file ${audio_file}
if [ $? -ne 0 ]; then
......
examples/librispeech/asr0/local/train.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 3 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name model_type"
if [ $# != 2 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
exit -1
fi
......@@ -10,7 +10,6 @@ echo "using $ngpu gpus..."
config_path=$1
ckpt_name=$2
model_type=$3
mkdir -p exp
......@@ -25,14 +24,12 @@ python3 -u ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--seed ${seed}
else
python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--seed ${seed}
fi
......
examples/librispeech/asr0/run.sh
浏览文件 @ 82c1f4c5
......@@ -2,13 +2,12 @@
set -e
source path.sh
gpus=0,1,2,3,4,5,6,7
gpus=0,1,2,3
stage=0
stop_stage=100
conf_path=conf/deepspeech2.yaml
decode_conf_path=conf/tuning/decode.yaml
avg_num=30
model_type=offline
avg_num=5
audio_file=data/demo_002_en.wav
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
......@@ -24,7 +23,7 @@ fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} ${model_type}
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
......@@ -34,15 +33,20 @@ fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}|| exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test export ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit|| exit -1
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
fi
examples/tiny/asr0/conf/augmentation.json 已删除 100644 → 0
浏览文件 @ 0d34c624
[
{
"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": {
"W": 5,
"warp_mode": "PIL",
"F": 30,
"n_freq_masks": 2,
"T": 40,
"n_time_masks": 2,
"p": 1.0,
"adaptive_number_ratio": 0,
"adaptive_size_ratio": 0,
"max_n_time_masks": 20,
"replace_with_zero": true
},
"prob": 1.0
}
]
examples/tiny/asr0/conf/deepspeech2.yaml
浏览文件 @ 82c1f4c5
......@@ -16,28 +16,26 @@ max_output_input_ratio: 10.0
###########################################
# Dataloader #
###########################################
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear
vocab_filepath: data/lang_char/vocab.txt
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
delta_delta: False
stride_ms: 10.0
window_ms: 20.0
n_fft: None
max_freq: None
target_sample_rate: 16000
use_dB_normalization: True
target_dB: -20
dither: 1.0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 4
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
......@@ -45,8 +43,10 @@ batch_size: 4
num_conv_layers: 2
num_rnn_layers: 3
rnn_layer_size: 2048
rnn_direction: bidirect # [forward, bidirect]
num_fc_layers: 0
fc_layers_size_list: -1,
use_gru: False
share_rnn_weights: True
blank_id: 0
......@@ -59,6 +59,7 @@ lr: 1.0e-5
lr_decay: 0.8
weight_decay: 1.0e-6
global_grad_clip: 5.0
dist_sampler: False
log_interval: 1
checkpoint:
kbest_n: 3
......
examples/tiny/asr0/conf/deepspeech2_online.yaml
浏览文件 @ 82c1f4c5
......@@ -16,29 +16,27 @@ max_output_input_ratio: 10.0
###########################################
# Dataloader #
###########################################
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/lang_char/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
spectrum_type: linear
vocab_filepath: data/lang_char/vocab.txt
spm_model_prefix: ''
unit_type: 'char'
preprocess_config: conf/preprocess.yaml
feat_dim: 161
delta_delta: False
stride_ms: 10.0
window_ms: 20.0
n_fft: None
max_freq: None
target_sample_rate: 16000
use_dB_normalization: True
target_dB: -20
dither: 1.0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 0
window_ms: 25.0
sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
batch_size: 4
maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced
maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced
minibatches: 0 # for debug
batch_count: auto
batch_bins: 0
batch_frames_in: 0
batch_frames_out: 0
batch_frames_inout: 0
num_workers: 8
subsampling_factor: 1
num_encs: 1
############################################
# Network Architecture #
############################################
......@@ -61,6 +59,7 @@ lr: 1.0e-5
lr_decay: 1.0
weight_decay: 1.0e-6
global_grad_clip: 5.0
dist_sampler: False
log_interval: 1
checkpoint:
kbest_n: 3
......
examples/tiny/asr0/conf/preprocess.yaml 0 → 100644
浏览文件 @ 82c1f4c5
process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 161
n_shift: 160
win_length: 400
dither: 0.1
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/tiny/asr0/local/export.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path model_type"
if [ $# != 3 ];then
echo "usage: $0 config_path ckpt_prefix jit_model_path"
exit -1
fi
......@@ -11,14 +11,12 @@ echo "using $ngpu gpus..."
config_path=$1
ckpt_path_prefix=$2
jit_model_export_path=$3
model_type=$4
python3 -u ${BIN_DIR}/export.py \
--ngpu ${ngpu} \
--config ${config_path} \
--checkpoint_path ${ckpt_path_prefix} \
--export_path ${jit_model_export_path} \
--model_type ${model_type}
--export_path ${jit_model_export_path}
if [ $? -ne 0 ]; then
echo "Failed in export!"
......
examples/tiny/asr0/local/test.sh
浏览文件 @ 82c1f4c5
#!/bin/bash
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
......@@ -11,7 +11,6 @@ echo "using $ngpu gpus..."
config_path=$1
decode_config_path=$2
ckpt_prefix=$3
model_type=$4
# download language model
bash local/download_lm_en.sh
......@@ -24,8 +23,7 @@ python3 -u ${BIN_DIR}/test.py \
--config ${config_path} \
--decode_cfg ${decode_config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix} \
--model_type ${model_type}
--checkpoint_path ${ckpt_prefix}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
......
examples/tiny/asr0/local/train.sh
浏览文件 @ 82c1f4c5
......@@ -15,14 +15,13 @@ if [ ${seed} != 0 ]; then
echo "using seed $seed & FLAGS_cudnn_deterministic=True ..."
fi
if [ $# != 3 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name model_type"
if [ $# != 2 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
exit -1
fi
config_path=$1
ckpt_name=$2
model_type=$3
mkdir -p exp
......@@ -31,7 +30,6 @@ python3 -u ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--profiler-options "${profiler_options}" \
--seed ${seed}
else
......@@ -39,7 +37,6 @@ python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${BIN_DIR}/t
--ngpu ${ngpu} \
--config ${config_path} \
--output exp/${ckpt_name} \
--model_type ${model_type} \
--profiler-options "${profiler_options}" \
--seed ${seed}
fi
......
examples/tiny/asr0/run.sh
浏览文件 @ 82c1f4c5
......@@ -8,8 +8,6 @@ stop_stage=100
conf_path=conf/deepspeech2.yaml
decode_conf_path=conf/tuning/decode.yaml
avg_num=1
model_type=offline
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
avg_ckpt=avg_${avg_num}
......@@ -23,7 +21,7 @@ fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} ${model_type}
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
......@@ -33,10 +31,10 @@ fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}|| exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# export ckpt avg_n
CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi
paddlespeech/cli/asr/infer.py
浏览文件 @ 82c1f4c5
......@@ -138,6 +138,7 @@ class ASRExecutor(BaseExecutor):
tag = model_type + '-' + lang + '-' + sample_rate_str
self.task_resource.set_task_model(tag, version=None)
self.res_path = self.task_resource.res_dir
self.cfg_path = os.path.join(
self.res_path, self.task_resource.res_dict['cfg_path'])
self.ckpt_path = os.path.join(
......@@ -158,15 +159,18 @@ class ASRExecutor(BaseExecutor):
self.config.merge_from_file(self.cfg_path)
with UpdateConfig(self.config):
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
self.vocab = self.config.vocab_filepath
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type,
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
if "deepspeech2" in model_type:
self.config.decode.lang_model_path = os.path.join(
MODEL_HOME, 'language_model',
self.config.decode.lang_model_path)
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
self.download_lm(
......@@ -174,12 +178,6 @@ class ASRExecutor(BaseExecutor):
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
elif "conformer" in model_type or "transformer" in model_type:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type,
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
self.config.decode.decoding_method = decode_method
else:
......@@ -222,19 +220,7 @@ class ASRExecutor(BaseExecutor):
logger.info("Preprocess audio_file:" + audio_file)
# Get the object for feature extraction
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
audio, _ = self.collate_fn_test.process_utterance(
audio_file=audio_file, transcript=" ")
audio_len = audio.shape[0]
audio = paddle.to_tensor(audio, dtype='float32')
audio_len = paddle.to_tensor(audio_len)
audio = paddle.unsqueeze(audio, axis=0)
# vocab_list = collate_fn_test.vocab_list
self._inputs["audio"] = audio
self._inputs["audio_len"] = audio_len
logger.info(f"audio feat shape: {audio.shape}")
elif "conformer" in model_type or "transformer" in model_type:
if "deepspeech2" in model_type or "conformer" in model_type or "transformer" in model_type:
logger.info("get the preprocess conf")
preprocess_conf = self.config.preprocess_config
preprocess_args = {"train": False}
......@@ -242,7 +228,6 @@ class ASRExecutor(BaseExecutor):
logger.info("read the audio file")
audio, audio_sample_rate = soundfile.read(
audio_file, dtype="int16", always_2d=True)
if self.change_format:
if audio.shape[1] >= 2:
audio = audio.mean(axis=1, dtype=np.int16)
......@@ -285,7 +270,7 @@ class ASRExecutor(BaseExecutor):
cfg = self.config.decode
audio = self._inputs["audio"]
audio_len = self._inputs["audio_len"]
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
if "deepspeech2" in model_type:
decode_batch_size = audio.shape[0]
self.model.decoder.init_decoder(
decode_batch_size, self.text_feature.vocab_list,
......
