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CKPT.yaml

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+# yamllint disable
+ErrorRate: 0.3987189829349518
+end-of-epoch: true
+unixtime: 1717514247.4720354

brain.ckpt

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+oid sha256:847c6ac3bf30588a0216e35313ad9505053ca7e62871c1160c33d532b5c8815f
+size 50

classifier.ckpt

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+oid sha256:d292d66608240df48e5d2f530c94544a6fec4c02d7337aec2a2ef54b26c9337a
+size 3840482

counter.ckpt

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+oid sha256:6b86b273ff34fce19d6b804eff5a3f5747ada4eaa22f1d49c01e52ddb7875b4b
+size 1

dataloader-TRAIN.ckpt

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+oid sha256:21cc5776d62ef1cf0bac1940bf24bfa6c55f025bd07894e4b10f42f109409ec3
+size 5

embedding_model.ckpt

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+oid sha256:2f039c9c9eb7c802f53f434c3e2a5ff6e6a2450fb878518ef5a2b94ccda65e47
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hyperparams.yaml

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+# Generated 2024-06-04 from:
+# /content/speechbrain/recipes/VoxCeleb/SpeakerRec/hparams/train_x_vectors.yaml
+# yamllint disable
+# ################################
+# Model: Speaker identification with ECAPA
+# Authors: Hwidong Na & Mirco Ravanelli
+# ################################
+
+# Basic parameters
+seed: 1986
+__set_seed: !apply:torch.manual_seed [1986]
+output_folder: results/xvect_augment/1986
+save_folder: results/xvect_augment/1986/save
+pretrained_path: !ref <save_folder>/CKPT+2024-06-04+15-17-27+00
+train_log: results/xvect_augment/1986/train_log.txt
+
+# Data for augmentation
+NOISE_DATASET_URL: 
+  https://www.dropbox.com/scl/fi/a09pj97s5ifan81dqhi4n/noises.zip?rlkey=j8b0n9kdjdr32o1f06t0cw5b7&dl=1
+RIR_DATASET_URL: 
+  https://www.dropbox.com/scl/fi/linhy77c36mu10965a836/RIRs.zip?rlkey=pg9cu8vrpn2u173vhiqyu743u&dl=1
+
+# Data files
+data_folder: ./Voxceleb  # e.g. /path/to/Voxceleb
+data_folder_noise: ./Voxceleb/noise         # The noisy sequences for data augmentation will automatically be downloaded here.
+data_folder_rir: ./Voxceleb/rir         # The impulse responses used for data augmentation will automatically be downloaded here.
+train_annotation: results/xvect_augment/1986/save/train.csv
+valid_annotation: results/xvect_augment/1986/save/dev.csv
+noise_annotation: results/xvect_augment/1986/save/noise.csv
+rir_annotation: results/xvect_augment/1986/save/rir.csv
+
+# Use the following links for the official voxceleb splits:
+# VoxCeleb1 (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt
+# VoxCeleb1-H (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_hard2.txt
+# VoxCeleb1-E (cleaned): https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/list_test_all2.txt.
+# VoxCeleb1-E and VoxCeleb1-H lists are drawn from the VoxCeleb1 training set.
+# Therefore you cannot use any files in VoxCeleb1 for training if you are using these lists for testing.
+verification_file: ./Voxceleb/save/veri_test.txt
+
+split_ratio: [90, 10]
+skip_prep: true
+ckpt_interval_minutes: 15 # save checkpoint every N min
+
+# Training parameters
+number_of_epochs: 1
+batch_size: 16
+lr: 0.001
+lr_final: 0.0001
+
+sample_rate: 16000
+sentence_len: 3.0 # seconds
+shuffle: true
+random_chunk: false
+
+# Feature parameters
+n_mels: 24
+left_frames: 0
+right_frames: 0
+deltas: false
+
+# Number of speakers
+out_n_neurons: 1349 #1211 for vox1  # 5994 for vox2, 7205 for vox1+vox2
+emb_dim: 512
+
+num_workers: 4
+dataloader_options:
+  batch_size: 16
+  shuffle: true
+  num_workers: 4
+
+# Functions
+compute_features: &id005 !new:speechbrain.lobes.features.Fbank
+  n_mels: 24
+  left_frames: 0
+  right_frames: 0
+  deltas: false
+
+embedding_model: &id006 !new:speechbrain.lobes.models.Xvector.Xvector
+  in_channels: 24
+  activation: !name:torch.nn.LeakyReLU
+  tdnn_blocks: 5
+  tdnn_channels: [512, 512, 512, 512, 1500]
