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train_zh-CN_with_wav2vec.yaml
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# ################################
# Model: wav2vec2 + DNN + CTC
# Augmentation: SpecAugment
# Authors: Pooneh Mousavi 2023
# ################################
# Seed needs to be set at top of yaml, before objects with parameters are made
seed: 1234
__set_seed: !apply:speechbrain.utils.seed_everything [!ref <seed>]
output_folder: !ref results/wav2vec2_ctc_zh-CN/<seed>
test_wer_file: !ref <output_folder>/wer_test.txt
save_folder: !ref <output_folder>/save
train_log: !ref <output_folder>/train_log.txt
# URL for the biggest Fairseq multilingual
wav2vec2_hub: facebook/wav2vec2-large-xlsr-53
wav2vec2_folder: !ref <save_folder>/wav2vec2_checkpoint
# Data files
data_folder: !PLACEHOLDER # 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: zh-CN # use 'it' for Italian, 'rw' for Kinyarwanda, 'en' for english
train_csv: !ref <save_folder>/train.csv
valid_csv: !ref <save_folder>/dev.csv
test_csv: !ref <save_folder>/test.csv
skip_prep: False # Skip data preparation
# 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
####################### Training Parameters ####################################
number_of_epochs: 30
lr: 1.0
lr_wav2vec: 0.0001
sorting: ascending
precision: fp32 # bf16, fp16 or fp32
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 8 per GPU to fit 32GB of VRAMM
dynamic_batching: False
batch_size: 12
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: unigram # ["unigram", "bpe", "char"]
character_coverage: 1.0
####################### Model Parameters #######################################
wav2vec_output_dim: 1024
dnn_neurons: 1024
freeze_wav2vec: False
freeze_feature_extractor: False
dropout: 0.15
warmup_steps: 500
# Outputs
output_neurons: 4652 # 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
bos_index: 1
eos_index: 2
# Decoding parameters
# Be sure that the bos and eos index match with the BPEs ones
# Decoding parameters
test_beam_search:
blank_index: !ref <blank_index>
beam_size: 100
beam_prune_logp: -12.0
token_prune_min_logp: -1.2
prune_history: True
topk: 1
alpha: 1.0
beta: 0.5
# To use n-gram LM for decoding, follow steps in README.md.
# kenlm_model_path: none
#
# Functions and classes
#
epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <number_of_epochs>
############################## Augmentations ###################################
# Speed perturbation
speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb
orig_freq: !ref <sample_rate>
speeds: [95, 100, 105]
# 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: 5
# Augmenter: Combines previously defined augmentations to perform data augmentation
wav_augment: !new:speechbrain.augment.augmenter.Augmenter
min_augmentations: 3
max_augmentations: 3
augment_prob: 1.0
augmentations: [
!ref <speed_perturb>,
!ref <drop_freq>,
!ref <drop_chunk>]
############################## Models ##########################################
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_transformers.wav2vec2.Wav2Vec2
source: !ref <wav2vec2_hub>
output_norm: True
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