Fix/smaller fixes

.pre-commit-config.yaml

@@ -3,14 +3,12 @@ repos:
     rev: 23.7.0
     hooks:
     - id: black
--   repo: https://github.com/timothycrosley/isort
-    rev: 5.12.0
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.0.290
     hooks:
-    -   id: isort
--   repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
-    hooks:
-    - id: flake8
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+        types_or: [python, pyi, jupyter]
 -   repo: https://github.com/kynan/nbstripout
     rev: 0.6.0
     hooks:

README.md

@@ -6,7 +6,7 @@ ______________________________________________________________________
 [![Documentation](https://img.shields.io/badge/docs-passing-green)](https://alexandrainst.github.io/CoRal-models/coral_models.html)
 [![License](https://img.shields.io/github/license/alexandrainst/CoRal-models)](https://github.com/alexandrainst/CoRal-models/blob/main/LICENSE)
 [![LastCommit](https://img.shields.io/github/last-commit/alexandrainst/CoRal-models)](https://github.com/alexandrainst/CoRal-models/commits/main)
-[![Code Coverage](https://img.shields.io/badge/Coverage-61%25-yellow.svg)](https://github.com/alexandrainst/CoRal-models/tree/main/tests)
+[![Code Coverage](https://img.shields.io/badge/Coverage-60%25-yellow.svg)](https://github.com/alexandrainst/CoRal-models/tree/main/tests)
 
 
 Developers:

config/config.yaml

@@ -1,8 +1,8 @@
 defaults:
   - model: wav2vec2
   - datasets:
-    - common_voice_da
     - nst_da
+    - common_voice_9_da
   - override hydra/job_logging: custom
   - _self_
 
@@ -38,6 +38,6 @@ logging_steps: 10
 eval_steps: 100
 save_steps: 100
 save_total_limit: 2
-early_stopping: true
+early_stopping: false
 early_stopping_patience: 50
 fp16: true

config/datasets/common_voice_da.yaml → config/datasets/common_voice_13_da.yaml

@@ -1,4 +1,4 @@
-common_voice_da:
+common_voice_13_da:
   id: mozilla-foundation/common_voice_13_0
   subset: da
   train_name: train

config/datasets/common_voice_nn.yaml → config/datasets/common_voice_13_nn.yaml

@@ -1,4 +1,4 @@
-common_voice_nn:
+common_voice_13_nn:
   id: mozilla-foundation/common_voice_13_0
   subset: nn-NO
   train_name: train

config/datasets/common_voice_sv.yaml → config/datasets/common_voice_13_sv.yaml

@@ -1,4 +1,4 @@
-common_voice_sv:
+common_voice_13_sv:
   id: mozilla-foundation/common_voice_13_0
   subset: sv-SE
   train_name: train

config/datasets/common_voice_9_da.yaml

@@ -0,0 +1,7 @@
+common_voice_9_da:
+  id: mozilla-foundation/common_voice_9_0
+  subset: da
+  train_name: train
+  val_name: validation
+  test_name: test
+  text_column: sentence

config/model/test_wav2vec2.yaml

@@ -32,4 +32,6 @@ learning_rate: 4e-5
 warmup_steps: 1
 early_stopping: true
 early_stopping_patience: 5
+adam_first_momentum: 0.9
+adam_second_momentum: 0.999
 fp16: false

config/model/wav2vec2.yaml

@@ -10,10 +10,11 @@ characters_to_keep: 'abcdefghijklmnopqrstuvwxyzæøå0123456789éü '
 # Model hyperparameters
 sampling_rate: 16_000
 activation_dropout: 0.1
-attention_dropout: 0.1
-hidden_dropout: 0.1
-feat_proj_dropout: 0.1
-final_dropout: 0.1
+attention_dropout: 0.0
+hidden_dropout: 0.0
+feat_proj_dropout: 0.0
+feat_quantizer_dropout: 0.0
+final_dropout: 0.0
 mask_time_prob: 0.5
 mask_time_length: 10
 mask_feature_prob: 0.5
@@ -31,7 +32,9 @@ decoder:
 
 # Training hyperparameters
 batch_size: 8
-gradient_accumulation: 4
-max_steps: 120_000
+gradient_accumulation: 32
+max_steps: 13_000
 learning_rate: 3e-5
-warmup_steps: 500
+adam_first_momentum: 0.9
+adam_second_momentum: 0.98
+warmup_steps: 1_300

config/model/wav2vec2_no_reg.yaml

@@ -0,0 +1,40 @@
+name: wav2vec2_no_reg
+type: wav2vec2
+pretrained_model_id: chcaa/xls-r-300m-danish
+freeze_feature_encoder: false
+
+# Data hyperparameters
+clean_dataset: true
+characters_to_keep: 'abcdefghijklmnopqrstuvwxyzæøå0123456789éü '
+
+# Model hyperparameters
+sampling_rate: 16_000
+activation_dropout: 0.0
+hidden_dropout: 0.0
+attention_dropout: 0.0
+feat_proj_dropout: 0.0
+feat_quantizer_dropout: 0.0
+final_dropout: 0.0
+mask_time_prob: 0.0
+mask_time_length: 10
+mask_feature_prob: 0.0
+mask_feature_length: 64
+layerdrop: 0.0
+ctc_loss_reduction: mean
+
+# Decoder hyperparameters
+language_model_decoder: ngram
+decoder:
+  dataset_id: DDSC/reddit-da-asr-preprocessed
+  dataset_subset: null
+  dataset_split: train
+  n: 5
+
+# Training hyperparameters
+batch_size: 8
+gradient_accumulation: 32
+max_steps: 13_000
+learning_rate: 3e-5
+adam_first_momentum: 0.9
+adam_second_momentum: 0.98
+warmup_steps: 1_300

