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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dev.csv filter=lfs diff=lfs merge=lfs -text
+save/train.txt filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text

dev.csv

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env.log

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+SpeechBrain system description
+==============================
+Python version:
+3.10.15 | packaged by conda-forge | (main, Oct 16 2024, 01:24:24) [GCC 13.3.0]
+==============================
+Installed Python packages:
+audioread==3.0.1
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+decorator==5.1.1
+filelock==3.17.0
+fsspec==2025.2.0
+huggingface-hub==0.28.1
+HyperPyYAML==1.2.2
+idna==3.10
+Jinja2==3.1.5
+joblib==1.4.2
+lazy_loader==0.4
+librosa==0.10.2.post1
+llvmlite==0.44.0
+MarkupSafe==3.0.2
+mpmath==1.3.0
+msgpack==1.1.0
+networkx==3.4.2
+numba==0.61.0
+numpy==2.1.3
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-runtime-cu12==12.4.127
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cusparselt-cu12==0.6.2
+nvidia-ml-py==12.570.86
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.4.127
+nvitop==1.4.2
+packaging==24.2
+platformdirs==4.3.6
+pooch==1.8.2
+psutil==6.1.1
+pycparser==2.22
+PyYAML==6.0.2
+requests==2.32.3
+ruamel.yaml==0.18.10
+ruamel.yaml.clib==0.2.12
+scikit-learn==1.6.1
+scipy==1.15.1
+sentencepiece==0.2.0
+soundfile==0.13.1
+soxr==0.5.0.post1
+speechbrain==1.0.2
+sympy==1.13.1
+threadpoolctl==3.5.0
+torch==2.6.0
+torchaudio==2.6.0
+tqdm==4.67.1
+triton==3.2.0
+typing_extensions==4.12.2
+urllib3==2.3.0
+==============================
+Git revision:
+948fa2be4
+==============================
+CUDA version:
+12.4