paddlespeech/resource/model_alias.py
浏览文件 @ 82c1f4c5
......@@ -23,7 +23,7 @@ model_alias = {
# ---------------------------------
"deepspeech2offline": ["paddlespeech.s2t.models.ds2:DeepSpeech2Model"],
"deepspeech2online":
["paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline"],
["paddlespeech.s2t.models.ds2:DeepSpeech2Model"],
"conformer": ["paddlespeech.s2t.models.u2:U2Model"],
"conformer_online": ["paddlespeech.s2t.models.u2:U2Model"],
"transformer": ["paddlespeech.s2t.models.u2:U2Model"],
......
paddlespeech/resource/pretrained_models.py
浏览文件 @ 82c1f4c5
......@@ -136,9 +136,9 @@ asr_dynamic_pretrained_models = {
"deepspeech2online_wenetspeech-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz',
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.1.model.tar.gz',
'md5':
'e393d4d274af0f6967db24fc146e8074',
'd1be86a3e786042ab64f05161b5fae62',
'cfg_path':
'model.yaml',
'ckpt_path':
......@@ -152,13 +152,13 @@ asr_dynamic_pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'4d26066c6f19f52087425dc722ae5b13',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'exp/deepspeech2/checkpoints/avg_10',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
......@@ -168,9 +168,9 @@ asr_dynamic_pretrained_models = {
"deepspeech2online_aishell-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_1.0.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'df5ddeac8b679a470176649ac4b78726',
'cfg_path':
'model.yaml',
'ckpt_path':
......@@ -188,13 +188,13 @@ asr_dynamic_pretrained_models = {
"deepspeech2offline_librispeech-en-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_offline_librispeech_ckpt_1.0.1.model.tar.gz',
'md5':
'f5666c81ad015c8de03aac2bc92e5762',
'ed9e2b008a65268b3484020281ab048c',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'exp/deepspeech2/checkpoints/avg_5',
'lm_url':
'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
'lm_md5':
......@@ -207,17 +207,17 @@ asr_static_pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'1.0': {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'4d26066c6f19f52087425dc722ae5b13',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'exp/deepspeech2/checkpoints/avg_10',
'model':
'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
'exp/deepspeech2/checkpoints/avg_10.jit.pdmodel',
'params':
'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
'exp/deepspeech2/checkpoints/avg_10.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
......@@ -830,7 +830,7 @@ vector_dynamic_pretrained_models = {
'cfg_path':
'conf/model.yaml', # the yaml config path
'ckpt_path':
'model/model', # the format is ${dir}/{model_name},
'model/model', # the format is ${dir}/{model_name},
# so the first 'model' is dir, the second 'model' is the name
# this means we have a model stored as model/model.pdparams
},
......
paddlespeech/s2t/exps/deepspeech2/bin/export.py
浏览文件 @ 82c1f4c5
......@@ -32,11 +32,9 @@ def main(config, args):
if __name__ == "__main__":
parser = default_argument_parser()
# save jit model to
# save jit model to
parser.add_argument(
"--export_path", type=str, help="path of the jit model to save")
parser.add_argument(
"--model_type", type=str, default='offline', help="offline/online")
parser.add_argument(
'--nxpu',
type=int,
......@@ -44,7 +42,6 @@ if __name__ == "__main__":
choices=[0, 1],
help="if nxpu == 0 and ngpu == 0, use cpu.")
args = parser.parse_args()
print("model_type:{}".format(args.model_type))
print_arguments(args)
# https://yaml.org/type/float.html
......
paddlespeech/s2t/exps/deepspeech2/bin/test.py
浏览文件 @ 82c1f4c5
......@@ -32,9 +32,7 @@ def main(config, args):
if __name__ == "__main__":
parser = default_argument_parser()
parser.add_argument(
"--model_type", type=str, default='offline', help='offline/online')
# save asr result to
# save asr result to
parser.add_argument(
"--result_file", type=str, help="path of save the asr result")
parser.add_argument(
......@@ -45,7 +43,6 @@ if __name__ == "__main__":
help="if nxpu == 0 and ngpu == 0, use cpu.")
args = parser.parse_args()
print_arguments(args, globals())
print("model_type:{}".format(args.model_type))
# https://yaml.org/type/float.html
config = CfgNode(new_allowed=True)
......
paddlespeech/s2t/exps/deepspeech2/bin/test_export.py
浏览文件 @ 82c1f4c5
......@@ -38,8 +38,6 @@ if __name__ == "__main__":
#load jit model from
parser.add_argument(
"--export_path", type=str, help="path of the jit model to save")
parser.add_argument(
"--model_type", type=str, default='offline', help='offline/online')
parser.add_argument(
'--nxpu',
type=int,
......@@ -50,7 +48,6 @@ if __name__ == "__main__":
"--enable-auto-log", action="store_true", help="use auto log")
args = parser.parse_args()
print_arguments(args, globals())
print("model_type:{}".format(args.model_type))
# https://yaml.org/type/float.html
config = CfgNode(new_allowed=True)
......
paddlespeech/s2t/exps/deepspeech2/bin/test_wav.py
浏览文件 @ 82c1f4c5
......@@ -23,7 +23,6 @@ from yacs.config import CfgNode
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
from paddlespeech.s2t.training.cli import default_argument_parser
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.checkpoint import Checkpoint
......@@ -113,12 +112,7 @@ class DeepSpeech2Tester_hub():
config.input_dim = self.collate_fn_test.feature_size
config.output_dim = self.collate_fn_test.vocab_size
if self.args.model_type == 'offline':
model = DeepSpeech2Model.from_config(config)
elif self.args.model_type == 'online':
model = DeepSpeech2ModelOnline.from_config(config)
else:
raise Exception("wrong model type")
model = DeepSpeech2Model.from_config(config)
self.model = model
......@@ -172,8 +166,6 @@ def main(config, args):
if __name__ == "__main__":
parser = default_argument_parser()
parser.add_argument(
"--model_type", type=str, default='offline', help='offline/online')
parser.add_argument("--audio_file", type=str, help='audio file path')
# save asr result to
parser.add_argument(
......@@ -184,7 +176,6 @@ if __name__ == "__main__":
print("Please input the audio file path")
sys.exit(-1)
check(args.audio_file)
print("model_type:{}".format(args.model_type))
# https://yaml.org/type/float.html
config = CfgNode(new_allowed=True)
......
paddlespeech/s2t/exps/deepspeech2/bin/train.py
浏览文件 @ 82c1f4c5
......@@ -31,8 +31,6 @@ def main(config, args):
if __name__ == "__main__":
parser = default_argument_parser()
parser.add_argument(
"--model_type", type=str, default='offline', help='offline/online')
parser.add_argument(
'--nxpu',
type=int,
......@@ -40,7 +38,6 @@ if __name__ == "__main__":
choices=[0, 1],
help="if nxpu == 0 and ngpu == 0, use cpu.")
args = parser.parse_args()
print("model_type:{}".format(args.model_type))
print_arguments(args, globals())
# https://yaml.org/type/float.html
......
