Delete model/1234/save/CKPT+2024-05-27+00-52-30+00/train.yaml

model/1234/save/CKPT+2024-05-27+00-52-30+00/train.yaml

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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: !ref model/<seed>
-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: /path/to/data  # 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: Data/train_wavs/train.csv
-valid_csv: Data/dev_wavs/dev.csv
-test_csv: 
-    - Data/test_wavs/test.csv
-
-skip_prep: True # Skip data preparation
-
-tokenizer: !new:speechbrain.dataio.encoder.CTCTextEncoder
-
-encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
-    wav2vec2: !ref <wav2vec2>
-    enc: !ref <enc>
-    ctc_lin: !ref <ctc_lin>
-    log_softmax: !ref <log_softmax>
-
-decoding_function: !name:speechbrain.decoders.ctc_greedy_decode
-    blank_id: !ref <blank_index>
-
-use_language_modelling: True
-ngram_lm_path: languageModel.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: 12
-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>
-
-
-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: wavlm-large/
-    output_norm: False
-    freeze: !ref <freeze_wav2vec>
-    freeze_feature_extractor: !ref <freeze_feature_extractor>
-    save_path: !ref <wav2vec2_folder>
-
-
-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>
-    encoder: !ref <encoder>
-
-
-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