hyperparams.yaml

@@ -0,0 +1,276 @@
+# Generated 2025-02-12 from:
+# /home/hamidovaslon1/speechbrain/recipes/CommonVoice/ASR/transformer/hparams/staging_5.yaml
+# yamllint disable
+seed: 1101
+__set_seed: !apply:speechbrain.utils.seed_everything [1101]
+output_folder: results/conformer_en/1101
+output_wer_folder: results/conformer_en/1101/
+save_folder: results/conformer_en/1101/save
+train_log: results/conformer_en/1101/train_log.txt
+
+# Data files
+data_folder: /home/hamidovaslon1/speechbrain/recipes/CommonVoice/ASR/uz  # e.g, /localscratch/cv-corpus-5.1-2020-06-22/fr
+train_tsv_file: /home/hamidovaslon1/speechbrain/recipes/CommonVoice/ASR/uz/train.tsv # Standard CommonVoice .tsv files
+dev_tsv_file: /home/hamidovaslon1/speechbrain/recipes/CommonVoice/ASR/uz/dev.tsv # Standard CommonVoice .tsv files
+test_tsv_file: /home/hamidovaslon1/speechbrain/recipes/CommonVoice/ASR/uz/test.tsv # Standard CommonVoice .tsv files
+accented_letters: false
+language: uz # use 'it' for Italian, 'rw' for Kinyarwanda, 'en' for english
+train_csv: results/conformer_en/1101/train.csv
+valid_csv: results/conformer_en/1101/dev.csv
+test_csv: results/conformer_en/1101/test.csv
+skip_prep: false # Skip data preparation
+convert_to_wav: false # Switch this to True to convert all mp3 files to wav.
+
+# We remove utterance slonger than 10s in the train/dev/test sets as
+# longer sentences certainly correspond to "open microphones".
+avoid_if_longer_than: 50
+
+# THIS IS TERRIBLE BUT WE HAVE NO CHOICE.
+# Some version of the CV dataset may contain one or two files of more than
+# 2 min in the validation and or test. This is an error by design of the dataset
+# as these files contain 90% of silence. We exclude them.
+avoid_if_longer_than_val_test: 100.0
+
+ckpt_interval_minutes: 15 # save checkpoint every N min
+
+####################### Training Parameters ####################################
+number_of_epochs: 50
+optimizer_step_limit: 30000
+batch_size: 200 # This works with a 32GB GPU ! (bs * nb_gpu * accum) > 128 !
+ctc_weight: 0.3
+grad_accumulation_factor: 3
+loss_reduction: batchmean
+sorting: random
+num_workers: 4
+precision: fp16 # bf16, fp16 or fp32
+
+# stages related parameters
+lr_adam: 0.0008
+weight_decay: 0.01
+warmup_steps: 1000
+augment_warmup: 6000
+
+# BPE parameters
+token_type: bpe   # ["unigram", "bpe", "char"]
+character_coverage: 1.0
+
+# Feature parameters
+sample_rate: 16000
+n_fft: 400
+n_mels: 80
+
+# This setup works well for A100 80GB GPU, adapts it to your needs.
+# Or turn it off (but training speed will decrease)
+dynamic_batching: true
+max_batch_length_train: 300
+max_batch_length_val: 100 # we reduce it as the beam is much wider (VRAM)
+num_bucket: 200
+shuffle: true # if true re-creates batches at each epoch shuffling examples.
+batch_ordering: random
+max_batch_ex: 256
+
+dynamic_batch_sampler_train:
+  max_batch_length: 300
+  num_buckets: 200
+  shuffle: true
+  batch_ordering: random
+  max_batch_ex: 256
+
+dynamic_batch_sampler_valid:
+  max_batch_length: 100
+  num_buckets: 200
+  shuffle: true
+  batch_ordering: random
+  max_batch_ex: 256
+
+# Dataloader options
+train_dataloader_opts:
+  batch_size: 200
+  shuffle: true
+  num_workers: 4
+
+valid_dataloader_opts:
+  batch_size: 200
+
+test_dataloader_opts:
+  batch_size: 200
+
+
+####################### Model Parameters ###########################
+# Transformer
+d_model: 384
+nhead: 8
+num_encoder_layers: 8
+num_decoder_layers: 4
+d_ffn: 1536
+transformer_dropout: 0.1
+activation: &id001 !name:torch.nn.GELU
+output_neurons: 4000
+
+# Outputs
+blank_index: 0
+label_smoothing: 0.1
+pad_index: 0
+bos_index: 1
+eos_index: 2
+
+# Decoding parameters
+min_decode_ratio: 0.0
+max_decode_ratio: 1.0
+valid_search_interval: 1
+valid_beam_size: 1 # We do greedy here so it's faster to decode ...
+test_beam_size: 10
+ctc_weight_decode: 0.3
+scorer_beam_scale: 0.3
+
+############################## models ################################
+
+CNN: &id002 !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)
+
+Transformer: &id003 !new:speechbrain.lobes.models.transformer.TransformerASR.TransformerASR
+                                                                                     # yamllint disable-line rule:line-length
+  input_size: 640
+  tgt_vocab: 4000
+  d_model: 384
+  nhead: 8
+  num_encoder_layers: 8
+  num_decoder_layers: 4
+  d_ffn: 1536
+  dropout: 0.1
+  conformer_activation: *id001
+  activation: *id001
+  encoder_module: conformer
+  attention_type: RelPosMHAXL
+  normalize_before: true
+  causal: false
+
+ctc_lin: &id005 !new:speechbrain.nnet.linear.Linear
+
+  input_size: 384
+  n_neurons: 4000
+
+seq_lin: &id004 !new:speechbrain.nnet.linear.Linear
+  input_size: 384
+  n_neurons: 4000
+
+modules:
+  CNN: *id002
+  Transformer: *id003
+  seq_lin: *id004
+  ctc_lin: *id005
+model: &id008 !new:torch.nn.ModuleList
+- [*id002, *id003, *id004, *id005]
+Adam: !name:torch.optim.AdamW
+  lr: 0.0008
+  weight_decay: 0.01
+
+# Scorer
+ctc_scorer: &id006 !new:speechbrain.decoders.scorer.CTCScorer
+  eos_index: 2
+  blank_index: 0
+  ctc_fc: *id005
+scorer: &id007 !new:speechbrain.decoders.scorer.ScorerBuilder
+
+  full_scorers: [*id006]
+  weights:
+    ctc: 0.3
+  scorer_beam_scale: 0.3
+
+valid_search: !new:speechbrain.decoders.S2STransformerBeamSearcher
+  modules: [*id003, *id004]
+  bos_index: 1
+  eos_index: 2
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 1
+  using_eos_threshold: false
+  length_normalization: true
+  scorer: *id007
+test_search: !new:speechbrain.decoders.S2STransformerBeamSearcher
+  modules: [*id003, *id004]
+  bos_index: 1
+  eos_index: 2
+  min_decode_ratio: 0.0
+  max_decode_ratio: 1.0
+  beam_size: 10
+  temperature: 1.15
+  using_eos_threshold: true
+  scorer: *id007
+log_softmax: !new:torch.nn.LogSoftmax
+  dim: -1
+
+ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
+  blank_index: 0
+  reduction: batchmean
+
+seq_cost: !name:speechbrain.nnet.losses.kldiv_loss
+  label_smoothing: 0.1
+  reduction: batchmean
+
+noam_annealing: &id009 !new:speechbrain.nnet.schedulers.NoamScheduler
+  lr_initial: 0.0008
+  n_warmup_steps: 1000
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/conformer_en/1101/save
+  recoverables:
+    model: *id008
+    noam_scheduler: *id009
+    normalizer: &id011 !new:speechbrain.processing.features.InputNormalization
+      norm_type: global
+      update_until_epoch: 4
+
+############################## Augmentations ###################################
+
+# Time Drop
+    counter: &id010 !new:speechbrain.utils.epoch_loop.EpochCounter
+
+      limit: 50
+
+epoch_counter: *id010
+normalize: *id011
+time_drop: &id012 !new:speechbrain.augment.freq_domain.SpectrogramDrop
+  drop_length_low: 15
+  drop_length_high: 25
+  drop_count_low: 3
+  drop_count_high: 3
+  replace: zeros
+  dim: 1
+
+# Frequency Drop
+freq_drop: &id013 !new:speechbrain.augment.freq_domain.SpectrogramDrop
+  drop_length_low: 25
+  drop_length_high: 35
+  drop_count_low: 2
+  drop_count_high: 2
+  replace: zeros
+  dim: 2
+
+# Time warp
+time_warp: &id014 !new:speechbrain.augment.freq_domain.Warping
+
+fea_augment: !new:speechbrain.augment.augmenter.Augmenter
+  min_augmentations: 3
+  max_augmentations: 3
+  augment_prob: 1.0
+  augmentations: [*id012, *id013, *id014]
+
+compute_features: !new:speechbrain.lobes.features.Fbank
+  sample_rate: 16000
+  n_fft: 400
+  n_mels: 80
+
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/conformer_en/1101/train_log.txt
+
+error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
+acc_computer: !name:speechbrain.utils.Accuracy.AccuracyStats
+cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
+  split_tokens: true

