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PaddleSpeech
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fix the bidirect rnn, add deepspeech2.yaml for aishell, tiny, librispeech

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pull/735/head
huangyuxin 3 years ago
parent 85d5021475
commit 7a3d164122
4 changed files with 213 additions and 5 deletions
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15
deepspeech/models/ds2_online/deepspeech2.py
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@ -56,12 +56,17 @@ class CRNNEncoder(nn.Layer):
self.rnn = nn.LayerList()
self.layernorm_list = nn.LayerList()
self.fc_layers_list = nn.LayerList()
layernorm_size = rnn_size
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 = rnn_size
rnn_input_size = layernorm_size
if use_gru == True:
self.rnn.append(
nn.GRU(
@ -78,7 +83,7 @@ class CRNNEncoder(nn.Layer):
direction=rnn_direction))
self.layernorm_list.append(nn.LayerNorm(layernorm_size))
fc_input_size = rnn_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]))
@ -385,8 +390,8 @@ class DeepSpeech2InferModelOnline(DeepSpeech2ModelOnline):
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, self.encoder.feat_size
], #[B, chunk_size, feat_dim]
shape=[None, None,
self.encoder.feat_size], #[B, chunk_size, feat_dim]
dtype='float32'), # audio, [B,T,D]
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]

67
examples/aishell/s0/conf/deepspeech2_online.yaml
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@ -0,0 +1,67 @@
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.0
max_input_len: 27.0 # second
min_output_len: 0.0
max_output_len: .inf
min_output_input_ratio: 0.00
max_output_input_ratio: .inf
collator:
batch_size: 32 # one gpu
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
specgram_type: linear #linear, mfcc, fbank
feat_dim:
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
model:
num_conv_layers: 2
num_rnn_layers: 4
rnn_layer_size: 1024
rnn_direction: bidirect
num_fc_layers: 2
fc_layers_size_list: 512, 256
use_gru: True
training:
n_epoch: 50
lr: 2e-3
lr_decay: 0.83
weight_decay: 1e-06
global_grad_clip: 3.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
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
cutoff_prob: 0.99
cutoff_top_n: 40
num_proc_bsearch: 10

67
examples/librispeech/s0/conf/deepspeech2_online.yaml
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@ -0,0 +1,67 @@
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev-clean
test_manifest: data/manifest.test-clean
min_input_len: 0.0
max_input_len: 27.0 # second
min_output_len: 0.0
max_output_len: .inf
min_output_input_ratio: 0.00
max_output_input_ratio: .inf
collator:
batch_size: 20
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
specgram_type: linear
target_sample_rate: 16000
max_freq: None
n_fft: None
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
model:
num_conv_layers: 2
num_rnn_layers: 3
rnn_layer_size: 2048
rnn_direction: forward
num_fc_layers: 2
fc_layers_size_list: 512, 256
use_gru: False
training:
n_epoch: 50
lr: 1e-3
lr_decay: 0.83
weight_decay: 1e-06
global_grad_clip: 5.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 128
error_rate_type: wer
decoding_method: ctc_beam_search
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 1.9
beta: 0.3
beam_size: 500
cutoff_prob: 1.0
cutoff_top_n: 40
num_proc_bsearch: 8

69
examples/tiny/s0/conf/deepspeech2_online.yaml
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@ -0,0 +1,69 @@
# 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: 0.0
max_input_len: 27.0
min_output_len: 0.0
max_output_len: 400.0
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
mean_std_filepath: data/mean_std.json
unit_type: char
vocab_filepath: data/vocab.txt
augmentation_config: conf/augmentation.json
random_seed: 0
spm_model_prefix:
specgram_type: linear
feat_dim:
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
batch_size: 4
model:
num_conv_layers: 2
num_rnn_layers: 4
rnn_layer_size: 2048
rnn_direction: forward
num_fc_layers: 2
fc_layers_size_list: 512, 256
use_gru: True
training:
n_epoch: 10
lr: 1e-5
lr_decay: 1.0
weight_decay: 1e-06
global_grad_clip: 5.0
log_interval: 1
checkpoint:
kbest_n: 3
latest_n: 2
decoding:
batch_size: 128
error_rate_type: wer
decoding_method: ctc_beam_search
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 500
cutoff_prob: 1.0
cutoff_top_n: 40
num_proc_bsearch: 8
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