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PaddleSpeech/examples/ted_en_zh/t0/conf/transformer.yaml

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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train.tiny
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.05 # second
max_input_len: 30.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.01
max_output_input_ratio: 20.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: data/bpe_unigram_8000
mean_std_filepath: ""
# augmentation_config: conf/augmentation.json
batch_size: 10
raw_wav: True # use raw_wav or kaldi feature
specgram_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 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: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
asr_weight: 0.0
ctc_weight: 0.0
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 2
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 5
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 5
error_rate_type: char-bleu
decoding_method: fullsentence # 'fullsentence', 'simultaneous'
alpha: 2.5
beta: 0.3
beam_size: 10
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.