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save/CKPT+2025-02-12+07-39-03+00/CKPT.yaml

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+# yamllint disable
+ACC: 0.9379125854089191
+end-of-epoch: true
+epoch: 48
+unixtime: 1739345943.714444

save/CKPT+2025-02-12+07-39-03+00/brain.ckpt

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save/CKPT+2025-02-12+07-39-03+00/counter.ckpt

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+size 2

save/CKPT+2025-02-12+07-39-03+00/dataloader-TRAIN.ckpt

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save/CKPT+2025-02-12+07-39-03+00/model.ckpt

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save/CKPT+2025-02-12+07-39-03+00/noam_scheduler.ckpt

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save/CKPT+2025-02-12+07-39-03+00/normalizer.ckpt

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save/CKPT+2025-02-12+07-39-03+00/optimizer.ckpt

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save/CKPT+2025-02-12+07-39-03+00/scaler.ckpt

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save/CKPT+2025-02-12+09-16-43+00/CKPT.yaml

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+# yamllint disable
+ACC: 0.9376027931824437
+end-of-epoch: true
+epoch: 50
+unixtime: 1739351803.0805285

save/CKPT+2025-02-12+09-16-43+00/brain.ckpt

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save/CKPT+2025-02-12+09-16-43+00/counter.ckpt

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save/CKPT+2025-02-12+09-16-43+00/dataloader-TRAIN.ckpt

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save/CKPT+2025-02-12+09-16-43+00/model.ckpt

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save/CKPT+2025-02-12+09-16-43+00/noam_scheduler.ckpt

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save/CKPT+2025-02-12+09-16-43+00/normalizer.ckpt

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save/CKPT+2025-02-12+09-16-43+00/optimizer.ckpt

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save/CKPT+2025-02-12+09-16-43+00/scaler.ckpt

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+version https://git-lfs.github.com/spec/v1
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save/train.txt

