metadata

base_model: facebook/wav2vec2-large-xlsr-53
datasets:
  - fleurs
library_name: transformers
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-large-xlsr-53-Hindi-Version3
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: fleurs
          type: fleurs
          config: hi_in
          split: None
          args: hi_in
        metrics:
          - type: wer
            value: 0.282143903153373
            name: Wer
language:
  - hi
pipeline_tag: automatic-speech-recognition

wav2vec2-large-xlsr-53-Hindi-Version3

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the fleurs dataset. It achieves the following results on the evaluation set:

Loss: 0.7311
Wer: 0.2821

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.362	6.7568	500	3.3795	1.0
0.5309	13.5135	1000	0.5572	0.4268
0.2342	20.2703	1500	0.5206	0.3341
0.1408	27.0270	2000	0.5516	0.3292
0.1126	33.7838	2500	0.6105	0.3199
0.0892	40.5405	3000	0.6489	0.3123
0.0721	47.2973	3500	0.6533	0.3067
0.0719	54.0541	4000	0.6898	0.3050
0.0592	60.8108	4500	0.7007	0.2990
0.0737	67.5676	5000	0.7106	0.2921
0.0399	74.3243	5500	0.7271	0.2916
0.0409	81.0811	6000	0.7298	0.2871
0.0322	87.8378	6500	0.7311	0.2835
0.0285	94.5946	7000	0.7311	0.2821

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1