Create hyperparams.yaml

hyperparams.yaml

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+# Seed needs to be set at top of yaml, before objects with parameters are made
+seed: 2024
+__set_seed: !apply:torch.manual_seed [!ref <seed>]
+
+skip_training: True
+
+# Hparams NEEDED
+HPARAMS_NEEDED: ["log_softmax"]
+# Modules Needed
+MODULES_NEEDED: ["whisper"]
+
+output_folder: !ref output_folder_whisper
+pretrained_path: Macedonian-ASR/buki-whisper-capitalised-2.0
+output_wer_folder: !ref <output_folder>/
+save_folder: !ref <output_folder>/save
+train_log: !ref <output_folder>/train_log.txt
+
+
+# URL for the biggest Fairseq english whisper model.
+whisper_hub: openai/whisper-large-v3
+
+# Normalize inputs with the same normalization done in the paper (https://cdn.openai.com/papers/whisper.pdf). Refer to Appendix C for further information.
+normalized_transcripts: False
+restore_capitalization: False
+
+# Data files
+language: "macedonian"
+data_folder: "../../data/combined_data/speechbrain_splits"
+accented_letters: True
+
+ckpt_interval_minutes: 30 # save checkpoint every N min
+
+####################### Training Parameters ####################################
+freeze_whisper: False
+freeze_encoder: True
+number_of_epochs: 50
+weight_decay: 0.01
+lr_whisper: 1e-5
+warmup_steps: 500
+max_grad_norm: 2.0
+precision: fp16 # bf16, fp16 or fp32
+eval_precision: fp16
+sample_rate: 16000
+
+# With data_parallel batch_size is split into N jobs
+batch_size: 6
+test_batch_size: 1
+grad_accumulation_factor: 2
+
+
+# Decoding parameters
+min_decode_ratio: 0.0
+max_decode_ratio: 1.0
+test_beam_size: 8
+
+####################### Model Parameters #######################################
+train_dataloader_opts:
+    batch_size: !ref <batch_size>
+
+valid_dataloader_opts:
+    batch_size: !ref <batch_size>
+
+test_dataloader_opts:
+    batch_size: !ref <test_batch_size>
+
+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: 16000
+   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
+   concat_original: False
+   min_augmentations: 1
+   max_augmentations: 3
+   augment_prob: 0.5
+   augmentations: [
+      !ref <speed_perturb>,
+      !ref <drop_freq>,
+      !ref <drop_chunk>]
+
+
+############################## Models ##########################################
+
+
+whisper: !new:speechbrain.lobes.models.huggingface_transformers.whisper.Whisper
+    source: !ref <whisper_hub>
+    freeze: !ref <freeze_whisper>
+    freeze_encoder: !ref <freeze_encoder>
+    save_path: !ref <save_folder>/whisper_checkpoint
+    language: !ref <language>
+    task: "transcribe"
+
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+    apply_log: True
+
+nll_loss: !name:speechbrain.nnet.losses.nll_loss
+
+modules:
+    whisper: !ref <whisper>
+
+############################## Decoding & optimiser ############################
+
+whisper_opt_class: !name:torch.optim.AdamW
+    lr: !ref <lr_whisper>
+    weight_decay: !ref <weight_decay>
+
+valid_search: !new:speechbrain.decoders.seq2seq.S2SWhisperGreedySearcher
+    model: !ref <whisper>
+    min_decode_ratio: !ref <min_decode_ratio>
+    max_decode_ratio: !ref <max_decode_ratio>
+
+test_search: !new:speechbrain.decoders.seq2seq.S2SWhisperBeamSearcher
+    module: [!ref <whisper>]
+    min_decode_ratio: !ref <min_decode_ratio>
+    max_decode_ratio: !ref <max_decode_ratio>
+    beam_size: !ref <test_beam_size>
+
+lr_annealing_whisper: !new:speechbrain.nnet.schedulers.NoamScheduler
+    lr_initial: !ref <lr_whisper>
+    n_warmup_steps: !ref <warmup_steps>
+
+############################## Logging and Pretrainer ##########################
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+    checkpoints_dir: !ref <save_folder>
+    recoverables:
+        whisper: !ref <whisper>
+        scheduler_whisper: !ref <lr_annealing_whisper>
+        counter: !ref <epoch_counter>
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        whisper: !ref <whisper>
+    paths:
+        whisper: !ref <pretrained_path>/model.ckpt
+
+
+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