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train.csv

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train.py

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+#!/usr/bin/env python3
+"""Recipe for training a Transformer ASR system with CommonVoice
+The system employs an encoder, a decoder, and an attention mechanism
+between them. Decoding is performed with (CTC/Att joint) beamsearch.
+
+To run this recipe, do the following:
+> python train.py hparams/conformer_large.yaml
+
+
+Authors
+ * Titouan Parcollet 2021, 2024
+ * Jianyuan Zhong 2020
+ * Pooneh Mousavi 2023
+"""
+import os
+import sys
+
+import torch
+import torchaudio
+from hyperpyyaml import load_hyperpyyaml
+
+import speechbrain as sb
+from speechbrain.tokenizers.SentencePiece import SentencePiece
+from speechbrain.utils.data_utils import undo_padding
+from speechbrain.utils.distributed import if_main_process, run_on_main
+from speechbrain.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+# Define training procedure
+class ASR(sb.core.Brain):
+    def compute_forward(self, batch, stage):
+        """Forward computations from the waveform batches to the output probabilities."""
+        batch = batch.to(self.device)
+        wavs, wav_lens = batch.sig
+        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
+        tokens_bos, _ = batch.tokens_bos
+
+        # Add waveform augmentation if specified.
+        if (
+            stage == sb.Stage.TRAIN
+            and hasattr(self.hparams, "wav_augment")
+            and self.optimizer_step > self.hparams.augment_warmup
+        ):
+            wavs, wav_lens = self.hparams.wav_augment(wavs, wav_lens)
+            tokens_bos = self.hparams.wav_augment.replicate_labels(tokens_bos)
+
+        # compute features
+        feats = self.hparams.compute_features(wavs)
+        current_epoch = self.hparams.epoch_counter.current
+        feats = self.hparams.normalize(feats, wav_lens, epoch=current_epoch)
+
+        # Add feature augmentation if specified.
+        if (
+            stage == sb.Stage.TRAIN
+            and hasattr(self.hparams, "fea_augment")
+            and self.optimizer_step > self.hparams.augment_warmup
+        ):
+            feats, fea_lens = self.hparams.fea_augment(feats, wav_lens)
+            tokens_bos = self.hparams.fea_augment.replicate_labels(tokens_bos)
+
+        # forward modules
+        src = self.modules.CNN(feats)
+        enc_out, pred = self.modules.Transformer(
+            src, tokens_bos, wav_lens, pad_idx=self.hparams.pad_index
+        )
+
+        # output layer for ctc log-probabilities
+        logits = self.modules.ctc_lin(enc_out)
+        p_ctc = self.hparams.log_softmax(logits)
+
+        # output layer for seq2seq log-probabilities
+        pred = self.modules.seq_lin(pred)
+        p_seq = self.hparams.log_softmax(pred)
+
+        # Compute outputs
+        hyps = None
+        current_epoch = self.hparams.epoch_counter.current
+        is_valid_search = (
+            stage == sb.Stage.VALID
+            and current_epoch % self.hparams.valid_search_interval == 0
+        )
+        is_test_search = stage == sb.Stage.TEST
+
+        if is_valid_search:
+            hyps, _, _, _ = self.hparams.valid_search(
+                enc_out.detach(), wav_lens
+            )
+
+        elif is_test_search:
+            hyps, _, _, _ = self.hparams.test_search(enc_out.detach(), wav_lens)
+
+        return p_ctc, p_seq, wav_lens, hyps
+
+    def compute_objectives(self, predictions, batch, stage):
+        """Computes the loss (CTC+NLL) given predictions and targets."""
+
+        (p_ctc, p_seq, wav_lens, predicted_tokens) = predictions
+
+        ids = batch.id
+        tokens_eos, tokens_eos_lens = batch.tokens_eos
+        tokens, tokens_lens = batch.tokens
+
+        # Augment Labels
+        if stage == sb.Stage.TRAIN:
+            # Labels must be extended if parallel augmentation or concatenated
+            # augmentation was performed on the input (increasing the time dimension)
+            if (
+                hasattr(self.hparams, "wav_augment")
+                and self.optimizer_step > self.hparams.augment_warmup
+            ):
+                (
+                    tokens,
+                    tokens_lens,
+                    tokens_eos,
+                    tokens_eos_lens,
+                ) = self.hparams.wav_augment.replicate_multiple_labels(
+                    tokens, tokens_lens, tokens_eos, tokens_eos_lens
+                )
+            if (
+                hasattr(self.hparams, "fea_augment")
+                and self.optimizer_step > self.hparams.augment_warmup
+            ):
+                (
+                    tokens,
+                    tokens_lens,
+                    tokens_eos,
+                    tokens_eos_lens,
+                ) = self.hparams.fea_augment.replicate_multiple_labels(
+                    tokens, tokens_lens, tokens_eos, tokens_eos_lens
+                )
+
+        loss_seq = self.hparams.seq_cost(
+            p_seq, tokens_eos, length=tokens_eos_lens
+        )
+        loss_ctc = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
+        loss = (
+            self.hparams.ctc_weight * loss_ctc
+            + (1 - self.hparams.ctc_weight) * loss_seq
+        )
+
+        if stage != sb.Stage.TRAIN:
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+            if current_epoch % valid_search_interval == 0 or (
+                stage == sb.Stage.TEST
+            ):
+                # Decode token terms to words
+                predicted_words = self.tokenizer(
+                    predicted_tokens, task="decode_from_list"
+                )
+
+                # Convert indices to words
+                target_words = undo_padding(tokens, tokens_lens)