paddlespeech/s2t/exps/deepspeech2/model.py
浏览文件 @ 82c1f4c5
......@@ -23,16 +23,12 @@ import paddle
from paddle import distributed as dist
from paddle import inference
from paddle.io import DataLoader
from paddlespeech.s2t.io.dataloader import BatchDataLoader
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.io.dataset import ManifestDataset
from paddlespeech.s2t.io.sampler import SortagradBatchSampler
from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
from paddlespeech.s2t.models.ds2 import DeepSpeech2InferModel
from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog
from paddlespeech.s2t.training.reporter import report
from paddlespeech.s2t.training.timer import Timer
......@@ -136,18 +132,13 @@ class DeepSpeech2Trainer(Trainer):
config = self.config.clone()
with UpdateConfig(config):
if self.train:
config.input_dim = self.train_loader.collate_fn.feature_size
config.output_dim = self.train_loader.collate_fn.vocab_size
config.input_dim = self.train_loader.feat_dim
config.output_dim = self.train_loader.vocab_size
else:
config.input_dim = self.test_loader.collate_fn.feature_size
config.output_dim = self.test_loader.collate_fn.vocab_size
config.input_dim = self.test_loader.feat_dim
config.output_dim = self.test_loader.vocab_size
if self.args.model_type == 'offline':
model = DeepSpeech2Model.from_config(config)
elif self.args.model_type == 'online':
model = DeepSpeech2ModelOnline.from_config(config)
else:
raise Exception("wrong model type")
model = DeepSpeech2Model.from_config(config)
if self.parallel:
model = paddle.DataParallel(model)
......@@ -175,76 +166,81 @@ class DeepSpeech2Trainer(Trainer):
config = self.config.clone()
config.defrost()
if self.train:
# train
config.manifest = config.train_manifest
train_dataset = ManifestDataset.from_config(config)
if self.parallel:
batch_sampler = SortagradDistributedBatchSampler(
train_dataset,
batch_size=config.batch_size,
num_replicas=None,
rank=None,
shuffle=True,
drop_last=True,
sortagrad=config.sortagrad,
shuffle_method=config.shuffle_method)
else:
batch_sampler = SortagradBatchSampler(
train_dataset,
shuffle=True,
batch_size=config.batch_size,
drop_last=True,
sortagrad=config.sortagrad,
shuffle_method=config.shuffle_method)
config.keep_transcription_text = False
collate_fn_train = SpeechCollator.from_config(config)
self.train_loader = DataLoader(
train_dataset,
batch_sampler=batch_sampler,
collate_fn=collate_fn_train,
num_workers=config.num_workers)
# dev
config.manifest = config.dev_manifest
dev_dataset = ManifestDataset.from_config(config)
config.augmentation_config = ""
config.keep_transcription_text = False
collate_fn_dev = SpeechCollator.from_config(config)
self.valid_loader = DataLoader(
dev_dataset,
batch_size=int(config.batch_size),
shuffle=False,
drop_last=False,
collate_fn=collate_fn_dev,
num_workers=config.num_workers)
logger.info("Setup train/valid Dataloader!")
# train/valid dataset, return token ids
self.train_loader = BatchDataLoader(
json_file=config.train_manifest,
train_mode=True,
sortagrad=config.sortagrad,
batch_size=config.batch_size,
maxlen_in=config.maxlen_in,
maxlen_out=config.maxlen_out,
minibatches=config.minibatches,
mini_batch_size=self.args.ngpu,
batch_count=config.batch_count,
batch_bins=config.batch_bins,
batch_frames_in=config.batch_frames_in,
batch_frames_out=config.batch_frames_out,
batch_frames_inout=config.batch_frames_inout,
preprocess_conf=config.preprocess_config,
n_iter_processes=config.num_workers,
subsampling_factor=1,
num_encs=1,
dist_sampler=config.get('dist_sampler', False),
shortest_first=False)
self.valid_loader = BatchDataLoader(
json_file=config.dev_manifest,
train_mode=False,
sortagrad=False,
batch_size=config.batch_size,
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=self.args.ngpu,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=config.preprocess_config,
n_iter_processes=config.num_workers,
subsampling_factor=1,
num_encs=1,
dist_sampler=config.get('dist_sampler', False),
shortest_first=False)
logger.info("Setup train/valid Dataloader!")
else:
# test
config.manifest = config.test_manifest
test_dataset = ManifestDataset.from_config(config)
config.augmentation_config = ""
config.keep_transcription_text = True
collate_fn_test = SpeechCollator.from_config(config)
decode_batch_size = config.get('decode', dict()).get(
'decode_batch_size', 1)
self.test_loader = DataLoader(
test_dataset,
# test dataset, return raw text
self.test_loader = BatchDataLoader(
json_file=config.test_manifest,
train_mode=False,
sortagrad=False,
batch_size=decode_batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn_test,
num_workers=config.num_workers)
logger.info("Setup test Dataloader!")
maxlen_in=float('inf'),
maxlen_out=float('inf'),
minibatches=0,
mini_batch_size=1,
batch_count='auto',
batch_bins=0,
batch_frames_in=0,
batch_frames_out=0,
batch_frames_inout=0,
preprocess_conf=config.preprocess_config,
n_iter_processes=1,
subsampling_factor=1,
num_encs=1)
logger.info("Setup test/align Dataloader!")
class DeepSpeech2Tester(DeepSpeech2Trainer):
def __init__(self, config, args):
super().__init__(config, args)
self._text_featurizer = TextFeaturizer(
unit_type=config.unit_type, vocab=None)
unit_type=config.unit_type,
vocab=config.vocab_filepath)
self.vocab_list = self._text_featurizer.vocab_list
def ordid2token(self, texts, texts_len):
""" ord() id to chr() chr """
......@@ -252,7 +248,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
for text, n in zip(texts, texts_len):
n = n.numpy().item()
ids = text[:n]
trans.append(''.join([chr(i) for i in ids]))
trans.append(self._text_featurizer.defeaturize(ids.numpy().tolist()))
return trans
def compute_metrics(self,
......@@ -307,8 +303,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
# Initialized the decoder in model
decode_cfg = self.config.decode
vocab_list = self.test_loader.collate_fn.vocab_list
decode_batch_size = self.test_loader.batch_size
vocab_list = self.vocab_list
decode_batch_size = decode_cfg.decode_batch_size
self.model.decoder.init_decoder(
decode_batch_size, vocab_list, decode_cfg.decoding_method,
decode_cfg.lang_model_path, decode_cfg.alpha, decode_cfg.beta,
......@@ -338,17 +334,9 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
@paddle.no_grad()
def export(self):
if self.args.model_type == 'offline':
infer_model = DeepSpeech2InferModel.from_pretrained(
self.test_loader, self.config, self.args.checkpoint_path)
elif self.args.model_type == 'online':
infer_model = DeepSpeech2InferModelOnline.from_pretrained(
self.test_loader, self.config, self.args.checkpoint_path)
else:
raise Exception("wrong model type")
infer_model = DeepSpeech2InferModel.from_pretrained(
self.test_loader, self.config, self.args.checkpoint_path)
infer_model.eval()
feat_dim = self.test_loader.collate_fn.feature_size
static_model = infer_model.export()
logger.info(f"Export code: {static_model.forward.code}")
paddle.jit.save(static_model, self.args.export_path)
......@@ -376,10 +364,10 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
# Initialized the decoder in model
decode_cfg = self.config.decode
vocab_list = self.test_loader.collate_fn.vocab_list
if self.args.model_type == "online":
vocab_list = self.vocab_list
if self.config.rnn_direction == "forward":
decode_batch_size = 1
elif self.args.model_type == "offline":
elif self.config.rnn_direction == "bidirect":
decode_batch_size = self.test_loader.batch_size
else:
raise Exception("wrong model type")
......@@ -412,11 +400,11 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
self.model.decoder.del_decoder()
def compute_result_transcripts(self, audio, audio_len):
if self.args.model_type == "online":
if self.config.rnn_direction == "forward":
output_probs, output_lens, trans_batch = self.static_forward_online(
audio, audio_len, decoder_chunk_size=1)
result_transcripts = [trans[-1] for trans in trans_batch]
elif self.args.model_type == "offline":
elif self.config.rnn_direction == "bidirect":
output_probs, output_lens = self.static_forward_offline(audio,
audio_len)
batch_size = output_probs.shape[0]
......
paddlespeech/s2t/models/ds2/conv.py
浏览文件 @ 82c1f4c5
......@@ -11,161 +11,23 @@
# 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.
from paddle import nn
from paddle.nn import functional as F
import paddle
from paddlespeech.s2t.modules.activation import brelu
from paddlespeech.s2t.modules.mask import make_non_pad_mask
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.modules.subsampling import Conv2dSubsampling4
logger = Log(__name__).getlog()
__all__ = ['ConvStack', "conv_output_size"]
class Conv2dSubsampling4Pure(Conv2dSubsampling4):
def __init__(self, idim: int, odim: int, dropout_rate: float):
super().__init__(idim, odim, dropout_rate, None)
self.output_dim = ((idim - 1) // 2 - 1) // 2 * odim
self.receptive_field_length = 2 * (
3 - 1) + 3 # stride_1 * (kernel_size_2 - 1) + kerel_size_1
def conv_output_size(I, F, P, S):
# https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks#hyperparameters
# Output size after Conv:
# By noting I the length of the input volume size,
# F the length of the filter,
# P the amount of zero padding,
# S the stride,
# then the output size O of the feature map along that dimension is given by:
# O = (I - F + Pstart + Pend) // S + 1
# When Pstart == Pend == P, we can replace Pstart + Pend by 2P.
# When Pstart == Pend == 0
# O = (I - F - S) // S
# https://iq.opengenus.org/output-size-of-convolution/
# Output height = (Input height + padding height top + padding height bottom - kernel height) / (stride height) + 1
# Output width = (Output width + padding width right + padding width left - kernel width) / (stride width) + 1
return (I - F + 2 * P - S) // S
# receptive field calculator
# https://fomoro.com/research/article/receptive-field-calculator
# https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks#hyperparameters
# https://distill.pub/2019/computing-receptive-fields/
# Rl-1 = Sl * Rl + (Kl - Sl)
class ConvBn(nn.Layer):
"""Convolution layer with batch normalization.
