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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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add librispeech asr1

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pull/1225/head
huangyuxin 3 years ago
parent fb6d1e2c11
commit 41eeed0450
20 changed files with 590 additions and 627 deletions
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2
examples/aishell/asr1/local/align.sh
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@ -24,7 +24,7 @@ python3 -u ${BIN_DIR}/alignment.py \
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.align \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decode_batch_size ${batch_size}
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in ctc alignment!"

10
examples/aishell/asr1/local/test.sh
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@ -30,7 +30,7 @@ for type in attention ctc_greedy_search; do
# stream decoding only support batchsize=1
batch_size=1
else
batch_size=64
batch_size=1
fi
output_dir=${ckpt_prefix}
mkdir -p ${output_dir}
@ -40,8 +40,8 @@ for type in attention ctc_greedy_search; do
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.decode_batch_size ${batch_size}
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
@ -60,8 +60,8 @@ for type in ctc_prefix_beam_search attention_rescoring; do
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.batch_size ${batch_size}
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"

4
examples/aishell/asr1/local/test_wav.sh
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@ -46,8 +46,8 @@ for type in attention_rescoring; do
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.decode_batch_size ${batch_size} \
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size} \
--audio_file ${audio_file}
if [ $? -ne 0 ]; then

4
examples/csmsc/voc5/README.md
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@ -125,8 +125,8 @@ HiFiGAN checkpoint contains files listed below.
```text
hifigan_csmsc_ckpt_0.1.1
├── default.yaml # default config used to train hifigan
├── feats_stats.npy # statistics used to normalize spectrogram when training hifigan
└── snapshot_iter_2500000.pdz # generator parameters of hifigan
├── feats_stats.npy # generator parameters of hifigan
└── snapshot_iter_2500000.pdz # statistics used to normalize spectrogram when training hifigan
```
## Acknowledgement

184
examples/librispeech/asr1/conf/chunk_conformer.yaml
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@ -1,103 +1,99 @@
# network architecture
model:
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
causal: True
use_dynamic_chunk: true
cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
use_dynamic_left_chunk: false
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
causal: True
use_dynamic_chunk: true
cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
use_dynamic_left_chunk: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
###########################################
# Data #
###########################################
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
collator:
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 16
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 16
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Training #
###########################################
n_epoch: 120
accum_grad: 8
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
training:
n_epoch: 120
accum_grad: 8
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 128
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
beam_size: 10
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.

179
examples/librispeech/asr1/conf/chunk_transformer.yaml
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@ -1,103 +1,90 @@
# network architecture
model:
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
use_dynamic_chunk: true
use_dynamic_left_chunk: false
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
use_dynamic_chunk: true
use_dynamic_left_chunk: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
###########################################
# Data #
###########################################
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
collator:
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
training:
n_epoch: 120
accum_grad: 1
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.
###########################################
# Training #
###########################################
n_epoch: 120
accum_grad: 1
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5

181
examples/librispeech/asr1/conf/conformer.yaml
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@ -1,104 +1,97 @@
# network architecture
model:
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
ctc_grad_norm_type: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
ctc_grad_norm_type: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test-clean
###########################################
# Data #
###########################################
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test-clean
collator:
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 16
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 16
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
training:
n_epoch: 70
accum_grad: 8
global_grad_clip: 3.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
beam_size: 10
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.
###########################################
# Training #
###########################################
n_epoch: 70
accum_grad: 8
global_grad_clip: 3.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5

183
examples/librispeech/asr1/conf/transformer.yaml
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@ -1,110 +1,89 @@
# network architecture
model:
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test-clean
min_input_len: 0.5 # second
max_input_len: 30.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 100.0
###########################################
# Data #
###########################################
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test-clean
collator:
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 32
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
training:
n_epoch: 120
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.
###########################################
# Dataloader #
###########################################
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/preprocess.yaml
feat_dim: 80
stride_ms: 10.0
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: 32
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
augmentation_config: conf/preprocess.yaml
num_workers: 0
subsampling_factor: 1
num_encs: 1
###########################################
# Training #
###########################################
n_epoch: 120
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5

11
examples/librispeech/asr1/conf/tuning/chunk_decode.yaml
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@ -0,0 +1,11 @@
decode_batch_size: 128
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
beam_size: 10
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.

