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train.yaml
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# ############################################################################
# Model: Direct SLU
# Encoder: Pre-trained ASR encoder -> LSTM
# Decoder: GRU + beamsearch
# Tokens: BPE with unigram
# losses: NLL
# Training: SLURP
# Authors: Loren Lugosch, Mirco Ravanelli 2020
# ############################################################################
# Seed needs to be set at top of yaml, before objects with parameters are made
seed: 1986
__set_seed: !apply:speechbrain.utils.seed_everything [!ref <seed>]
output_folder: !ref results/better_tokenizer/<seed>
save_folder: !ref <output_folder>/save
train_log: !ref <output_folder>/train_log.txt
test_wer_file: !ref <output_folder>/wer_test_real.txt
# Data for augmentation
NOISE_DATASET_URL: https://www.dropbox.com/scl/fi/a09pj97s5ifan81dqhi4n/noises.zip?rlkey=j8b0n9kdjdr32o1f06t0cw5b7&dl=1
RIR_DATASET_URL: https://www.dropbox.com/scl/fi/linhy77c36mu10965a836/RIRs.zip?rlkey=pg9cu8vrpn2u173vhiqyu743u&dl=1
# Data files
# The SLURP dataset will be automatically downloaded in the specified data_folder
data_folder: !PLACEHOLDER # e.g, /localscratch/SLURP
data_folder_noise: !ref <data_folder>/noise # The noisy sequences for data augmentation will automatically be downloaded here.
data_folder_rir: !ref <data_folder>/rir # The impulse responses used for data augmentation will automatically be downloaded here.
train_splits: ["train_synthetic", "train_real"]
csv_train: !ref <save_folder>/train-type=direct.csv
csv_valid: !ref <save_folder>/devel-type=direct.csv
csv_test: !ref <save_folder>/test-type=direct.csv
noise_annotation: !ref <save_folder>/noise.csv
rir_annotation: !ref <save_folder>/rir.csv
tokenizer_file: https://www.dropbox.com/s/tmwq12r5vgcsif9/58_unigram.model?dl=1
skip_prep: False
####################### Training Parameters ####################################
number_of_epochs: 20
batch_size: 16
lr: 0.0003
# token_type: unigram # ["unigram", "bpe", "char"]
sorting: random
ckpt_interval_minutes: 15 # save checkpoint every N min
####################### Model Parameters #######################################
sample_rate: 16000
emb_size: 128
dec_neurons: 512
output_neurons: 58 # index(eos/bos) = 0
ASR_encoder_dim: 512
encoder_dim: 256
# Decoding parameters
bos_index: 0
eos_index: 0
min_decode_ratio: 0.0
max_decode_ratio: 10.0
slu_beam_size: 80
eos_threshold: 1.5
temperature: 1.25
num_workers: 4
dataloader_opts:
num_workers: !ref <num_workers>
batch_size: !ref <batch_size>
shuffle: True
epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <number_of_epochs>
# Download and prepare the dataset of noisy sequences for augmentation
prepare_noise_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
URL: !ref <NOISE_DATASET_URL>
dest_folder: !ref <data_folder_noise>
ext: wav
csv_file: !ref <noise_annotation>
# Download and prepare the dataset of room impulse responses for augmentation
prepare_rir_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
URL: !ref <RIR_DATASET_URL>
dest_folder: !ref <data_folder_rir>
ext: wav
csv_file: !ref <rir_annotation>
# Add reverberation to input signal
add_reverb: !new:speechbrain.augment.time_domain.AddReverb
csv_file: !ref <rir_annotation>
reverb_sample_rate: !ref <sample_rate>
clean_sample_rate: !ref <sample_rate>
num_workers: !ref <num_workers>
# Add noise to input signal
snr_low: 0 # Min SNR for noise augmentation
snr_high: 15 # Max SNR for noise augmentation
add_noise: !new:speechbrain.augment.time_domain.AddNoise
csv_file: !ref <noise_annotation>
snr_low: !ref <snr_low>
snr_high: !ref <snr_high>
noise_sample_rate: !ref <sample_rate>
clean_sample_rate: !ref <sample_rate>
num_workers: !ref <num_workers>
############################## Augmentations ###################################
# Speed perturbation
speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb
orig_freq: !ref <sample_rate>
speeds: [90, 95, 105, 110]