+                target_words = self.tokenizer(
+                    target_words, task="decode_from_list"
+                )
+                self.wer_metric.append(ids, predicted_words, target_words)
+                self.cer_metric.append(ids, predicted_words, target_words)
+
+            # compute the accuracy of the one-step-forward prediction
+            self.acc_metric.append(p_seq, tokens_eos, tokens_eos_lens)
+        return loss
+
+    def on_fit_batch_end(self, batch, outputs, loss, should_step):
+        """At the end of the optimizer step, apply noam annealing."""
+        if should_step:
+            self.hparams.noam_annealing(self.optimizer)
+
+    def on_stage_start(self, stage, epoch):
+        """Gets called at the beginning of each epoch"""
+        if stage != sb.Stage.TRAIN:
+            self.acc_metric = self.hparams.acc_computer()
+            self.cer_metric = self.hparams.cer_computer()
+            self.wer_metric = self.hparams.error_rate_computer()
+
+    def on_stage_end(self, stage, stage_loss, epoch):
+        """Gets called at the end of a epoch."""
+        # Compute/store important stats
+        stage_stats = {"loss": stage_loss}
+        if stage == sb.Stage.TRAIN:
+            self.train_stats = stage_stats
+        else:
+            stage_stats["ACC"] = self.acc_metric.summarize()
+            current_epoch = self.hparams.epoch_counter.current
+            valid_search_interval = self.hparams.valid_search_interval
+            if (
+                current_epoch % valid_search_interval == 0
+                or stage == sb.Stage.TEST
+            ):
+                stage_stats["WER"] = self.wer_metric.summarize("error_rate")
+                stage_stats["CER"] = self.cer_metric.summarize("error_rate")
+
+        # log stats and save checkpoint at end-of-epoch
+        if stage == sb.Stage.VALID:
+            # report different epoch stages according current stage
+            current_epoch = self.hparams.epoch_counter.current
+            lr = self.hparams.noam_annealing.current_lr
+            steps = self.hparams.noam_annealing.n_steps
+
+            epoch_stats = {
+                "epoch": epoch,
+                "lr": lr,
+                "steps": steps,
+            }
+            self.hparams.train_logger.log_stats(
+                stats_meta=epoch_stats,
+                train_stats=self.train_stats,
+                valid_stats=stage_stats,
+            )
+            self.checkpointer.save_and_keep_only(
+                meta={"ACC": stage_stats["ACC"], "epoch": epoch},
+                max_keys=["ACC"],
+            )
+
+        elif stage == sb.Stage.TEST:
+            self.hparams.train_logger.log_stats(
+                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
+                test_stats=stage_stats,
+            )
+            if if_main_process():
+                with open(
+                    self.hparams.test_wer_file, "w", encoding="utf-8"
+                ) as w:
+                    self.wer_metric.write_stats(w)
+
+
+# Define custom data procedure
+def dataio_prepare(hparams, tokenizer):
+    """This function prepares the datasets to be used in the brain class.
+    It also defines the data processing pipeline through user-defined functions.
+    """
+
+    # 1. Define datasets
+    data_folder = hparams["data_folder"]
+
+    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
+        csv_path=hparams["train_csv"],
+        replacements={"data_root": data_folder},
+    )
+
+    if hparams["sorting"] == "ascending":
+        # we sort training data to speed up training and get better results.
+        train_data = train_data.filtered_sorted(
+            sort_key="duration",
+            key_max_value={"duration": hparams["avoid_if_longer_than"]},
+        )
+        # when sorting do not shuffle in dataloader ! otherwise is pointless
+        hparams["train_dataloader_opts"]["shuffle"] = False
+
+    elif hparams["sorting"] == "descending":
+        train_data = train_data.filtered_sorted(
+            sort_key="duration",
+            reverse=True,
+            key_max_value={"duration": hparams["avoid_if_longer_than"]},
+        )
+        # when sorting do not shuffle in dataloader ! otherwise is pointless
+        hparams["train_dataloader_opts"]["shuffle"] = False
+
+    elif hparams["sorting"] == "random":
+        pass
+
+    else:
+        raise NotImplementedError(
+            "sorting must be random, ascending or descending"
+        )
+
+    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
+        csv_path=hparams["valid_csv"],
+        replacements={"data_root": data_folder},
+    )
+    # We also sort the validation data so it is faster to validate
+    valid_data = valid_data.filtered_sorted(
+        sort_key="duration",
+        key_max_value={"duration": hparams["avoid_if_longer_than_val_test"]},
+    )
+
+    test_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
+        csv_path=hparams["test_csv"],
+        replacements={"data_root": data_folder},
+    )
+
+    # We also sort the validation data so it is faster to validate
+    test_data = test_data.filtered_sorted(
+        sort_key="duration",
+        key_max_value={"duration": hparams["avoid_if_longer_than_val_test"]},
+    )
+
+    datasets = [train_data, valid_data, test_data]
+
+    # 2. Define audio pipeline:
+    @sb.utils.data_pipeline.takes("wav")
+    @sb.utils.data_pipeline.provides("sig")
+    def audio_pipeline(wav):
+        info = torchaudio.info(wav)
+        sig = sb.dataio.dataio.read_audio(wav)
+        resampled = torchaudio.transforms.Resample(
+            info.sample_rate,
+            hparams["sample_rate"],
+        )(sig)
+        return resampled
+
