Question: validation_ds for En Conformer-CTC Small LibriSpeech configuration.
this is a question about the pre-trained [model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small_ls).


I downloaded the model and used the following command to check the training-related conf.

```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="<path_of_pretrained_model>")
asr_model._cfg
```


And I checked the following.
>{'sample_rate': 16000, 'log_prediction': True, 'ctc_reduction': 'mean_batch', 'train_ds': {'manifest_filepath': '/data/Librispeech_SP_Tarred/tarred_audio_manifest.json', 'sample_rate': 16000, 'batch_size': 32, 'shuffle': True, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'trim_silence': False, 'max_duration': 16.7, 'min_duration': 0.1, 'shuffle_n': 2048, 'is_tarred': True, 'tarred_audio_filepaths': '/data/Librispeech_SP_Tarred/audio__OP_0..511_CL_.tar'}, 'validation_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'], 'sample_rate': 16000, 'batch_size': 32, 'shuffle': False, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'is_tarred': False, 'tarred_audio_filepaths': '/data/LibriSpeech/eval__OP_0..1023_CL_.tar'}, 'test_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'], 'sample_rate': 16000, 'batch_size': 32, 'shuffle': False, 'num_workers': 8, 'pin_memory': True, 'use_start_end_token': False, 'is_tarred': False, 'tarred_audio_filepaths': '/data/LibriSpeech/eval__OP_0..1023_CL_.tar'}, 'tokenizer': {'dir': '/tokenizers/librispeech/experimental/tokenizer_spe_unigram_v128_max0/', 'type': 'bpe'}, 'preprocessor': {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'sample_rate': 16000, 'normalize': 'per_feature', 'window_size': 0.025, 'window_stride': 0.01, 'window': 'hann', 'features': 80, 'n_fft': 512, 'log': True, 'frame_splicing': 1, 'dither': 1e-05, 'pad_to': 0, 'pad_value': 0.0}, 'spec_augment': {'_target_': 'nemo.collections.asr.modules.SpectrogramAugmentation', 'freq_masks': 2, 'time_masks': 5, 'freq_width': 27, 'time_width': 0.05}, 'encoder': {'_target_': 'nemo.collections.asr.modules.ConformerEncoder', 'feat_in': 80, 'feat_out': -1, 'n_layers': 16, 'd_model': 176, 'subsampling': 'striding', 'subsampling_factor': 4, 'subsampling_conv_channels': 176, 'ff_expansion_factor': 4, 'self_attention_model': 'rel_pos', 'n_heads': 4, 'att_context_size': [-1, -1], 'xscaling': True, 'untie_biases': True, 'pos_emb_max_len': 5000, 'conv_kernel_size': 31, 'dropout': 0.1, 'dropout_emb': 0.0, 'dropout_att': 0.1}, 'decoder': {'_target_': 'nemo.collections.asr.modules.ConvASRDecoder', 'feat_in': 176, 'num_classes': 128, 'vocabulary': ['<unk>', 'e', 's', '▁', 't', 'a', 'o', 'i', '▁the', 'd', 'l', 'n', '▁a', 'm', 'y', 'u', '▁s', 'p', 'ed', 'c', '▁and', 're', '▁to', '▁of', 'r', 'w', 'ing', '▁w', 'h', '▁p', '▁c', 'er', 'f', 'k', 'ar', '▁in', '▁f', '▁b', 'g', 'an', 'in', '▁i', 'en', '▁he', 'le', '▁g', 'or', 'll', 'b', '▁be', 'ro', 'st', 'on', '▁d', 'v', 'ly', 'ce', 'ur', 'es', '▁that', '▁o', 'us', '▁was', '▁it', '▁th', 've', 'ch', 'un', 'al', '▁t', '▁ma', 'ri', '▁you', '▁on', 'ver', 'ent', '▁for', '▁re', 'ra', "'", '▁his', 'ir', 'ter', '▁with', '▁her', 'it', 'th', '▁mo', '▁me', '▁ha', '▁e', '▁as', 'tion', '▁had', '▁not', '▁no', '▁do', 'ther', '▁but', '▁st', '▁she', '▁is', 'igh', '▁ho', '▁lo', 'ng', '▁him', '▁an', 'ck', 'j', 'ugh', '▁de', '▁li', '▁mi', '▁la', '▁my', '▁con', '▁have', '▁this', '▁which', 'q', '▁up', '▁said', '▁from', '▁who', '▁ex', 'x', 'z']}, 'optim': {'name': 'adamw', 'lr': 5.0, 'betas': [0.9, 0.98], 'weight_decay': 0.001, 'sched': {'name': 'NoamAnnealing', 'd_model': 176, 'warmup_steps': 10000, 'warmup_ratio': None, 'min_lr': 1e-06}}, 'target': 'nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE'},


I paid attention to the next part.

>'validation_ds': {'manifest_filepath': ['/manifests/librispeech/librivox-dev-other.json', '/manifests/librispeech/librivox-dev-clean.json', '/manifests/librispeech/librivox-test-other.json', '/manifests/librispeech/librivox-test-clean.json'],


Did you use data other than LS-dev for LS training?
Or, is it a default value of the model not used in real training?