11
examples/librispeech/asr1/conf/tuning/decode.yaml
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@ -0,0 +1,11 @@
decode_batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
beam_size: 10
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

10
examples/librispeech/asr1/local/align.sh
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@ -1,7 +1,7 @@
#!/bin/bash
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_prefix=$2
decode_config_path=$2
ckpt_prefix=$3
batch_size=1
output_dir=${ckpt_prefix}
@ -20,9 +21,10 @@ mkdir -p ${output_dir}
python3 -u ${BIN_DIR}/alignment.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.align \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.batch_size ${batch_size}
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in ctc alignment!"

22
examples/librispeech/asr1/local/test.sh
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@ -15,8 +15,8 @@ recog_set="test-clean"
stage=0
stop_stage=100
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
if [ $# != 3 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
exit -1
fi
@ -24,7 +24,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_prefix=$2
decode_config_path=$2
ckpt_prefix=$3
chunk_mode=false
if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
@ -52,10 +53,11 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
python3 -u ${BIN_DIR}/test.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_config ${decode_config_path} \
--result_file ${ckpt_prefix}.${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.batch_size ${batch_size}
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
@ -76,10 +78,11 @@ for type in ctc_greedy_search; do
python3 -u ${BIN_DIR}/test.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_config ${decode_config_path} \
--result_file ${ckpt_prefix}.${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.batch_size ${batch_size}
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"
@ -96,10 +99,11 @@ for type in ctc_prefix_beam_search attention_rescoring; do
python3 -u ${BIN_DIR}/test.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_config ${decode_config_path} \
--result_file ${ckpt_prefix}.${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.batch_size ${batch_size}
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size}
if [ $? -ne 0 ]; then
echo "Failed in evaluation!"

14
examples/librispeech/asr1/local/test_wav.sh
Unescape View File

@ -1,7 +1,7 @@
#!/bin/bash
if [ $# != 3 ];then
echo "usage: ${0} config_path ckpt_path_prefix audio_file"
if [ $# != 4 ];then
echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
exit -1
fi
@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1
ckpt_prefix=$2
audio_file=$3
decode_config_path=$2
ckpt_prefix=$3
audio_file=$4
mkdir -p data
wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
@ -49,10 +50,11 @@ for type in attention_rescoring; do
python3 -u ${BIN_DIR}/test_wav.py \
--ngpu ${ngpu} \
--config ${config_path} \
--decode_config ${decode_config_path} \
--result_file ${output_dir}/${type}.rsl \
--checkpoint_path ${ckpt_prefix} \
--opts decoding.decoding_method ${type} \
--opts decoding.batch_size ${batch_size} \
--opts decode.decoding_method ${type} \
--opts decode.decode_batch_size ${batch_size} \
--audio_file ${audio_file}
#score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel}.model --wer true ${expdir}/${decode_dir} ${dict}

7
examples/librispeech/asr1/run.sh
Unescape View File

@ -8,6 +8,7 @@ gpus=0,1,2,3
stage=0
stop_stage=50
conf_path=conf/transformer.yaml
decode_conf_path=conf/tuning/decode.yaml
avg_num=30
audio_file=data/demo_002_en.wav
@ -34,17 +35,17 @@ fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n
CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || 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
# ctc alignment of test data
CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# test a single .wav file
CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${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
if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then