+    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
+
+    # 3. Define text pipeline:
+    @sb.utils.data_pipeline.takes("wrd")
+    @sb.utils.data_pipeline.provides(
+        "wrd", "tokens_list", "tokens_bos", "tokens_eos", "tokens"
+    )
+    def text_pipeline(wrd):
+        yield wrd
+        tokens_list = tokenizer.sp.encode_as_ids(wrd)
+        yield tokens_list
+        tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list))
+        yield tokens_bos
+        tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]])
+        yield tokens_eos
+        tokens = torch.LongTensor(tokens_list)
+        yield tokens
+
+    sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
+
+    # 4. Set output:
+    sb.dataio.dataset.set_output_keys(
+        datasets,
+        ["id", "sig", "tokens_bos", "tokens_eos", "tokens"],
+    )
+
+    # 5. If Dynamic Batching is used, we instantiate the needed samplers.
+    train_batch_sampler = None
+    valid_batch_sampler = None
+    if hparams["dynamic_batching"]:
+        from speechbrain.dataio.sampler import DynamicBatchSampler  # noqa
+
+        dynamic_hparams_train = hparams["dynamic_batch_sampler_train"]
+        dynamic_hparams_valid = hparams["dynamic_batch_sampler_valid"]
+
+        train_batch_sampler = DynamicBatchSampler(
+            train_data,
+            length_func=lambda x: x["duration"],
+            **dynamic_hparams_train,
+        )
+        valid_batch_sampler = DynamicBatchSampler(
+            valid_data,
+            length_func=lambda x: x["duration"],
+            **dynamic_hparams_valid,
+        )
+
+    return (
+        train_data,
+        valid_data,
+        test_data,
+        train_batch_sampler,
+        valid_batch_sampler,
+    )
+
+
+if __name__ == "__main__":
+    # CLI:
+    hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
+    with open(hparams_file, encoding="utf-8") as fin:
+        hparams = load_hyperpyyaml(fin, overrides)
+
+    # create ddp_group with the right communication protocol
+    sb.utils.distributed.ddp_init_group(run_opts)
+
+    # Dataset preparation (parsing CommonVoice)
+    from common_voice_prepare import prepare_common_voice  # noqa
+
+    # Create experiment directory
+    sb.create_experiment_directory(
+        experiment_directory=hparams["output_folder"],
+        hyperparams_to_save=hparams_file,
+        overrides=overrides,
+    )
+
+    # Due to DDP, we do the preparation ONLY on the main python process
+    run_on_main(
+        prepare_common_voice,
+        kwargs={
+            "data_folder": hparams["data_folder"],
+            "save_folder": hparams["output_folder"],
+            "train_tsv_file": hparams["train_tsv_file"],
+            "dev_tsv_file": hparams["dev_tsv_file"],
+            "test_tsv_file": hparams["test_tsv_file"],
+            "accented_letters": hparams["accented_letters"],
+            "language": hparams["language"],
+            "skip_prep": hparams["skip_prep"],
+            "convert_to_wav": hparams["convert_to_wav"],
+        },
+    )
+
+    # Defining tokenizer and loading it
+    tokenizer = SentencePiece(
+        model_dir=hparams["save_folder"],
+        vocab_size=hparams["output_neurons"],
+        annotation_train=hparams["train_csv"],
+        annotation_read="wrd",
+        model_type=hparams["token_type"],
+        character_coverage=hparams["character_coverage"],
+        bos_id=hparams["bos_index"],
+        eos_id=hparams["eos_index"],
+    )
+
+    # here we create the datasets objects as well as tokenization and encoding
+    (
+        train_data,
+        valid_data,
+        test_data,
+        train_bsampler,
+        valid_bsampler,
+    ) = dataio_prepare(hparams, tokenizer)
+
+    # Trainer initialization
+    asr_brain = ASR(
+        modules=hparams["modules"],
+        opt_class=hparams["Adam"],
+        hparams=hparams,
+        run_opts=run_opts,
+        checkpointer=hparams["checkpointer"],
+    )
+
+    # adding objects to trainer:
+    asr_brain.tokenizer = tokenizer
+
+    # Manage dynamic batching
+    train_dataloader_opts = hparams["train_dataloader_opts"]
+    valid_dataloader_opts = hparams["valid_dataloader_opts"]
+    if train_bsampler is not None:
+        collate_fn = None
+        if "collate_fn" in train_dataloader_opts:
+            collate_fn = train_dataloader_opts["collate_fn"]
+
+        train_dataloader_opts = {
+            "batch_sampler": train_bsampler,
+            "num_workers": hparams["num_workers"],
+        }
+
+        if collate_fn is not None:
+            train_dataloader_opts["collate_fn"] = collate_fn
+
+    if valid_bsampler is not None:
+        collate_fn = None
+        if "collate_fn" in valid_dataloader_opts:
+            collate_fn = valid_dataloader_opts["collate_fn"]
+
+        valid_dataloader_opts = {"batch_sampler": valid_bsampler}
+
+        if collate_fn is not None:
+            valid_dataloader_opts["collate_fn"] = collate_fn
+
+    # Training
+    asr_brain.fit(
+        asr_brain.hparams.epoch_counter,
+        train_data,
+        valid_data,
+        train_loader_kwargs=train_dataloader_opts,
+        valid_loader_kwargs=valid_dataloader_opts,
+    )
+
+    # Testing
+    os.makedirs(hparams["output_wer_folder"], exist_ok=True)
+
+    asr_brain.hparams.test_wer_file = os.path.join(
+        hparams["output_wer_folder"], "wer_valid.txt"
+    )
+    asr_brain.evaluate(
+        valid_data,
+        max_key="ACC",
+        test_loader_kwargs=hparams["test_dataloader_opts"],
+    )
+
+    asr_brain.hparams.test_wer_file = os.path.join(
+        hparams["output_wer_folder"], "wer_test.txt"
+    )
+    asr_brain.evaluate(
+        test_data,
+        max_key="ACC",
+        test_loader_kwargs=hparams["test_dataloader_opts"],
+    )

