# 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