:param kernel_size: The x dimension of a filter kernel. Or input a tuple for
two image dimension.
:type kernel_size: int|tuple|list
:param num_channels_in: Number of input channels.
:type num_channels_in: int
:param num_channels_out: Number of output channels.
:type num_channels_out: int
:param stride: The x dimension of the stride. Or input a tuple for two
image dimension.
:type stride: int|tuple|list
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension.
:type padding: int|tuple|list
:param act: Activation type, relu|brelu
:type act: string
:return: Batch norm layer after convolution layer.
:rtype: Variable
"""
def __init__(self, num_channels_in, num_channels_out, kernel_size, stride,
padding, act):
super().__init__()
assert len(kernel_size) == 2
assert len(stride) == 2
assert len(padding) == 2
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.conv = nn.Conv2D(
num_channels_in,
num_channels_out,
kernel_size=kernel_size,
stride=stride,
padding=padding,
weight_attr=None,
bias_attr=False,
data_format='NCHW')
self.bn = nn.BatchNorm2D(
num_channels_out,
weight_attr=None,
bias_attr=None,
data_format='NCHW')
self.act = F.relu if act == 'relu' else brelu
def forward(self, x, x_len):
"""
x(Tensor): audio, shape [B, C, D, T]
"""
def forward(self, x: paddle.Tensor,
x_len: paddle.Tensor) -> [paddle.Tensor, paddle.Tensor]:
x = x.unsqueeze(1) # (b, c=1, t, f)
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x_len = (x_len - self.kernel_size[1] + 2 * self.padding[1]
) // self.stride[1] + 1
# reset padding part to 0
masks = make_non_pad_mask(x_len) #[B, T]
masks = masks.unsqueeze(1).unsqueeze(1) # [B, 1, 1, T]
# TODO(Hui Zhang): not support bool multiply
# masks = masks.type_as(x)
masks = masks.astype(x.dtype)
x = x.multiply(masks)
return x, x_len
class ConvStack(nn.Layer):
"""Convolution group with stacked convolution layers.
:param feat_size: audio feature dim.
:type feat_size: int
:param num_stacks: Number of stacked convolution layers.
:type num_stacks: int
"""
def __init__(self, feat_size, num_stacks):
super().__init__()
self.feat_size = feat_size # D
self.num_stacks = num_stacks
self.conv_in = ConvBn(
num_channels_in=1,
num_channels_out=32,
kernel_size=(41, 11), #[D, T]
stride=(2, 3),
padding=(20, 5),
act='brelu')
out_channel = 32
convs = [
ConvBn(
num_channels_in=32,
num_channels_out=out_channel,
kernel_size=(21, 11),
stride=(2, 1),
padding=(10, 5),
act='brelu') for i in range(num_stacks - 1)
]
self.conv_stack = nn.LayerList(convs)
# conv output feat_dim
output_height = (feat_size - 1) // 2 + 1
for i in range(self.num_stacks - 1):
output_height = (output_height - 1) // 2 + 1
self.output_height = out_channel * output_height
def forward(self, x, x_len):
"""
x: shape [B, C, D, T]
x_len : shape [B]
"""
x, x_len = self.conv_in(x, x_len)
for i, conv in enumerate(self.conv_stack):
x, x_len = conv(x, x_len)
#b, c, t, f = paddle.shape(x) #not work under jit
x = x.transpose([0, 2, 1, 3]).reshape([0, 0, -1])
x_len = ((x_len - 1) // 2 - 1) // 2
return x, x_len
paddlespeech/s2t/models/ds2/deepspeech2.py
浏览文件 @ 82c1f4c5
......@@ -13,15 +13,14 @@
# limitations under the License.
"""Deepspeech2 ASR Model"""
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.s2t.models.ds2.conv import ConvStack
from paddlespeech.s2t.models.ds2.rnn import RNNStack
from paddlespeech.s2t.models.ds2.conv import Conv2dSubsampling4Pure
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils.checkpoint import Checkpoint
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']
......@@ -32,72 +31,197 @@ class CRNNEncoder(nn.Layer):
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=3,
num_rnn_layers=4,
rnn_size=1024,
use_gru=False,
share_rnn_weights=True):
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False):
super().__init__()
self.rnn_size = rnn_size
self.feat_size = feat_size # 161 for linear
self.dict_size = dict_size
self.conv = ConvStack(feat_size, num_conv_layers)
i_size = self.conv.output_height # H after conv stack
self.rnn = RNNStack(
i_size=i_size,
h_size=rnn_size,
num_stacks=num_rnn_layers,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
self.num_rnn_layers = num_rnn_layers
self.num_fc_layers = num_fc_layers
self.rnn_direction = rnn_direction
self.fc_layers_size_list = fc_layers_size_list
self.use_gru = use_gru
self.conv = Conv2dSubsampling4Pure(feat_size, 32, dropout_rate=0.0)
self.output_dim = self.conv.output_dim
i_size = self.conv.output_dim
self.rnn = nn.LayerList()
self.layernorm_list = nn.LayerList()
self.fc_layers_list = nn.LayerList()
if rnn_direction == 'bidirect' or rnn_direction == 'bidirectional':
layernorm_size = 2 * rnn_size
elif rnn_direction == 'forward':
layernorm_size = rnn_size
else:
raise Exception("Wrong rnn direction")
for i in range(0, num_rnn_layers):
if i == 0:
rnn_input_size = i_size
else:
rnn_input_size = layernorm_size
if use_gru is True:
self.rnn.append(
nn.GRU(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
else:
self.rnn.append(
nn.LSTM(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
self.layernorm_list.append(nn.LayerNorm(layernorm_size))
self.output_dim = layernorm_size
fc_input_size = layernorm_size
for i in range(self.num_fc_layers):
self.fc_layers_list.append(
nn.Linear(fc_input_size, fc_layers_size_list[i]))
fc_input_size = fc_layers_size_list[i]
self.output_dim = fc_layers_size_list[i]
@property
def output_size(self):
return self.rnn_size * 2
return self.output_dim
def forward(self, audio, audio_len):
def forward(self, x, x_lens, init_state_h_box=None, init_state_c_box=None):
"""Compute Encoder outputs
Args:
audio (Tensor): [B, Tmax, D]
text (Tensor): [B, Umax]
audio_len (Tensor): [B]
text_len (Tensor): [B]
Returns:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
Return:
x (Tensor): encoder outputs, [B, T, D]
x_lens (Tensor): encoder length, [B]
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
# [B, T, D] -> [B, D, T]
audio = audio.transpose([0, 2, 1])
# [B, D, T] -> [B, C=1, D, T]
x = audio.unsqueeze(1)
x_lens = audio_len
if init_state_h_box is not None:
init_state_list = None
if self.use_gru is True:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_list = init_state_h_list
else:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_c_list = paddle.split(
init_state_c_box, self.num_rnn_layers, axis=0)
init_state_list = [(init_state_h_list[i], init_state_c_list[i])
for i in range(self.num_rnn_layers)]
else:
init_state_list = [None] * self.num_rnn_layers
# convolution group
x, x_lens = self.conv(x, x_lens)
final_chunk_state_list = []
for i in range(0, self.num_rnn_layers):
x, final_state = self.rnn[i](x, init_state_list[i],
x_lens) #[B, T, D]
final_chunk_state_list.append(final_state)
x = self.layernorm_list[i](x)
for i in range(self.num_fc_layers):
x = self.fc_layers_list[i](x)
x = F.relu(x)
if self.use_gru is True:
final_chunk_state_h_box = paddle.concat(
final_chunk_state_list, axis=0)
final_chunk_state_c_box = init_state_c_box
else:
final_chunk_state_h_list = [
final_chunk_state_list[i][0] for i in range(self.num_rnn_layers)
]
final_chunk_state_c_list = [
final_chunk_state_list[i][1] for i in range(self.num_rnn_layers)
]
final_chunk_state_h_box = paddle.concat(
final_chunk_state_h_list, axis=0)
final_chunk_state_c_box = paddle.concat(
final_chunk_state_c_list, axis=0)
return x, x_lens, final_chunk_state_h_box, final_chunk_state_c_box
def forward_chunk_by_chunk(self, x, x_lens, decoder_chunk_size=8):
"""Compute Encoder outputs
# convert data from convolution feature map to sequence of vectors
#B, C, D, T = paddle.shape(x) # not work under jit
x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
#x = x.reshape([B, T, C * D]) #[B, T, C*D] # not work under jit
x = x.reshape([0, 0, -1]) #[B, T, C*D]
# remove padding part
x, x_lens = self.rnn(x, x_lens) #[B, T, D]
return x, x_lens
Args:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
decoder_chunk_size: The chunk size of decoder
Returns:
eouts_list (List of Tensor): The list of encoder outputs in chunk_size: [B, chunk_size, D] * num_chunks
eouts_lens_list (List of Tensor): The list of encoder length in chunk_size: [B] * num_chunks
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
subsampling_rate = self.conv.subsampling_rate
receptive_field_length = self.conv.receptive_field_length
chunk_size = (decoder_chunk_size - 1
) * subsampling_rate + receptive_field_length
chunk_stride = subsampling_rate * decoder_chunk_size
max_len = x.shape[1]
assert (chunk_size <= max_len)
eouts_chunk_list = []
eouts_chunk_lens_list = []
if (max_len - chunk_size) % chunk_stride != 0:
padding_len = chunk_stride - (max_len - chunk_size) % chunk_stride
else:
padding_len = 0
padding = paddle.zeros((x.shape[0], padding_len, x.shape[2]))
padded_x = paddle.concat([x, padding], axis=1)
num_chunk = (max_len + padding_len - chunk_size) / chunk_stride + 1
num_chunk = int(num_chunk)
chunk_state_h_box = None
chunk_state_c_box = None
final_state_h_box = None
final_state_c_box = None
for i in range(0, num_chunk):
start = i * chunk_stride
end = start + chunk_size
x_chunk = padded_x[:, start:end, :]
x_len_left = paddle.where(x_lens - i * chunk_stride < 0,
paddle.zeros_like(x_lens),
x_lens - i * chunk_stride)
x_chunk_len_tmp = paddle.ones_like(x_lens) * chunk_size
x_chunk_lens = paddle.where(x_len_left < x_chunk_len_tmp,
x_len_left, x_chunk_len_tmp)
eouts_chunk, eouts_chunk_lens, chunk_state_h_box, chunk_state_c_box = self.forward(
x_chunk, x_chunk_lens, chunk_state_h_box, chunk_state_c_box)
eouts_chunk_list.append(eouts_chunk)
eouts_chunk_lens_list.append(eouts_chunk_lens)
final_state_h_box = chunk_state_h_box
final_state_c_box = chunk_state_c_box
return eouts_chunk_list, eouts_chunk_lens_list, final_state_h_box, final_state_c_box
class DeepSpeech2Model(nn.Layer):
"""The DeepSpeech2 network structure.