train_log.txt

@@ -0,0 +1,50 @@
+epoch: 1, lr: 4.94e-04, steps: 618 - train loss: 93.40 - valid loss: 73.46, valid ACC: 2.66e-01, valid WER: 1.48e+02, valid CER: 77.76
+epoch: 2, lr: 7.20e-04, steps: 1236 - train loss: 67.77 - valid loss: 54.63, valid ACC: 4.16e-01, valid WER: 1.07e+02, valid CER: 65.90
+epoch: 3, lr: 5.88e-04, steps: 1854 - train loss: 32.65 - valid loss: 18.09, valid ACC: 8.09e-01, valid WER: 34.41, valid CER: 11.58
+epoch: 4, lr: 5.09e-04, steps: 2472 - train loss: 15.31 - valid loss: 12.28, valid ACC: 8.69e-01, valid WER: 24.02, valid CER: 6.89
+epoch: 5, lr: 4.55e-04, steps: 3090 - train loss: 11.17 - valid loss: 10.13, valid ACC: 8.91e-01, valid WER: 19.82, valid CER: 5.35
+epoch: 6, lr: 4.16e-04, steps: 3708 - train loss: 9.08 - valid loss: 8.75, valid ACC: 9.04e-01, valid WER: 17.69, valid CER: 4.62
+epoch: 7, lr: 3.85e-04, steps: 4326 - train loss: 7.60 - valid loss: 8.02, valid ACC: 9.11e-01, valid WER: 16.39, valid CER: 4.18
+epoch: 8, lr: 3.60e-04, steps: 4944 - train loss: 6.63 - valid loss: 7.57, valid ACC: 9.15e-01, valid WER: 15.58, valid CER: 3.91
+epoch: 9, lr: 3.39e-04, steps: 5562 - train loss: 5.84 - valid loss: 7.26, valid ACC: 9.18e-01, valid WER: 15.03, valid CER: 3.75
+epoch: 10, lr: 3.22e-04, steps: 6180 - train loss: 12.07 - valid loss: 7.65, valid ACC: 9.14e-01, valid WER: 15.78, valid CER: 4.08
+epoch: 11, lr: 3.07e-04, steps: 6798 - train loss: 21.74 - valid loss: 7.37, valid ACC: 9.18e-01, valid WER: 15.02, valid CER: 3.81
+epoch: 12, lr: 2.94e-04, steps: 7416 - train loss: 19.26 - valid loss: 7.25, valid ACC: 9.20e-01, valid WER: 14.69, valid CER: 3.66
+epoch: 13, lr: 2.82e-04, steps: 8034 - train loss: 17.96 - valid loss: 7.02, valid ACC: 9.23e-01, valid WER: 14.09, valid CER: 3.48
+epoch: 14, lr: 2.72e-04, steps: 8652 - train loss: 17.09 - valid loss: 6.90, valid ACC: 9.23e-01, valid WER: 14.04, valid CER: 3.43
+epoch: 15, lr: 2.63e-04, steps: 9270 - train loss: 16.46 - valid loss: 6.77, valid ACC: 9.25e-01, valid WER: 13.64, valid CER: 3.30
+epoch: 16, lr: 2.54e-04, steps: 9888 - train loss: 15.70 - valid loss: 6.66, valid ACC: 9.26e-01, valid WER: 13.35, valid CER: 3.23
+epoch: 17, lr: 2.47e-04, steps: 10506 - train loss: 15.24 - valid loss: 6.54, valid ACC: 9.28e-01, valid WER: 13.08, valid CER: 3.16
+epoch: 18, lr: 2.40e-04, steps: 11124 - train loss: 14.75 - valid loss: 6.47, valid ACC: 9.28e-01, valid WER: 13.04, valid CER: 3.11
+epoch: 19, lr: 2.33e-04, steps: 11742 - train loss: 14.33 - valid loss: 6.35, valid ACC: 9.29e-01, valid WER: 12.76, valid CER: 3.03
+epoch: 20, lr: 2.28e-04, steps: 12360 - train loss: 14.02 - valid loss: 6.30, valid ACC: 9.30e-01, valid WER: 12.61, valid CER: 2.98
+epoch: 21, lr: 2.22e-04, steps: 12978 - train loss: 13.60 - valid loss: 6.21, valid ACC: 9.31e-01, valid WER: 12.45, valid CER: 2.94
+epoch: 22, lr: 2.17e-04, steps: 13596 - train loss: 13.32 - valid loss: 6.23, valid ACC: 9.31e-01, valid WER: 12.42, valid CER: 2.93
+epoch: 23, lr: 2.12e-04, steps: 14214 - train loss: 13.06 - valid loss: 6.10, valid ACC: 9.32e-01, valid WER: 12.23, valid CER: 2.86
+epoch: 24, lr: 2.08e-04, steps: 14832 - train loss: 12.81 - valid loss: 6.05, valid ACC: 9.33e-01, valid WER: 12.07, valid CER: 2.83
+epoch: 25, lr: 2.04e-04, steps: 15450 - train loss: 12.58 - valid loss: 6.00, valid ACC: 9.33e-01, valid WER: 12.03, valid CER: 2.80
