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CTC-only training recipes for LibriSpeech (code from Samsung AI Cambr…
…idge) (speechbrain#2290) CTC-only pre-training of conformer and branchformer. --------- Co-authored-by: Shucong Zhang/Embedded AI /SRUK/Engineer/Samsung Electronics <s1.zhang@sruk-ccn4.eu.corp.samsungelectronics.net> Co-authored-by: Adel Moumen <adelmoumen.pro@gmail.com> Co-authored-by: Adel Moumen <88119391+Adel-Moumen@users.noreply.github.com> Co-authored-by: Parcollet Titouan <titouan.parcollet@univ-avignon.fr>
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recipes/LibriSpeech/ASR/CTC/hparams/branchformer_large.yaml
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| # ############################################################################ | ||
| # Model: E2E ASR with CTC | ||
| # Encoder: Branchformer Encoder | ||
| # Decoder: CTC beam searcher and greedy searcher | ||
| # Tokens: character | ||
| # Training: Librispeech 960h | ||
| # Authors: Titouan Parcollet, Shucong Zhang, Adel Moumen | ||
| # ############################################################################ | ||
| # Seed needs to be set at top of yaml, before objects with parameters are made | ||
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| seed: 3402 | ||
| __set_seed: !apply:torch.manual_seed [!ref <seed>] | ||
| output_folder: !ref results/branchformer_ctc/ | ||
| wer_file: !ref <output_folder>/wer.txt | ||
| save_folder: !ref <output_folder>/save | ||
| train_log: !ref <output_folder>/train_log.txt | ||
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| # Data files | ||
| data_folder: !PLACEHOLDER # e.g., /path/to/LibriSpeech | ||
| # If RIRS_NOISES dir exists in /localscratch/xxx_corpus/RIRS_NOISES | ||
| # then data_folder_rirs should be /localscratch/xxx_corpus | ||
| # otherwise the dataset will automatically be downloaded | ||
| # data_folder_rirs: !ref <data_folder> | ||
| train_splits: ["train-clean-100", "train-clean-360", "train-other-500"] | ||
| dev_splits: ["dev-clean"] | ||
| test_splits: ["dev-clean", "test-clean", "test-other"] | ||
| skip_prep: False | ||
| train_csv: !ref <output_folder>/train.csv | ||
| valid_csv: !ref <output_folder>/dev-clean.csv | ||
| test_csv: | ||
| - !ref <output_folder>/dev-clean.csv | ||
| - !ref <output_folder>/test-clean.csv | ||
| - !ref <output_folder>/test-other.csv | ||
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| ####################### Training Parameters #################################### | ||
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| number_of_epochs: 500 | ||
| batch_size: 16 # This works for 2x GPUs with 32GB | ||
| grad_accumulation_factor: 2 | ||
| max_grad_norm: 5.0 | ||
| sorting: descending #random | ||
| num_workers: 8 | ||
| loss_reduction: batchmean | ||
| valid_search_interval: 1 | ||
| avg_checkpoints: 10 # Number of checkpoints to average for evaluation | ||
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| lr_model: 0.001 | ||
| weight_decay: 0.0005 | ||
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| # Feature parameters | ||
| sample_rate: 16000 | ||
| n_fft: 512 | ||
| n_mels: 80 | ||
| win_length: 25 | ||
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| # Training parameters | ||
| # To make Transformers converge, the global bath size should be large enough. | ||
| # The global batch size is max_batch_len * n_gpus * gradient_accumulation. | ||
| # Empirically, we used 850 * 8 A40 45G GPUs * 2 or 1700 * 4 A100 80G * 2. | ||
| # Please, set your parameters accordingly. | ||
| dynamic_batching: True | ||
| max_batch_length_train: 850 | ||
| max_batch_len_val: 100 | ||
| num_bucket: 200 | ||
| shuffle: False # if true re-creates batches at each epoch shuffling examples. | ||
| max_batch_ex: 128 | ||
| batch_ordering: random | ||
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| dynamic_batch_sampler_train: | ||
| max_batch_length: !ref <max_batch_length_train> | ||
| num_buckets: !ref <num_bucket> | ||
| shuffle: !ref <shuffle> | ||
| batch_ordering: !ref <batch_ordering> | ||
| max_batch_ex: !ref <max_batch_ex> | ||
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| dynamic_batch_sampler_val: | ||
| max_batch_length: !ref <max_batch_len_val> | ||
| num_buckets: !ref <num_bucket> | ||
| shuffle: !ref <shuffle> | ||
| batch_ordering: !ref <batch_ordering> | ||
| max_batch_ex: !ref <max_batch_ex> | ||
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| # Dataloader options | ||
| train_dataloader_opts: | ||
| batch_size: !ref <batch_size> | ||
| shuffle: True | ||
| num_workers: !ref <num_workers> | ||
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| valid_dataloader_opts: | ||
| batch_size: 1 | ||
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| test_dataloader_opts: | ||
| batch_size: 1 | ||
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| ####################### Model parameters ########################### | ||
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| # Transformer | ||
| attention_type: RelPosMHAXL | ||
| d_model: 256 | ||
| nhead: 4 | ||
| csgu_linear_units: 2400 | ||
| csgu_kernel_size: 31 | ||
| num_encoder_layers: 18 | ||
| num_decoder_layers: 0 | ||
| transformer_dropout: 0.1 | ||
| activation: !name:torch.nn.GELU | ||
| output_neurons: 31 | ||
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| # BPE parameters | ||
| token_type: char # ["unigram", "bpe", "char"] | ||
| character_coverage: 1.0 | ||
| blank_index: 0 | ||
| bos_index: 1 | ||
| eos_index: 2 | ||
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| # Decoding parameters | ||
| beam_size: 100 | ||