# Frequency drop: randomly drops a number of frequency bands to zero.
drop_freq: !new:speechbrain.augment.time_domain.DropFreq
drop_freq_low: 0
drop_freq_high: 1
drop_freq_count_low: 1
drop_freq_count_high: 3
drop_freq_width: 0.05
# Time drop: randomly drops a number of temporal chunks.
drop_chunk: !new:speechbrain.augment.time_domain.DropChunk
drop_length_low: 1000
drop_length_high: 2000
drop_count_low: 1
drop_count_high: 3
# Augmenter: Combines previously defined augmentations to perform data augmentation
wav_augment: !new:speechbrain.augment.augmenter.Augmenter
concat_original: True
shuffle_augmentations: True
min_augmentations: 1
max_augmentations: 4
augment_prob: 1.0
augmentations: [
!ref <add_noise>,
!ref <add_reverb>,
!ref <speed_perturb>,
!ref <drop_freq>,
!ref <drop_chunk>]
############################## Models ##########################################
asr_model_source: speechbrain/asr-crdnn-rnnlm-librispeech
slu_enc: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <ASR_encoder_dim>]
lstm: !new:speechbrain.nnet.RNN.LSTM
input_size: !ref <ASR_encoder_dim>
bidirectional: True
hidden_size: !ref <encoder_dim>
num_layers: 2
linear: !new:speechbrain.nnet.linear.Linear
input_size: !ref <encoder_dim> * 2
n_neurons: !ref <encoder_dim>
output_emb: !new:speechbrain.nnet.embedding.Embedding
num_embeddings: !ref <output_neurons>
embedding_dim: !ref <emb_size>
dec: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
enc_dim: !ref <encoder_dim>
input_size: !ref <emb_size>
rnn_type: gru
attn_type: keyvalue
hidden_size: !ref <dec_neurons>
attn_dim: 512
num_layers: 3
scaling: 1.0
dropout: 0.0
seq_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <dec_neurons>
n_neurons: !ref <output_neurons>
modules:
slu_enc: !ref <slu_enc>
output_emb: !ref <output_emb>
dec: !ref <dec>
seq_lin: !ref <seq_lin>
model: !new:torch.nn.ModuleList
- [!ref <slu_enc>, !ref <output_emb>,
!ref <dec>, !ref <seq_lin>]
tokenizer: !new:sentencepiece.SentencePieceProcessor
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
collect_in: !ref <save_folder>/SLURM_tokenizer
loadables:
tokenizer: !ref <tokenizer>
paths:
tokenizer: !ref <tokenizer_file>
beam_searcher: !new:speechbrain.decoders.S2SRNNBeamSearcher
embedding: !ref <output_emb>
decoder: !ref <dec>
linear: !ref <seq_lin>
bos_index: !ref <bos_index>
eos_index: !ref <eos_index>
min_decode_ratio: !ref <min_decode_ratio>
max_decode_ratio: !ref <max_decode_ratio>
beam_size: !ref <slu_beam_size>
eos_threshold: !ref <eos_threshold>
temperature: !ref <temperature>
max_attn_shift: 30
using_max_attn_shift: False
opt_class: !name:torch.optim.Adam
lr: !ref <lr>
lr_annealing: !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: !ref <lr>
improvement_threshold: 0.0025
annealing_factor: 0.8
patient: 0
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: !ref <save_folder>
recoverables:
model: !ref <model>
scheduler: !ref <lr_annealing>
counter: !ref <epoch_counter>
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: True
seq_cost: !name:speechbrain.nnet.losses.nll_loss
label_smoothing: 0.1
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
save_file: !ref <train_log>
error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
split_tokens: True