+epoch: 26, lr: 2.00e-04, steps: 16068 - train loss: 12.31 - valid loss: 5.94, valid ACC: 9.33e-01, valid WER: 11.87, valid CER: 2.75
+epoch: 27, lr: 1.96e-04, steps: 16686 - train loss: 12.11 - valid loss: 5.93, valid ACC: 9.33e-01, valid WER: 11.86, valid CER: 2.74
+epoch: 28, lr: 1.92e-04, steps: 17304 - train loss: 11.91 - valid loss: 5.92, valid ACC: 9.34e-01, valid WER: 11.89, valid CER: 2.76
+epoch: 29, lr: 1.89e-04, steps: 17922 - train loss: 11.70 - valid loss: 5.84, valid ACC: 9.34e-01, valid WER: 11.66, valid CER: 2.70
+epoch: 30, lr: 1.86e-04, steps: 18540 - train loss: 11.52 - valid loss: 5.83, valid ACC: 9.35e-01, valid WER: 11.60, valid CER: 2.69
+epoch: 31, lr: 1.83e-04, steps: 19158 - train loss: 11.32 - valid loss: 5.82, valid ACC: 9.34e-01, valid WER: 11.62, valid CER: 2.69
+epoch: 32, lr: 1.80e-04, steps: 19776 - train loss: 11.22 - valid loss: 5.77, valid ACC: 9.35e-01, valid WER: 11.46, valid CER: 2.63
+epoch: 33, lr: 1.77e-04, steps: 20394 - train loss: 11.06 - valid loss: 5.73, valid ACC: 9.35e-01, valid WER: 11.53, valid CER: 2.65
+epoch: 34, lr: 1.75e-04, steps: 21012 - train loss: 10.95 - valid loss: 5.74, valid ACC: 9.36e-01, valid WER: 11.36, valid CER: 2.61
+epoch: 35, lr: 1.72e-04, steps: 21630 - train loss: 10.79 - valid loss: 5.72, valid ACC: 9.35e-01, valid WER: 11.38, valid CER: 2.59
+epoch: 36, lr: 1.70e-04, steps: 22248 - train loss: 10.66 - valid loss: 5.69, valid ACC: 9.36e-01, valid WER: 11.39, valid CER: 2.60
+epoch: 37, lr: 1.67e-04, steps: 22866 - train loss: 10.60 - valid loss: 5.67, valid ACC: 9.36e-01, valid WER: 11.29, valid CER: 2.58
+epoch: 38, lr: 1.65e-04, steps: 23484 - train loss: 10.46 - valid loss: 5.67, valid ACC: 9.36e-01, valid WER: 11.20, valid CER: 2.57
+epoch: 39, lr: 1.63e-04, steps: 24102 - train loss: 10.29 - valid loss: 5.63, valid ACC: 9.37e-01, valid WER: 11.09, valid CER: 2.53
+epoch: 40, lr: 1.61e-04, steps: 24720 - train loss: 10.14 - valid loss: 5.61, valid ACC: 9.37e-01, valid WER: 11.12, valid CER: 2.52
+epoch: 41, lr: 1.59e-04, steps: 25338 - train loss: 10.05 - valid loss: 5.61, valid ACC: 9.37e-01, valid WER: 11.09, valid CER: 2.52
+epoch: 42, lr: 1.57e-04, steps: 25956 - train loss: 9.92 - valid loss: 5.59, valid ACC: 9.37e-01, valid WER: 11.09, valid CER: 2.52
+epoch: 43, lr: 1.55e-04, steps: 26574 - train loss: 9.85 - valid loss: 5.56, valid ACC: 9.37e-01, valid WER: 11.00, valid CER: 2.49
+epoch: 44, lr: 1.53e-04, steps: 27192 - train loss: 9.81 - valid loss: 5.58, valid ACC: 9.37e-01, valid WER: 11.07, valid CER: 2.50
+epoch: 45, lr: 1.52e-04, steps: 27810 - train loss: 9.63 - valid loss: 5.56, valid ACC: 9.38e-01, valid WER: 10.93, valid CER: 2.48
+epoch: 46, lr: 1.50e-04, steps: 28428 - train loss: 9.57 - valid loss: 5.54, valid ACC: 9.38e-01, valid WER: 10.93, valid CER: 2.47
+epoch: 47, lr: 1.48e-04, steps: 29046 - train loss: 9.53 - valid loss: 5.54, valid ACC: 9.37e-01, valid WER: 10.86, valid CER: 2.47
+epoch: 48, lr: 1.47e-04, steps: 29664 - train loss: 9.48 - valid loss: 5.53, valid ACC: 9.38e-01, valid WER: 10.84, valid CER: 2.46
+epoch: 49, lr: 1.46e-04, steps: 30000 - train loss: 9.35 - valid loss: 5.55, valid ACC: 9.38e-01, valid WER: 10.90, valid CER: 2.44
+epoch: 50, lr: 8.00e-04, steps: 30000 - train loss: 0.00e+00 - valid loss: 5.55, valid ACC: 9.38e-01, valid WER: 10.90, valid CER: 2.44