| beam_prune_logp: -12.0 | ||
| token_prune_min_logp: -1.2 | ||
| prune_history: False | ||
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| ############################## models ################################ | ||
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| CNN: !new:speechbrain.lobes.models.convolution.ConvolutionFrontEnd | ||
| input_shape: (8, 10, 80) | ||
| num_blocks: 2 | ||
| num_layers_per_block: 1 | ||
| out_channels: (64, 32) | ||
| kernel_sizes: (3, 3) | ||
| strides: (2, 2) | ||
| residuals: (False, False) | ||
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| Transformer: !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR # yamllint disable-line rule:line-length | ||
| input_size: 640 | ||
| tgt_vocab: !ref <output_neurons> | ||
| d_model: !ref <d_model> | ||
| nhead: !ref <nhead> | ||
| num_encoder_layers: !ref <num_encoder_layers> | ||
| num_decoder_layers: !ref <num_decoder_layers> | ||
| dropout: !ref <transformer_dropout> | ||
| activation: !ref <activation> | ||
| encoder_module: branchformer | ||
| attention_type: !ref <attention_type> | ||
| normalize_before: True | ||
| causal: False | ||
| csgu_linear_units: !ref <csgu_linear_units> | ||
| kernel_size: !ref <csgu_kernel_size> | ||
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| ctc_lin: !new:speechbrain.nnet.linear.Linear | ||
| input_size: !ref <d_model> | ||
| n_neurons: !ref <output_neurons> | ||
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| normalize: !new:speechbrain.processing.features.InputNormalization | ||
| norm_type: global | ||
| update_until_epoch: 4 | ||
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| modules: | ||
| CNN: !ref <CNN> | ||
| Transformer: !ref <Transformer> | ||
| ctc_lin: !ref <ctc_lin> | ||
| normalize: !ref <normalize> | ||
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| model: !new:torch.nn.ModuleList | ||
| - [!ref <CNN>, !ref <Transformer>, !ref <ctc_lin>] | ||
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| ####################### Decoding & optimiser ########################### | ||
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| # Decoding parameters | ||
| test_beam_search: | ||
| blank_index: !ref <blank_index> | ||
| beam_size: !ref <beam_size> | ||
| beam_prune_logp: !ref <beam_prune_logp> | ||
| token_prune_min_logp: !ref <token_prune_min_logp> | ||
| prune_history: !ref <prune_history> | ||
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| ctc_cost: !name:speechbrain.nnet.losses.ctc_loss | ||
| blank_index: !ref <blank_index> | ||
| reduction: !ref <loss_reduction> | ||
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| noam_annealing: !new:speechbrain.nnet.schedulers.LinearNoamScheduler | ||
| lr_initial: !ref <lr_model> | ||
| n_warmup_steps: 7500 | ||
| n_keep_steps: 36000 | ||
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| model_opt_class: !name:torch.optim.AdamW | ||
| lr: !ref <lr_model> | ||
| betas: (0.9, 0.98) | ||
| eps: 0.000000001 | ||
| weight_decay: !ref <weight_decay> | ||
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| log_softmax: !new:torch.nn.LogSoftmax | ||
| dim: -1 | ||
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| ############################## Augmentations ################################### | ||
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| speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb | ||
| orig_freq: !ref <sample_rate> | ||
| speeds: [95, 100, 105] | ||
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| 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 | ||
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| drop_chunk: !new:speechbrain.augment.time_domain.DropChunk | ||
| drop_length_low: 1000 | ||
| drop_length_high: 2000 | ||
| drop_count_low: 1 | ||
| drop_count_high: 5 | ||
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| # Augmenter: Combines previously defined augmentations to perform data augmentation | ||
| wav_augment: !new:speechbrain.augment.augmenter.Augmenter | ||
| parallel_augment: False | ||
| concat_original: True | ||
| repeat_augment: 1 | ||
| shuffle_augmentations: False | ||
| min_augmentations: 4 | ||
| max_augmentations: 4 | ||
| augment_prob: 1.0 | ||
| augmentations: [ | ||
| !ref <speed_perturb>, | ||
| !ref <drop_freq>, | ||
| !ref <drop_chunk> | ||
| ] | ||
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| compute_features: !new:speechbrain.lobes.features.Fbank | ||
| sample_rate: !ref <sample_rate> | ||
| n_fft: !ref <n_fft> | ||
| win_length: !ref <win_length> | ||
| n_mels: !ref <n_mels> | ||
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| ############################## Logging and Pretrainer ########################## | ||
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| checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer | ||
| checkpoints_dir: !ref <save_folder> | ||
| recoverables: | ||
| model: !ref <model> | ||
| noam_scheduler: !ref <noam_annealing> | ||
| normalizer: !ref <normalize> | ||
| counter: !ref <epoch_counter> | ||
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| epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter | ||
| limit: !ref <number_of_epochs> | ||
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| train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger | ||
| save_file: !ref <train_log> | ||
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| cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats | ||
| split_tokens: True | ||
| wer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats |
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