:param audio_data: Audio spectrogram data layer.
:type audio_data: Variable
:param text_data: Transcription text data layer.
:type text_data: Variable
:param audio: Audio spectrogram data layer.
:type audio: Variable
:param text: Transcription text data layer.
:type text: Variable
:param audio_len: Valid sequence length data layer.
:type audio_len: Variable
:param masks: Masks data layer to reset padding.
:type masks: Variable
:param feat_size: feature size for audio.
:type feat_size: int
:param dict_size: Dictionary size for tokenized transcription.
:type dict_size: int
:param num_conv_layers: Number of stacking convolution layers.
......@@ -106,37 +230,41 @@ class DeepSpeech2Model(nn.Layer):
:type num_rnn_layers: int
:param rnn_size: RNN layer size (dimension of RNN cells).
:type rnn_size: int
:param num_fc_layers: Number of stacking FC layers.
:type num_fc_layers: int
:param fc_layers_size_list: The list of FC layer sizes.
:type fc_layers_size_list: [int,]
:param use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
:param share_rnn_weights: Whether to share input-hidden weights between
forward and backward direction RNNs.
It is only available when use_gru=False.
:type share_weights: bool
:return: A tuple of an output unnormalized log probability layer (
before softmax) and a ctc cost layer.
:rtype: tuple of LayerOutput
"""
def __init__(self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=1024,
use_gru=False,
share_rnn_weights=True,
blank_id=0,
ctc_grad_norm_type=None):
def __init__(
self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=4,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False,
blank_id=0,
ctc_grad_norm_type=None, ):
super().__init__()
self.encoder = CRNNEncoder(
feat_size=feat_size,
dict_size=dict_size,
num_conv_layers=num_conv_layers,
num_rnn_layers=num_rnn_layers,
rnn_direction=rnn_direction,
num_fc_layers=num_fc_layers,
fc_layers_size_list=fc_layers_size_list,
rnn_size=rnn_size,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
assert (self.encoder.output_size == rnn_size * 2)
use_gru=use_gru)
self.decoder = CTCDecoder(
odim=dict_size, # <blank> is in vocab
......@@ -151,7 +279,7 @@ class DeepSpeech2Model(nn.Layer):
"""Compute Model loss
Args:
audio (Tensors): [B, T, D]
audio (Tensor): [B, T, D]
audio_len (Tensor): [B]
text (Tensor): [B, U]
text_len (Tensor): [B]
......@@ -159,22 +287,22 @@ class DeepSpeech2Model(nn.Layer):
Returns:
loss (Tensor): [1]
"""
eouts, eouts_len = self.encoder(audio, audio_len)
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
loss = self.decoder(eouts, eouts_len, text, text_len)
return loss
@paddle.no_grad()
def decode(self, audio, audio_len):
# decoders only accept string encoded in utf-8
# Make sure the decoder has been initialized
eouts, eouts_len = self.encoder(audio, audio_len)
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
probs = self.decoder.softmax(eouts)
batch_size = probs.shape[0]
self.decoder.reset_decoder(batch_size=batch_size)
self.decoder.next(probs, eouts_len)
trans_best, trans_beam = self.decoder.decode()
return trans_best
@classmethod
......@@ -196,13 +324,15 @@ class DeepSpeech2Model(nn.Layer):
The model built from pretrained result.
"""
model = cls(
feat_size=dataloader.collate_fn.feature_size,
dict_size=dataloader.collate_fn.vocab_size,
feat_size=dataloader.feat_dim,
dict_size=dataloader.vocab_size,
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
share_rnn_weights=config.share_rnn_weights,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
infos = Checkpoint().load_parameters(
......@@ -229,8 +359,10 @@ class DeepSpeech2Model(nn.Layer):
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
share_rnn_weights=config.share_rnn_weights,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
return model
......@@ -240,28 +372,46 @@ class DeepSpeech2InferModel(DeepSpeech2Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, audio, audio_len):
"""export model function
Args:
audio (Tensor): [B, T, D]
audio_len (Tensor): [B]
Returns:
probs: probs after softmax
"""
eouts, eouts_len = self.encoder(audio, audio_len)
probs = self.decoder.softmax(eouts)
return probs, eouts_len
def forward(self, audio_chunk, audio_chunk_lens, chunk_state_h_box=None,
chunk_state_c_box=None):
if self.encoder.rnn_direction == "forward":
eouts_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box = self.encoder(
audio_chunk, audio_chunk_lens, chunk_state_h_box, chunk_state_c_box)
probs_chunk = self.decoder.softmax(eouts_chunk)
return probs_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box
elif self.encoder.rnn_direction == "bidirect":
eouts, eouts_len, _, _ = self.encoder(audio_chunk, audio_chunk_lens)
probs = self.decoder.softmax(eouts)
return probs, eouts_len
else:
raise Exception("wrong model type")
def export(self):
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, self.encoder.feat_size],
dtype='float32'), # audio, [B,T,D]
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
])
if self.encoder.rnn_direction == "forward":
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None,
self.encoder.feat_size], #[B, chunk_size, feat_dim]
dtype='float32'),
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32'),
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32')
])
elif self.encoder.rnn_direction == "bidirect":
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, self.encoder.feat_size],
dtype='float32'), # audio, [B,T,D]
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
])
else:
raise Exception("wrong model type")
return static_model
paddlespeech/s2t/models/ds2/rnn.py 已删除 100644 → 0
浏览文件 @ 0d34c624
# 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 math
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.nn import initializer as I
from paddlespeech.s2t.modules.activation import brelu
from paddlespeech.s2t.modules.mask import make_non_pad_mask
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['RNNStack']
class RNNCell(nn.RNNCellBase):
r"""
Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
computes the outputs and updates states.
The formula used is as follows:
.. math::
h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
y_{t} & = h_{t}
where :math:`act` is for :attr:`activation`.
"""
def __init__(self,
hidden_size: int,
activation="tanh",
weight_ih_attr=None,
weight_hh_attr=None,
bias_ih_attr=None,
bias_hh_attr=None,
name=None):
super().__init__()
std = 1.0 / math.sqrt(hidden_size)
self.weight_hh = self.create_parameter(
(hidden_size, hidden_size),
weight_hh_attr,
default_initializer=I.Uniform(-std, std))
self.bias_ih = None
self.bias_hh = self.create_parameter(
(hidden_size, ),
bias_hh_attr,
is_bias=True,
default_initializer=I.Uniform(-std, std))
self.hidden_size = hidden_size
if activation not in ["tanh", "relu", "brelu"]:
raise ValueError(
"activation for SimpleRNNCell should be tanh or relu, "
"but get {}".format(activation))
self.activation = activation
self._activation_fn = paddle.tanh \
if activation == "tanh" \
else F.relu
if activation == 'brelu':
self._activation_fn = brelu
def forward(self, inputs, states=None):
if states is None:
states = self.get_initial_states(inputs, self.state_shape)
pre_h = states
i2h = inputs
if self.bias_ih is not None:
i2h += self.bias_ih
h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
if self.bias_hh is not None:
h2h += self.bias_hh
h = self._activation_fn(i2h + h2h)
return h, h
@property
def state_shape(self):
return (self.hidden_size, )
class GRUCell(nn.RNNCellBase):
r"""
Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
it computes the outputs and updates states.
