File size: 2,752 Bytes
44de1c6 980edc1 44de1c6 980edc1 44de1c6 980edc1 44de1c6 980edc1 44de1c6 980edc1 2cd67d5 44de1c6 2cd67d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-Hindi-Version3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/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 |