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# ################################
# Model: wav2vec2 + DNN + CTC
# Augmentation: SpecAugment
# Authors: Titouan Parcollet 2021
# ################################
# Seed needs to be set at top of yaml, before objects with parameters are made
seed: 1234
__set_seed: !!python/object/apply:torch.manual_seed [!ref <seed>]
output_folder: TunisianASR/results/14epoch_tunisian/1234/
wer_file: !ref <output_folder>/wer.txt
save_folder: !ref <output_folder>/save
train_log: !ref <output_folder>/train_log.txt
# URL for the biggest LeBenchmark wav2vec french.
wav2vec2_folder: !ref <save_folder>/wav2vec2_checkpoint
# Data files
data_folder: /gpfsscratch/rech/nou/uzn19yk/tunisian_junk # e.g, /localscratch/cv-corpus-5.1-2020-06-22/fr
train_tsv_file: !ref <data_folder>/train.tsv # Standard CommonVoice .tsv files
dev_tsv_file: !ref <data_folder>/dev.tsv # Standard CommonVoice .tsv files
test_tsv_file: !ref <data_folder>/test.tsv # Standard CommonVoice .tsv files
accented_letters: True
language: fr # use 'it' for Italian, 'rw' for Kinyarwanda, 'en' for english
train_csv: /gpfsscratch/rech/nou/uzn19yk/tunisian_csvs/good_final/train.csv
valid_csv: /gpfsscratch/rech/nou/uzn19yk/tunisian_csvs/good_final/dev.csv
test_csv:
- /gpfsscratch/rech/nou/uzn19yk/tunisian_csvs/full_annotation_test.csv
- /gpfsscratch/rech/nou/uzn19yk/tunisian_csvs/good_final/iwslt_test.csv
- /gpfsscratch/rech/nou/uzn19yk/tunisian_csvs/good_final/taric_test.csv
skip_prep: True # Skip data preparation
use_language_modelling: True
ngram_lm_path: arpas/outdomain.arpa
# We remove utterance slonger than 10s in the train/dev/test sets as
# longer sentences certainly correspond to "open microphones".
avoid_if_longer_than: 10.0
avoid_if_shorter_than: 1.2
# Training parameters
number_of_epochs: 14
lr: 1.0
lr_wav2vec: 0.0001
sorting: ascending
auto_mix_prec: False
sample_rate: 16000
ckpt_interval_minutes: 30 # save checkpoint every N min
# With data_parallel batch_size is split into N jobs
# With DDP batch_size is multiplied by N jobs
# Must be 6 per GPU to fit 16GB of VRAM
batch_size: 10
test_batch_size: 4
dataloader_options:
batch_size: !ref <batch_size>
num_workers: 6
test_dataloader_options:
batch_size: !ref <test_batch_size>
num_workers: 6
# BPE parameters
token_type: char # ["unigram", "bpe", "char"]
character_coverage: 1.0
# Model parameters
# activation: !name:torch.nn.LeakyReLU
wav2vec_output_dim: 1024
dnn_neurons: 1024
freeze_wav2vec: False
freeze_feature_extractor: True
dropout: 0.15
warmup_steps: 500 # The wav2vec 2 model isn't updated for this amount of steps
# Outputs
output_neurons: 40 # BPE size, index(blank/eos/bos) = 0
# Decoding parameters
# Be sure that the bos and eos index match with the BPEs ones
blank_index: 0
unk_index: 1
#
# Functions and classes
#
epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <number_of_epochs>
augmentation: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
sample_rate: !ref <sample_rate>
speeds: [95, 100, 105]
enc: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <wav2vec_output_dim>]
linear1: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <dnn_neurons>
bias: True
bn1: !name:speechbrain.nnet.normalization.BatchNorm1d
activation: !new:torch.nn.LeakyReLU
drop: !new:torch.nn.Dropout
p: !ref <dropout>
linear2: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <dnn_neurons>
bias: True
bn2: !name:speechbrain.nnet.normalization.BatchNorm1d
activation2: !new:torch.nn.LeakyReLU
drop2: !new:torch.nn.Dropout
p: !ref <dropout>
linear3: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <dnn_neurons>
bias: True
bn3: !name:speechbrain.nnet.normalization.BatchNorm1d
activation3: !new:torch.nn.LeakyReLU
wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
source: wavlm-large/
output_norm: False
freeze: !ref <freeze_wav2vec>
freeze_feature_extractor: !ref <freeze_feature_extractor>
save_path: !ref <wav2vec2_folder>
#####
# Uncomment this block if you prefer to use a Fairseq pretrained model instead
# of a HuggingFace one. Here, we provide an URL that is obtained from the
# Fairseq github for the multilingual XLSR.
#
#wav2vec2_url: https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_53_56k.pt
#wav2vec2: !new:speechbrain.lobes.models.fairseq_wav2vec.FairseqWav2Vec2
# pretrained_path: !ref <wav2vec2_url>
# output_norm: True
# freeze: False
# save_path: !ref <save_folder>/wav2vec2_checkpoint/model.pt
#####
ctc_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <dnn_neurons>
n_neurons: !ref <output_neurons>
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: True
ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
blank_index: !ref <blank_index>
modules:
wav2vec2: !ref <wav2vec2>
enc: !ref <enc>
ctc_lin: !ref <ctc_lin>
model: !new:torch.nn.ModuleList
- [!ref <enc>, !ref <ctc_lin>]
model_opt_class: !name:torch.optim.Adadelta
lr: !ref <lr>
rho: 0.95
eps: 1.e-8
wav2vec_opt_class: !name:torch.optim.Adam
lr: !ref <lr_wav2vec>
lr_annealing_model: !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: !ref <lr>
improvement_threshold: 0.0025
annealing_factor: 0.8
patient: 0
lr_annealing_wav2vec: !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: !ref <lr_wav2vec>
improvement_threshold: 0.0025
annealing_factor: 0.9
patient: 0
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: !ref <save_folder>
recoverables:
wav2vec2: !ref <wav2vec2>
model: !ref <model>
scheduler_model: !ref <lr_annealing_model>
scheduler_wav2vec: !ref <lr_annealing_wav2vec>
counter: !ref <epoch_counter>
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
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