197
examples/tiny/asr1/conf/conformer.yaml
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@ -1,116 +1,105 @@
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.tiny
dev_manifest: data/manifest.tiny
test_manifest: data/manifest.tiny
min_input_len: 0.5 # second
max_input_len: 20.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
###########################################
# Data #
###########################################
train_manifest: data/manifest.tiny
dev_manifest: data/manifest.tiny
test_manifest: data/manifest.tiny
min_input_len: 0.5 # second
max_input_len: 20.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
mean_std_filepath: ""
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_200'
augmentation_config: conf/preprocess.yaml
batch_size: 4
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
###########################################
# Dataloader #
###########################################
mean_std_filepath: ""
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_200'
augmentation_config: conf/preprocess.yaml
batch_size: 4
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
############################################
# Network Architecture #
############################################
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 5
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 1
checkpoint:
kbest_n: 10
latest_n: 1
###########################################
# training #
###########################################
n_epoch: 5
accum_grad: 4
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 1
checkpoint:
kbest_n: 10
latest_n: 1
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

188
examples/tiny/asr1/conf/transformer.yaml
Unescape View File

@ -1,110 +1,98 @@
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.tiny
dev_manifest: data/manifest.tiny
test_manifest: data/manifest.tiny
min_input_len: 0.5 # second
max_input_len: 20.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
###########################################
# Data #
###########################################
train_manifest: data/manifest.tiny
dev_manifest: data/manifest.tiny
test_manifest: data/manifest.tiny
min_input_len: 0.5 # second
max_input_len: 20.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
mean_std_filepath: data/mean_std.json
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_200'
augmentation_config: conf/preprocess.yaml
batch_size: 4
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
###########################################
# Dataloader #
###########################################
mean_std_filepath: data/mean_std.json
vocab_filepath: data/lang_char/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/lang_char/bpe_unigram_200'
augmentation_config: conf/preprocess.yaml
batch_size: 4
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
############################################
# Network Architecture #
############################################
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 5
accum_grad: 1
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 1
checkpoint:
kbest_n: 2
latest_n: 1
###########################################
# training #
###########################################
n_epoch: 5
accum_grad: 1
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 1
checkpoint:
kbest_n: 2
latest_n: 1
decoding:
batch_size: 8 #64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.

2
paddlespeech/s2t/exps/u2/bin/alignment.py
Unescape View File

@ -46,7 +46,7 @@ if __name__ == "__main__":
if args.decode_config:
decode_confs = CfgNode(new_allowed=True)
decode_confs.merge_from_file(args.decode_config)
config.decoding = decode_confs
config.decode = decode_confs
if args.opts:
config.merge_from_list(args.opts)
config.freeze()

2
paddlespeech/s2t/exps/u2/bin/test.py
Unescape View File

@ -50,7 +50,7 @@ if __name__ == "__main__":
if args.decode_config:
decode_confs = CfgNode(new_allowed=True)
decode_confs.merge_from_file(args.decode_config)
config.decoding = decode_confs
config.decode = decode_confs
if args.opts:
config.merge_from_list(args.opts)
config.freeze()

4
paddlespeech/s2t/exps/u2/bin/test_wav.py
Unescape View File

@ -81,7 +81,7 @@ class U2Infer():
ilen = paddle.to_tensor(feat.shape[0])
xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
decode_config = self.config.decoding
decode_config = self.config.decode
result_transcripts = self.model.decode(
xs,
ilen,
@ -135,7 +135,7 @@ if __name__ == "__main__":
if args.decode_config:
decode_confs = CfgNode(new_allowed=True)
decode_confs.merge_from_file(args.decode_config)
config.decoding = decode_confs
config.decode = decode_confs
if args.opts:
config.merge_from_list(args.opts)
config.freeze()

2
paddlespeech/s2t/exps/u2/config.py
Unescape View File

@ -29,7 +29,7 @@ U2Model.params(_C)
U2Trainer.params(_C)
_C.decoding = U2Tester.params()
_C.decode = U2Tester.params()
def get_cfg_defaults():

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