The formula for GRU used is as follows:
.. math::
r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
\widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
y_{t} & = h_{t}
where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
multiplication operator.
"""
def __init__(self,
input_size: int,
hidden_size: int,
weight_ih_attr=None,
weight_hh_attr=None,
bias_ih_attr=None,
bias_hh_attr=None,
name=None):
super().__init__()
std = 1.0 / math.sqrt(hidden_size)
self.weight_hh = self.create_parameter(
(3 * hidden_size, hidden_size),
weight_hh_attr,
default_initializer=I.Uniform(-std, std))
self.bias_ih = None
self.bias_hh = self.create_parameter(
(3 * hidden_size, ),
bias_hh_attr,
is_bias=True,
default_initializer=I.Uniform(-std, std))
self.hidden_size = hidden_size
self.input_size = input_size
self._gate_activation = F.sigmoid
self._activation = paddle.tanh
def forward(self, inputs, states=None):
if states is None:
states = self.get_initial_states(inputs, self.state_shape)
pre_hidden = states
x_gates = inputs
if self.bias_ih is not None:
x_gates = x_gates + self.bias_ih
h_gates = paddle.matmul(pre_hidden, self.weight_hh, transpose_y=True)
if self.bias_hh is not None:
h_gates = h_gates + self.bias_hh
x_r, x_z, x_c = paddle.split(x_gates, num_or_sections=3, axis=1)
h_r, h_z, h_c = paddle.split(h_gates, num_or_sections=3, axis=1)
r = self._gate_activation(x_r + h_r)
z = self._gate_activation(x_z + h_z)
c = self._activation(x_c + r * h_c) # apply reset gate after mm
h = (pre_hidden - c) * z + c
# https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/fluid/layers/dynamic_gru_cn.html#dynamic-gru
return h, h
@property
def state_shape(self):
r"""
The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
size would be automatically inserted into shape). The shape corresponds
to the shape of :math:`h_{t-1}`.
"""
return (self.hidden_size, )
class BiRNNWithBN(nn.Layer):
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
:param size: Dimension of RNN cells.
:type size: int
:param share_weights: Whether to share input-hidden weights between
forward and backward directional RNNs.
:type share_weights: bool
:return: Bidirectional simple rnn layer.
:rtype: Variable
"""
def __init__(self, i_size: int, h_size: int, share_weights: bool):
super().__init__()
self.share_weights = share_weights
if self.share_weights:
#input-hidden weights shared between bi-directional rnn.
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
# batch norm is only performed on input-state projection
self.fw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.bw_fc = self.fw_fc
self.bw_bn = self.fw_bn
else:
self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.bw_fc = nn.Linear(i_size, h_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(
h_size, bias_attr=None, data_format='NLC')
self.fw_cell = RNNCell(hidden_size=h_size, activation='brelu')
self.bw_cell = RNNCell(hidden_size=h_size, activation='brelu')
self.fw_rnn = nn.RNN(
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
self.bw_rnn = nn.RNN(
self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
# x, shape [B, T, D]
fw_x = self.fw_bn(self.fw_fc(x))
bw_x = self.bw_bn(self.bw_fc(x))
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
x = paddle.concat([fw_x, bw_x], axis=-1)
return x, x_len
class BiGRUWithBN(nn.Layer):
"""Bidirectonal gru layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
:param name: Name of the layer.
:type name: string
:param input: Input layer.
:type input: Variable
:param size: Dimension of GRU cells.
:type size: int
:param act: Activation type.
:type act: string
:return: Bidirectional GRU layer.
:rtype: Variable
"""
def __init__(self, i_size: int, h_size: int):
super().__init__()
hidden_size = h_size * 3
self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(
hidden_size, bias_attr=None, data_format='NLC')
self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(
hidden_size, bias_attr=None, data_format='NLC')
self.fw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
self.bw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
self.fw_rnn = nn.RNN(
self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
self.bw_rnn = nn.RNN(
self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
def forward(self, x, x_len):
# x, shape [B, T, D]
fw_x = self.fw_bn(self.fw_fc(x))
bw_x = self.bw_bn(self.bw_fc(x))
fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
x = paddle.concat([fw_x, bw_x], axis=-1)
return x, x_len
class RNNStack(nn.Layer):
"""RNN group with stacked bidirectional simple RNN or GRU layers.
:param input: Input layer.
:type input: Variable
:param size: Dimension of RNN cells in each layer.
:type size: int
:param num_stacks: Number of stacked rnn layers.
:type num_stacks: int
:param use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
:param share_rnn_weights: Whether to share input-hidden weights between
forward and backward directional RNNs.
It is only available when use_gru=False.
:type share_weights: bool
:return: Output layer of the RNN group.
:rtype: Variable
"""
def __init__(self,
i_size: int,
h_size: int,
num_stacks: int,
use_gru: bool,
share_rnn_weights: bool):
super().__init__()
rnn_stacks = []
for i in range(num_stacks):
if use_gru:
#default:GRU using tanh
rnn_stacks.append(BiGRUWithBN(i_size=i_size, h_size=h_size))
else:
rnn_stacks.append(
BiRNNWithBN(
i_size=i_size,
h_size=h_size,
share_weights=share_rnn_weights))
i_size = h_size * 2
self.rnn_stacks = nn.LayerList(rnn_stacks)
def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
"""
x: shape [B, T, D]
x_len: shpae [B]
"""
for i, rnn in enumerate(self.rnn_stacks):
x, x_len = rnn(x, x_len)
masks = make_non_pad_mask(x_len) #[B, T]
masks = masks.unsqueeze(-1) # [B, T, 1]
# TODO(Hui Zhang): not support bool multiply
masks = masks.astype(x.dtype)
x = x.multiply(masks)
return x, x_len
paddlespeech/s2t/models/ds2_online/__init__.py 已删除 100644 → 0
浏览文件 @ 0d34c624
# 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.
from .deepspeech2 import DeepSpeech2InferModelOnline
from .deepspeech2 import DeepSpeech2ModelOnline
from paddlespeech.s2t.utils import dynamic_pip_install
import sys
try:
import paddlespeech_ctcdecoders
except ImportError:
try:
package_name = 'paddlespeech_ctcdecoders'
if sys.platform != "win32":
dynamic_pip_install.install(package_name)
except Exception:
raise RuntimeError(
"Can not install package paddlespeech_ctcdecoders on your system. \
The DeepSpeech2 model is not supported for your system")
__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModelOnline']
paddlespeech/s2t/models/ds2_online/conv.py 已删除 100644 → 0
浏览文件 @ 0d34c624
# 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 paddle
from paddlespeech.s2t.modules.subsampling import Conv2dSubsampling4
class Conv2dSubsampling4Online(Conv2dSubsampling4):
def __init__(self, idim: int, odim: int, dropout_rate: float):
super().__init__(idim, odim, dropout_rate, None)
self.output_dim = ((idim - 1) // 2 - 1) // 2 * odim
self.receptive_field_length = 2 * (
3 - 1) + 3 # stride_1 * (kernel_size_2 - 1) + kerel_size_1
def forward(self, x: paddle.Tensor,
x_len: paddle.Tensor) -> [paddle.Tensor, paddle.Tensor]:
x = x.unsqueeze(1) # (b, c=1, t, f)
x = self.conv(x)
#b, c, t, f = paddle.shape(x) #not work under jit
x = x.transpose([0, 2, 1, 3]).reshape([0, 0, -1])
