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# Generated 2023-06-19 from:
# /kaggle/working/direct-train.yaml
# yamllint disable
# ############################################################################
# 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:torch.manual_seed [1986]
# ADD: prepared folder from prev step
prepared_folder: results/prepared
output_folder: results/better_tokenizer/1986
save_folder: results/better_tokenizer/1986/save
train_log: results/better_tokenizer/1986/train_log.txt
log_folder: results/better_tokenizer/1986/log
# Data files
# The SLURP dataset will be automatically downloaded in the specified data_folder
# data_folder: !PLACEHOLDER # e.g, /localscratch/SLURP
data_folder: /slurp/audio
data_folder_rirs: /slurp/audio
train_splits: [train_synthetic, train_real]
csv_train: results/prepared/train-type=direct-sample=0.2.csv
csv_valid: results/prepared/devel-type=direct-sample=0.2.csv
csv_test: results/prepared/test-type=direct.csv
tokenizer_file: https://www.dropbox.com/s/tmwq12r5vgcsif9/58_unigram.model?dl=1
skip_prep: false
# Training parameters
number_of_epochs: 40 # default 20
batch_size: 12 # default 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
dataloader_opts:
batch_size: 12
shuffle: true
epoch_counter: &id009 !new:speechbrain.utils.epoch_loop.EpochCounter
limit: 40
# Models
asr_model: !apply:speechbrain.pretrained.EncoderDecoderASR.from_hparams
source: speechbrain/asr-crdnn-rnnlm-librispeech
run_opts: {device: cuda:0}
slu_enc: &id001 !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, 512]
lstm: !new:speechbrain.nnet.RNN.LSTM
input_size: 512
bidirectional: true
hidden_size: 256
num_layers: 2
linear: !new:speechbrain.nnet.linear.Linear
input_size: 512
n_neurons: 256
output_emb: &id002 !new:speechbrain.nnet.embedding.Embedding
num_embeddings: 58
embedding_dim: 128
dec: &id003 !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
enc_dim: 256
input_size: 128
rnn_type: gru
attn_type: keyvalue
hidden_size: 512
attn_dim: 512
num_layers: 3
scaling: 1.0
dropout: 0.0
seq_lin: &id004 !new:speechbrain.nnet.linear.Linear
input_size: 512
n_neurons: 58
env_corrupt: &id005 !new:speechbrain.lobes.augment.EnvCorrupt
openrir_folder: /slurp/audio
babble_prob: 0.0
reverb_prob: 0.0
noise_prob: 1.0
noise_snr_low: 0
noise_snr_high: 15
modules:
slu_enc: *id001
output_emb: *id002
dec: *id003
seq_lin: *id004
env_corrupt: *id005
model: &id007 !new:torch.nn.ModuleList
- [*id001, *id002, *id003, *id004]
tokenizer: &id006 !new:sentencepiece.SentencePieceProcessor
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
collect_in: results/better_tokenizer/1986/save/SLURM_tokenizer
loadables:
tokenizer: *id006
paths:
tokenizer: https://www.dropbox.com/s/tmwq12r5vgcsif9/58_unigram.model?dl=1
beam_searcher: !new:speechbrain.decoders.S2SRNNBeamSearcher
embedding: *id002
decoder: *id003
linear: *id004
bos_index: 0
eos_index: 0
min_decode_ratio: 0.0
max_decode_ratio: 10.0
beam_size: 80
eos_threshold: 1.5
temperature: 1.25
using_max_attn_shift: false
max_attn_shift: 30
coverage_penalty: 0.
opt_class: !name:torch.optim.Adam
lr: 0.0003
lr_annealing: &id008 !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: 0.0003
improvement_threshold: 0.0025
annealing_factor: 0.8
patient: 0
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: results/better_tokenizer/1986/save
recoverables:
model: *id007
scheduler: *id008
counter: *id009
augmentation: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
sample_rate: 16000
speeds: [95, 100, 105]
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: true
seq_cost: !name:speechbrain.nnet.losses.nll_loss
label_smoothing: 0.1
# DEFAULT: train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
# save_file: !ref <train_log>
train_logger: !new:speechbrain.utils.train_logger.TensorboardLogger
save_dir: results/better_tokenizer/1986/log
error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
split_tokens: true
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