pyproject.toml

@@ -12,7 +12,7 @@ license = "MIT"
 python = ">=3.10,<3.12"
 hydra-core = "^1.1.1"
 evaluate = ">=0.4.0,<1.0.0"
-transformers = "^4.30.0"
+transformers = "^4.33.0"
 torch = "^2.0.0,!=2.0.1"
 librosa = ">=0.10.0.post2,<1.0.0"
 soundfile = ">=0.12.1,<1.0.0"
@@ -73,8 +73,11 @@ exclude = '''
 )/
 '''
 
-[tool.isort]
-profile = "black"
+[tool.ruff]
+target-version = "py311"
+
+[tool.ruff.extend-per-file-ignores]
+"__init__.py" = ["F401"]
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

src/coral_models/data.py

@@ -3,6 +3,7 @@
 import logging
 import os
 import re
+from pathlib import Path
 from unicodedata import normalize
 
 from datasets import (
@@ -36,15 +37,43 @@ def load_data(cfg: DictConfig) -> DatasetDict | IterableDatasetDict:
     for dataset_name, dataset_cfg in cfg.datasets.items():
         logger.info(f"Loading dataset {dataset_name!r}")
 
+        # Load from disk if the dataset ID is a path
+        if Path(dataset_cfg.id).exists():
+            dataset_paths = {
+                dataset_cfg.train_name: Path(dataset_cfg.id) / dataset_cfg.train_name
+            }
+            if dataset_cfg.val_name is not None:
+                dataset_paths[dataset_cfg.val_name] = (
+                    Path(dataset_cfg.id) / dataset_cfg.val_name
+                )
+            if dataset_cfg.test_name is not None:
+                dataset_paths[dataset_cfg.test_name] = (
+                    Path(dataset_cfg.id) / dataset_cfg.test_name
+                )
+            data_files = {
+                split: list(map(str, split_path.glob("data-*.arrow")))
+                for split, split_path in dataset_paths.items()
+            }
+            for split, files in data_files.items():
+                if len(files) == 0:
+                    raise FileNotFoundError(
+                        f"No data files found for split {split!r} in dataset "
+                        f"{dataset_name!r}. Please check that the provided dataset "
+                        "directory {dataset_paths[split]!r} contains arrow files of "
+                        "the form 'data-*.arrow'."
+                    )
+            dataset = load_dataset("arrow", data_files=data_files, streaming=True)
+
         # Load dataset from the Hugging Face Hub. The HUGGINGFACE_HUB_TOKEN is only used
         # during CI - normally it is expected that the user is logged in to the Hugging
         # Face Hub using the `huggingface-cli login` command.
-        dataset = load_dataset(
-            path=dataset_cfg.id,
-            name=dataset_cfg.subset,
-            token=os.getenv("HUGGINGFACE_HUB_TOKEN", True),
-            streaming=True,
-        )
+        else:
+            dataset = load_dataset(
+                path=dataset_cfg.id,
+                name=dataset_cfg.subset,
+                token=os.getenv("HUGGINGFACE_HUB_TOKEN", True),
+                streaming=True,
+            )
 
         assert isinstance(dataset, DatasetDict) or isinstance(
             dataset, IterableDatasetDict
@@ -212,7 +241,6 @@ def clean_dataset(
         "è": "e",
         "kg": " kilo ",
         "μg": " mikrogram ",
-        "μg": " mikrogram ",
         "-": " minus ",
         "+": " plus ",
         "μ": " mikro ",

src/coral_models/wav2vec2.py

@@ -144,6 +144,7 @@ def load_model(self) -> Wav2Vec2ForCTC:
                 hidden_dropout=self.cfg.model.hidden_dropout,
                 feat_proj_dropout=self.cfg.model.feat_proj_dropout,
                 final_dropout=self.cfg.model.final_dropout,
+                apply_spec_augment=True,
                 mask_time_prob=self.cfg.model.mask_time_prob,
                 mask_time_length=self.cfg.model.mask_time_length,
                 mask_feature_prob=self.cfg.model.mask_feature_prob,
@@ -154,6 +155,7 @@ def load_model(self) -> Wav2Vec2ForCTC:
                 bos_token_id=self.processor.tokenizer.bos_token_id,
                 eos_token_id=self.processor.tokenizer.eos_token_id,
                 vocab_size=len(self.processor.tokenizer.get_vocab()),
+                ctc_zero_infinity=True,
             )
             assert isinstance(model, Wav2Vec2ForCTC)
 
@@ -204,11 +206,13 @@ def load_training_arguments(self) -> TrainingArguments:
             seed=self.cfg.seed,
             remove_unused_columns=False,
             optim=OptimizerNames.ADAMW_TORCH,
-            use_mps_device=mps_is_available(),
+            adam_beta1=self.cfg.model.adam_first_momentum,
+            adam_beta2=self.cfg.model.adam_second_momentum,
             report_to=["wandb"] if self.cfg.wandb else [],
             ignore_data_skip=self.cfg.ignore_data_skip,
             save_safetensors=True,
-            no_cuda=hasattr(sys, "_called_from_test"),
+            use_cpu=hasattr(sys, "_called_from_test"),
+            auto_find_batch_size=True,
         )
         return args