WIP french dataset

Squashed commits from pchampio to avoid storing HF model junk in the repository
(still have a backup of the branch somewhere)

.dict-speechbrain.txt

@@ -1196,3 +1196,4 @@ quelques
 Université
 Università
 vie
+ois

recipes/ESTER+EPAC+ETAPE+REPERE/ASR/.gitignore

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+wav2vec2_checkpoint/

recipes/ESTER+EPAC+ETAPE+REPERE/ASR/extra_requirements.txt

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+ftfy
+# k2 # It is better to install k2 with the procedure listed here: https://k2-fsa.github.io/k2/installation/from_wheels.html
+num2words
+soundfile

recipes/ESTER+EPAC+ETAPE+REPERE/ASR/hparams/train_with_wav2vec_ctc_k2_char.yaml

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+# ################################
+# Model: wav2vec2 + DNN + CTC + LM (k2)
+# Augmentation: SpecAugment + Speed
+# Authors: Pierre Champion 2023
+# ################################
+
+# Seed needs to be set at top of yaml, before objects with parameters are made
+seed: 1116
+__set_seed: !apply:torch.manual_seed [!ref <seed>]
+output_folder: !ref results/<merge_train_csv>_wav2vec2_<token_type>_k2/<seed>
+save_folder: !ref <output_folder>/save
+train_log: !ref <output_folder>/train_log.txt
+
+# wav2vec2_hub: LeBenchmark/wav2vec2-FR-3K-base
+# wav2vec2_out_shape: 768
+
+# wav2vec2_hub: LeBenchmark/wav2vec2-FR-7K-large
+# wav2vec2_out_shape: 1024
+
+wav2vec2_hub: LeBenchmark/wav2vec2-FR-14K-xlarge
+wav2vec2_out_shape: 1280
+
+wav2vec2_folder: !ref wav2vec2_checkpoint
+
+# Data files
+data_folder: !PLACEHOLDER # e.g, /path/to/corpus (**/*.stm)
+stm_directory: !ref <data_folder>/**/[^\.ne_e2\.|\.ne\.|\.spk\.|part\.]*.stm
+wav_directory: !ref <data_folder>/**/*.wav
+train_splits: {"train_ESTER2":["/ESTER2/train_trans_rapide/*", "/ESTER2/train/*"], "train_ESTER1":["/ESTER1/train/*"], "train_EPAC":["/EPAC/train/*"], "train_ETAPE":["/ETAPE/train/*"], "train_REPERE":["/REPERE/train/*"]}
+dev_splits: {"dev_ESTER2":["/ESTER2/dev/*"], "dev_ESTER1":["/ESTER1/dev/*"], "dev_ETAPE":["/ETAPE/dev/*"], "dev_REPERE2014":["/REPERE/dev2014/*"]}
+test_splits: {"test_ESTER2":["/ESTER2/test/*"], "test_ESTER1":["/ESTER1/test/*"], "test_ETAPE":["/ETAPE/test/*"], "test_EPAC":["/EPAC/test/*"], "test_REPERE2014":["/REPERE/test2014/*"]}
+merge_train_csv: "train_ESTER2+train_ESTER1+train_EPAC+train_ETAPE+train_REPERE"
+prep_save_folder: !ref <output_folder>
+skip_prep: False
+skip_token_prep: False
+ckpt_interval_minutes: 10 # save checkpoint every N min
+# the following CSVs are found in <output_folder>
+train_csv: train.csv
+valid_csv: dev_ESTER2.csv
+test_csv:
+   - test_ESTER2.csv
+   - test_ESTER1.csv
+   - test_ETAPE.csv
+   - test_EPAC.csv
+   - test_REPERE2014.csv
+
+# For k2 CTC training
+caching: True
+lang_dir: !ref <output_folder>/lang
+vocab_file: !ref <output_folder>/vocab.txt
+# token_type: phone
+token_type: char
+sil_prob: 0.
+add_word_boundary: True
+# For k2 decoding
+test_search_beam: 32
+# Beam size (for decoding)
+test_output_beam: 8
+test_min_active_state: 300
+test_max_active_state: 3000
+# Acoustic scale (mutliplied by the log probs)
+ac_scale: 1.5
+compose_HL_with_G: True
+# 1best or whole-lattice-rescoring
+# decoding_method: whole-lattice-rescoring
+decoding_method: 1best
+# LM scale to be used for rescoring. Only used if rescoring
+rescoring_lm_scale: 0.4
+# This is where the 3gram and (optionally) 4gram LM are stored
+# They can be in either ARPA or FST format. If the former, then
+# the FST equivalent will be created in the same directory by