+  tdnn_kernel_sizes: [5, 3, 3, 1, 1]
+  tdnn_dilations: [1, 2, 3, 1, 1]
+  lin_neurons: 512
+
+classifier: &id007 !new:speechbrain.lobes.models.Xvector.Classifier
+  input_shape: [null, null, 512]
+  activation: !name:torch.nn.LeakyReLU
+  lin_blocks: 1
+  lin_neurons: 512
+  out_neurons: 1349
+
+epoch_counter: &id009 !new:speechbrain.utils.epoch_loop.EpochCounter
+  limit: 1
+
+############################## Augmentations ###################################
+
+# Download and prepare the dataset of noisy sequences for augmentation
+prepare_noise_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
+  URL: 
+    https://www.dropbox.com/scl/fi/a09pj97s5ifan81dqhi4n/noises.zip?rlkey=j8b0n9kdjdr32o1f06t0cw5b7&dl=1
+  dest_folder: ./Voxceleb/noise
+  ext: wav
+  csv_file: results/xvect_augment/1986/save/noise.csv
+
+
+# Add noise to input signal
+add_noise: &id001 !new:speechbrain.augment.time_domain.AddNoise
+  csv_file: results/xvect_augment/1986/save/noise.csv
+  snr_low: 0
+  snr_high: 15
+  noise_sample_rate: 16000
+  clean_sample_rate: 16000
+  num_workers: 4
+
+# Download and prepare the dataset of room impulse responses for augmentation
+prepare_rir_data: !name:speechbrain.augment.preparation.prepare_dataset_from_URL
+  URL: 
+    https://www.dropbox.com/scl/fi/linhy77c36mu10965a836/RIRs.zip?rlkey=pg9cu8vrpn2u173vhiqyu743u&dl=1
+  dest_folder: ./Voxceleb/rir
+  ext: wav
+  csv_file: results/xvect_augment/1986/save/rir.csv
+
+# Add reverberation to input signal
+add_reverb: &id002 !new:speechbrain.augment.time_domain.AddReverb
+  csv_file: results/xvect_augment/1986/save/rir.csv
+  reverb_sample_rate: 16000
+  clean_sample_rate: 16000
+  num_workers: 4
+
+# Frequency drop: randomly drops a number of frequency bands to zero.
+drop_freq: &id003 !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: &id004 !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
+  parallel_augment: true
+  concat_original: true
+  min_augmentations: 4
+  max_augmentations: 4
+  augment_prob: 1.0
+  augmentations: [*id001, *id002, *id003, *id004]
+
+mean_var_norm: &id008 !new:speechbrain.processing.features.InputNormalization
+
+# Cost + optimization
+  norm_type: sentence
+  std_norm: false
+
+mean_var_norm_emb: !new:speechbrain.processing.features.InputNormalization
+    norm_type: global
+    std_norm: False
+
+label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
+
+modules:
+  compute_features: *id005
+  embedding_model: *id006
+  classifier: *id007
+  mean_var_norm: *id008
+compute_cost: !name:speechbrain.nnet.losses.nll_loss
+# compute_error: !name:speechbrain.nnet.losses.classification_error
+
+opt_class: !name:torch.optim.Adam
+  lr: 0.001
+  weight_decay: 0.000002
+
+lr_annealing: !new:speechbrain.nnet.schedulers.LinearScheduler
+  initial_value: 0.001
+  final_value: 0.0001
+  epoch_count: 1
+
+# Logging + checkpoints
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/xvect_augment/1986/train_log.txt
+
+error_stats: !name:speechbrain.utils.metric_stats.MetricStats
+  metric: !name:speechbrain.nnet.losses.classification_error
+    reduction: batch
+
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/xvect_augment/1986/save
+  recoverables:
+    embedding_model: *id006
+    classifier: *id007
+    normalizer: *id008
+    counter: *id009
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        embedding_model: *id006
+        mean_var_norm: *id008
+        classifier: *id007
+        label_encoder: !ref <label_encoder>
+    paths:
+        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
+        mean_var_norm: !ref <pretrained_path>/normalizer.ckpt
+        classifier: !ref <pretrained_path>/classifier.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt

label_encoder.txt

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+================
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normalizer.ckpt

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optimizer.ckpt

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