x_len = ((x_len - 1) // 2 - 1) // 2
return x, x_len
paddlespeech/s2t/models/ds2_online/deepspeech2.py 已删除 100644 → 0
浏览文件 @ 0d34c624
# 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.
"""Deepspeech2 ASR Online Model"""
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.s2t.models.ds2_online.conv import Conv2dSubsampling4Online
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils.checkpoint import Checkpoint
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModelOnline']
class CRNNEncoder(nn.Layer):
def __init__(self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=4,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False):
super().__init__()
self.rnn_size = rnn_size
self.feat_size = feat_size # 161 for linear
self.dict_size = dict_size
self.num_rnn_layers = num_rnn_layers
self.num_fc_layers = num_fc_layers
self.rnn_direction = rnn_direction
self.fc_layers_size_list = fc_layers_size_list
self.use_gru = use_gru
self.conv = Conv2dSubsampling4Online(feat_size, 32, dropout_rate=0.0)
self.output_dim = self.conv.output_dim
i_size = self.conv.output_dim
self.rnn = nn.LayerList()
self.layernorm_list = nn.LayerList()
self.fc_layers_list = nn.LayerList()
if rnn_direction == 'bidirect' or rnn_direction == 'bidirectional':
layernorm_size = 2 * rnn_size
elif rnn_direction == 'forward':
layernorm_size = rnn_size
else:
raise Exception("Wrong rnn direction")
for i in range(0, num_rnn_layers):
if i == 0:
rnn_input_size = i_size
else:
rnn_input_size = layernorm_size
if use_gru is True:
self.rnn.append(
nn.GRU(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
else:
self.rnn.append(
nn.LSTM(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
self.layernorm_list.append(nn.LayerNorm(layernorm_size))
self.output_dim = layernorm_size
fc_input_size = layernorm_size
for i in range(self.num_fc_layers):
self.fc_layers_list.append(
nn.Linear(fc_input_size, fc_layers_size_list[i]))
fc_input_size = fc_layers_size_list[i]
self.output_dim = fc_layers_size_list[i]
@property
def output_size(self):
return self.output_dim
def forward(self, x, x_lens, init_state_h_box=None, init_state_c_box=None):
"""Compute Encoder outputs
Args:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
Return:
x (Tensor): encoder outputs, [B, T, D]
x_lens (Tensor): encoder length, [B]
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
if init_state_h_box is not None:
init_state_list = None
if self.use_gru is True:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_list = init_state_h_list
else:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_c_list = paddle.split(
init_state_c_box, self.num_rnn_layers, axis=0)
init_state_list = [(init_state_h_list[i], init_state_c_list[i])
for i in range(self.num_rnn_layers)]
else:
init_state_list = [None] * self.num_rnn_layers
x, x_lens = self.conv(x, x_lens)
final_chunk_state_list = []
for i in range(0, self.num_rnn_layers):
x, final_state = self.rnn[i](x, init_state_list[i],
x_lens) #[B, T, D]
final_chunk_state_list.append(final_state)
x = self.layernorm_list[i](x)
for i in range(self.num_fc_layers):
x = self.fc_layers_list[i](x)
x = F.relu(x)
if self.use_gru is True:
final_chunk_state_h_box = paddle.concat(
final_chunk_state_list, axis=0)
final_chunk_state_c_box = init_state_c_box
else:
final_chunk_state_h_list = [
final_chunk_state_list[i][0] for i in range(self.num_rnn_layers)
]
final_chunk_state_c_list = [
final_chunk_state_list[i][1] for i in range(self.num_rnn_layers)
]
final_chunk_state_h_box = paddle.concat(
final_chunk_state_h_list, axis=0)
final_chunk_state_c_box = paddle.concat(
final_chunk_state_c_list, axis=0)
return x, x_lens, final_chunk_state_h_box, final_chunk_state_c_box
def forward_chunk_by_chunk(self, x, x_lens, decoder_chunk_size=8):
"""Compute Encoder outputs
Args:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
decoder_chunk_size: The chunk size of decoder
Returns:
eouts_list (List of Tensor): The list of encoder outputs in chunk_size: [B, chunk_size, D] * num_chunks
eouts_lens_list (List of Tensor): The list of encoder length in chunk_size: [B] * num_chunks
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
subsampling_rate = self.conv.subsampling_rate
receptive_field_length = self.conv.receptive_field_length
chunk_size = (decoder_chunk_size - 1
) * subsampling_rate + receptive_field_length
chunk_stride = subsampling_rate * decoder_chunk_size
max_len = x.shape[1]
assert (chunk_size <= max_len)
eouts_chunk_list = []
eouts_chunk_lens_list = []
if (max_len - chunk_size) % chunk_stride != 0:
padding_len = chunk_stride - (max_len - chunk_size) % chunk_stride
else:
padding_len = 0
padding = paddle.zeros((x.shape[0], padding_len, x.shape[2]))
padded_x = paddle.concat([x, padding], axis=1)
num_chunk = (max_len + padding_len - chunk_size) / chunk_stride + 1
num_chunk = int(num_chunk)
chunk_state_h_box = None
chunk_state_c_box = None
final_state_h_box = None
final_state_c_box = None
for i in range(0, num_chunk):
start = i * chunk_stride
end = start + chunk_size
x_chunk = padded_x[:, start:end, :]
x_len_left = paddle.where(x_lens - i * chunk_stride < 0,
paddle.zeros_like(x_lens),
x_lens - i * chunk_stride)
x_chunk_len_tmp = paddle.ones_like(x_lens) * chunk_size
x_chunk_lens = paddle.where(x_len_left < x_chunk_len_tmp,
x_len_left, x_chunk_len_tmp)
eouts_chunk, eouts_chunk_lens, chunk_state_h_box, chunk_state_c_box = self.forward(
x_chunk, x_chunk_lens, chunk_state_h_box, chunk_state_c_box)
eouts_chunk_list.append(eouts_chunk)
eouts_chunk_lens_list.append(eouts_chunk_lens)
final_state_h_box = chunk_state_h_box
final_state_c_box = chunk_state_c_box
return eouts_chunk_list, eouts_chunk_lens_list, final_state_h_box, final_state_c_box
class DeepSpeech2ModelOnline(nn.Layer):
"""The DeepSpeech2 network structure for online.
:param audio: Audio spectrogram data layer.
:type audio: Variable
:param text: Transcription text data layer.
:type text: Variable
:param audio_len: Valid sequence length data layer.
:type audio_len: Variable
:param feat_size: feature size for audio.
:type feat_size: int
:param dict_size: Dictionary size for tokenized transcription.
:type dict_size: int
:param num_conv_layers: Number of stacking convolution layers.
:type num_conv_layers: int
:param num_rnn_layers: Number of stacking RNN layers.
:type num_rnn_layers: int
:param rnn_size: RNN layer size (dimension of RNN cells).
:type rnn_size: int
:param num_fc_layers: Number of stacking FC layers.
:type num_fc_layers: int
:param fc_layers_size_list: The list of FC layer sizes.
:type fc_layers_size_list: [int,]
:param use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
:return: A tuple of an output unnormalized log probability layer (
before softmax) and a ctc cost layer.
:rtype: tuple of LayerOutput
"""
def __init__(
self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=4,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False,
blank_id=0,
ctc_grad_norm_type=None, ):
super().__init__()
self.encoder = CRNNEncoder(
feat_size=feat_size,
dict_size=dict_size,
num_conv_layers=num_conv_layers,
num_rnn_layers=num_rnn_layers,
rnn_direction=rnn_direction,
num_fc_layers=num_fc_layers,
fc_layers_size_list=fc_layers_size_list,
rnn_size=rnn_size,
use_gru=use_gru)
self.decoder = CTCDecoder(
odim=dict_size, # <blank> is in vocab
enc_n_units=self.encoder.output_size,
blank_id=blank_id,
dropout_rate=0.0,
reduction=True, # sum
batch_average=True, # sum / batch_size
grad_norm_type=ctc_grad_norm_type)
def forward(self, audio, audio_len, text, text_len):
"""Compute Model loss
Args:
audio (Tensor): [B, T, D]
audio_len (Tensor): [B]
text (Tensor): [B, U]
text_len (Tensor): [B]
Returns:
loss (Tensor): [1]
"""
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
loss = self.decoder(eouts, eouts_len, text, text_len)
return loss
@paddle.no_grad()
def decode(self, audio, audio_len):
# decoders only accept string encoded in utf-8
# Make sure the decoder has been initialized
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
probs = self.decoder.softmax(eouts)
batch_size = probs.shape[0]
self.decoder.reset_decoder(batch_size=batch_size)
self.decoder.next(probs, eouts_len)
trans_best, trans_beam = self.decoder.decode()
return trans_best
@classmethod
def from_pretrained(cls, dataloader, config, checkpoint_path):
"""Build a DeepSpeech2Model model from a pretrained model.
Parameters
----------
dataloader: paddle.io.DataLoader
config: yacs.config.CfgNode
model configs
checkpoint_path: Path or str
the path of pretrained model checkpoint, without extension name
Returns
-------
DeepSpeech2ModelOnline
The model built from pretrained result.
"""
model = cls(
feat_size=dataloader.collate_fn.feature_size,
dict_size=dataloader.collate_fn.vocab_size,
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
infos = Checkpoint().load_parameters(
model, checkpoint_path=checkpoint_path)
logger.info(f"checkpoint info: {infos}")
layer_tools.summary(model)
return model
@classmethod
def from_config(cls, config):
"""Build a DeepSpeec2ModelOnline from config
Parameters
config: yacs.config.CfgNode
config
Returns
-------
DeepSpeech2ModelOnline
The model built from config.