+# using kaldilm.
+lm_dir: ../LM/results/n_gram_lm
+
+G_arpa: 3-for-char-gram.arpa
+G_rescoring_arpa: 4-for-char-gram.arpa
+
+# Training parameters
+number_of_epochs: 20
+lr: 1.5
+lr_wav2vec: 0.001
+sorting: ascending  # only ascending and descending are supported currently
+precision: fp32
+sample_rate: 16000
+
+# With data_parallel batch_size is split into N jobs
+# With DDP batch_size is multiplied by N jobs
+batch_size: 8
+num_workers: 20
+grad_accumulation_factor: 2
+test_batch_size: 12
+# nonfinite loss does happen from time to time on some segments with char training
+nonfinite_patience: 20
+
+# In seconds
+avoid_if_longer_than: 90.0
+avoid_if_smaller_than: 0.5
+
+# Dataloader options
+train_dataloader_opts:
+   batch_size: !ref <batch_size>
+   num_workers: !ref <num_workers>
+
+valid_dataloader_opts:
+   batch_size: !ref <batch_size>
+   num_workers: !ref <num_workers>
+
+test_dataloader_opts:
+   batch_size: !ref <test_batch_size>
+   num_workers: !ref <num_workers>
+
+# Model parameters
+activation: !name:torch.nn.LeakyReLU
+dnn_layers: 4
+freeze_wav2vec: True
+number_lines_for_tokens: 52
+
+# Outputs
+# in k2 check lang/tokens.txt
+# BPE size, index(blank/eos/bos) = 0
+output_neurons: !apply:speechbrain.lobes.utils.NumberOfLines
+   file: !ref <lang_dir>/tokens.txt
+   default: !ref <number_lines_for_tokens>
+
+#
+# Functions and classes
+#
+epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
+   limit: !ref <number_of_epochs>
+
+speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb
+   orig_freq: !ref <sample_rate>
+   speeds: [95, 100, 105]
+
+# Frequency drop: randomly drops a number of frequency bands to zero.
+drop_freq_low: 0  # Min frequency band dropout probability
+drop_freq_high: 1  # Max frequency band dropout probability
+drop_freq_count_low: 1  # Min number of frequency bands to drop
+drop_freq_count_high: 3  # Max number of frequency bands to drop
+drop_freq_width: 0.05  # Width of frequency bands to drop
+
+drop_freq: !new:speechbrain.augment.time_domain.DropFreq
+   drop_freq_low: !ref <drop_freq_low>
+   drop_freq_high: !ref <drop_freq_high>
+   drop_freq_count_low: !ref <drop_freq_count_low>
+   drop_freq_count_high: !ref <drop_freq_count_high>
+   drop_freq_width: !ref <drop_freq_width>
+
+# Augmenter: Combines previously defined augmentations to perform data augmentation
+wav_augment: !new:speechbrain.augment.augmenter.Augmenter
+   parallel_augment: False
+   repeat_augment: 1
+   shuffle_augmentations: False
+   min_augmentations: 1
+   max_augmentations: 1
+   augment_prob: 1.0
+   augmentations: [
+      !ref <speed_perturb>,
+      !ref <drop_freq>,
+   ]
+
+enc: !new:speechbrain.lobes.models.VanillaNN.VanillaNN
+   input_shape: [null, null, !ref <wav2vec2_out_shape>]
+   activation: !ref <activation>
+   dnn_blocks: !ref <dnn_layers>
+   dnn_neurons: !ref <wav2vec2_out_shape>
+
+wav2vec2: !new:speechbrain.lobes.models.huggingface_transformers.wav2vec2.Wav2Vec2
+   source: !ref <wav2vec2_hub>
+   output_norm: True
+   freeze: !ref <freeze_wav2vec>
+   save_path: !ref <wav2vec2_folder>
+
+ctc_lin: !new:speechbrain.nnet.linear.Linear
+   input_size: !ref <wav2vec2_out_shape>
+   n_neurons: !ref <output_neurons>
+
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+   apply_log: True
+
+ctc_cost: !name:speechbrain.k2_integration.losses.ctc_k2
+   reduction: mean
+   beam_size: 10
+
+modules:
+   wav2vec2: !ref <wav2vec2>
+   enc: !ref <enc>
+   ctc_lin: !ref <ctc_lin>
+
+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