"""
model = cls(
feat_size=config.input_dim,
dict_size=config.output_dim,
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
return model
class DeepSpeech2InferModelOnline(DeepSpeech2ModelOnline):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, audio_chunk, audio_chunk_lens, chunk_state_h_box,
chunk_state_c_box):
eouts_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box = self.encoder(
audio_chunk, audio_chunk_lens, chunk_state_h_box, chunk_state_c_box)
probs_chunk = self.decoder.softmax(eouts_chunk)
return probs_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box
def export(self):
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None,
self.encoder.feat_size], #[B, chunk_size, feat_dim]
dtype='float32'),
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32'),
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32')
])
return static_model
paddlespeech/server/engine/asr/online/asr_engine.py
浏览文件 @ 82c1f4c5
......@@ -25,7 +25,6 @@ from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.resource import CommonTaskResource
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.speech import SpeechSegment
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
......@@ -66,10 +65,13 @@ class PaddleASRConnectionHanddler:
self.text_feature = self.asr_engine.executor.text_feature
if "deepspeech2" in self.model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
self.am_predictor = self.asr_engine.executor.am_predictor
self.collate_fn_test = SpeechCollator.from_config(self.model_config)
# extract feat, new only fbank in conformer model
self.preprocess_conf = self.model_config.preprocess_config
self.preprocess_args = {"train": False}
self.preprocessing = Transformation(self.preprocess_conf)
self.decoder = CTCDecoder(
odim=self.model_config.output_dim, # <blank> is in vocab
enc_n_units=self.model_config.rnn_layer_size * 2,
......@@ -89,10 +91,8 @@ class PaddleASRConnectionHanddler:
cfg.num_proc_bsearch)
# frame window and frame shift, in samples unit
self.win_length = int(self.model_config.window_ms / 1000 *
self.sample_rate)
self.n_shift = int(self.model_config.stride_ms / 1000 *
self.sample_rate)
self.win_length = self.preprocess_conf.process[0]['win_length']
self.n_shift = self.preprocess_conf.process[0]['n_shift']
elif "conformer" in self.model_type or "transformer" in self.model_type:
# acoustic model
......@@ -114,20 +114,15 @@ class PaddleASRConnectionHanddler:
raise ValueError(f"Not supported: {self.model_type}")
def extract_feat(self, samples):
# we compute the elapsed time of first char occuring
# we compute the elapsed time of first char occuring
# and we record the start time at the first pcm sample arraving
if "deepspeech2online" in self.model_type:
# self.reamined_wav stores all the samples,
# self.reamined_wav stores all the samples,
# include the original remained_wav and this package samples
samples = np.frombuffer(samples, dtype=np.int16)
assert samples.ndim == 1
# pcm16 -> pcm 32
# pcm2float will change the orignal samples,
# so we shoule do pcm2float before concatenate
samples = pcm2float(samples)
if self.remained_wav is None:
self.remained_wav = samples
else:
......@@ -137,26 +132,11 @@ class PaddleASRConnectionHanddler:
f"The connection remain the audio samples: {self.remained_wav.shape}"
)
# read audio
speech_segment = SpeechSegment.from_pcm(
self.remained_wav, self.sample_rate, transcript=" ")
# audio augment
self.collate_fn_test.augmentation.transform_audio(speech_segment)
# extract speech feature
spectrum, transcript_part = self.collate_fn_test._speech_featurizer.featurize(
speech_segment, self.collate_fn_test.keep_transcription_text)
# CMVN spectrum
if self.collate_fn_test._normalizer:
spectrum = self.collate_fn_test._normalizer.apply(spectrum)
# spectrum augment
feat = self.collate_fn_test.augmentation.transform_feature(spectrum)
# audio_len is frame num
frame_num = feat.shape[0]
feat = paddle.to_tensor(feat, dtype='float32')
feat = paddle.unsqueeze(feat, axis=0)
# fbank
feat = self.preprocessing(self.remained_wav,
**self.preprocess_args)
feat = paddle.to_tensor(
feat, dtype="float32").unsqueeze(axis=0)
if self.cached_feat is None:
self.cached_feat = feat
......@@ -170,8 +150,11 @@ class PaddleASRConnectionHanddler:
if self.device is None:
self.device = self.cached_feat.place
self.num_frames += frame_num
self.remained_wav = self.remained_wav[self.n_shift * frame_num:]
# cur frame step
num_frames = feat.shape[1]
self.num_frames += num_frames
self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
logger.info(
f"process the audio feature success, the connection feat shape: {self.cached_feat.shape}"
......@@ -190,7 +173,7 @@ class PaddleASRConnectionHanddler:
f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
)
# self.reamined_wav stores all the samples,
# self.reamined_wav stores all the samples,
# include the original remained_wav and this package samples
if self.remained_wav is None:
self.remained_wav = samples
......@@ -246,7 +229,7 @@ class PaddleASRConnectionHanddler:
def reset(self):
if "deepspeech2" in self.model_type:
# for deepspeech2
# for deepspeech2
# init state
self.chunk_state_h_box = np.zeros(
(self.model_config.num_rnn_layers, 1,
......@@ -275,7 +258,7 @@ class PaddleASRConnectionHanddler:
## conformer
# cache for conformer online
# cache for conformer online
self.subsampling_cache = None
self.elayers_output_cache = None
self.conformer_cnn_cache = None
......@@ -359,7 +342,7 @@ class PaddleASRConnectionHanddler:
# update feat cache
self.cached_feat = self.cached_feat[:, end - cached_feature_num:, :]
# return trans_best[0]
# return trans_best[0]
elif "conformer" in self.model_type or "transformer" in self.model_type:
try:
logger.info(
......@@ -565,7 +548,7 @@ class PaddleASRConnectionHanddler:
@paddle.no_grad()
def rescoring(self):
"""Second-Pass Decoding,
"""Second-Pass Decoding,
only for conformer and transformer model.
"""
if "deepspeech2" in self.model_type:
......@@ -652,11 +635,11 @@ class PaddleASRConnectionHanddler:
## asr results
# hyps[0][0]: the sentence word-id in the vocab with a tuple
# hyps[0][1]: the sentence decoding probability with all paths
## timestamp
## timestamp
# hyps[0][2]: viterbi_blank ending probability
# hyps[0][3]: viterbi_non_blank dending probability
# hyps[0][4]: current_token_prob,
# hyps[0][5]: times_viterbi_blank ending timestamp,
# hyps[0][5]: times_viterbi_blank ending timestamp,
# hyps[0][6]: times_titerbi_non_blank encding timestamp.
self.hyps = [hyps[best_index][0]]
logger.info(f"best hyp ids: {self.hyps}")
......@@ -752,16 +735,19 @@ class ASRServerExecutor(ASRExecutor):
self.config = CfgNode(new_allowed=True)
self.config.merge_from_file(self.cfg_path)
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type,
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
self.vocab = self.config.vocab_filepath
with UpdateConfig(self.config):
if "deepspeech2" in model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
self.vocab = self.config.vocab_filepath
self.config.decode.lang_model_path = os.path.join(
MODEL_HOME, 'language_model',
self.config.decode.lang_model_path)
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
......@@ -772,14 +758,6 @@ class ASRServerExecutor(ASRExecutor):
elif "conformer" in model_type or "transformer" in model_type:
logger.info("start to create the stream conformer asr engine")
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.vocab = self.config.vocab_filepath
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type,
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
# update the decoding method
if decode_method:
self.config.decode.decoding_method = decode_method
......
paddlespeech/server/engine/asr/paddleinference/asr_engine.py
浏览文件 @ 82c1f4c5
......@@ -54,6 +54,7 @@ class ASRServerExecutor(ASRExecutor):
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
self.max_len = 50
self.task_resource.set_task_model(model_tag=tag)
if cfg_path is None or am_model is None or am_params is None:
self.res_path = self.task_resource.res_dir
......@@ -80,22 +81,24 @@ class ASRServerExecutor(ASRExecutor):
self.config.merge_from_file(self.cfg_path)
with UpdateConfig(self.config):
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
from paddlespeech.s2t.io.collator import SpeechCollator
if "deepspeech2" in model_type:
self.vocab = self.config.vocab_filepath
if self.config.spm_model_prefix:
self.config.spm_model_prefix = os.path.join(
self.res_path, self.config.spm_model_prefix)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab,
spm_model_prefix=self.config.spm_model_prefix)
self.config.decode.lang_model_path = os.path.join(
MODEL_HOME, 'language_model',
self.config.decode.lang_model_path)
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = self.task_resource.res_dict['lm_url']
lm_md5 = self.task_resource.res_dict['lm_md5']
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
elif "conformer" in model_type or "transformer" in model_type:
raise Exception("wrong type")
else:
raise Exception("wrong type")
......@@ -125,7 +128,7 @@ class ASRServerExecutor(ASRExecutor):
cfg = self.config.decode
audio = self._inputs["audio"]
audio_len = self._inputs["audio_len"]
if "deepspeech2online" in model_type or "deepspeech2offline" in model_type:
if "deepspeech2" in model_type:
decode_batch_size = audio.shape[0]
# init once
self.decoder.init_decoder(
......@@ -192,7 +195,7 @@ class ASREngine(BaseEngine):
return True
def run(self, audio_data):
"""engine run
"""engine run
Args:
audio_data (bytes): base64.b64decode
......
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