File size: 3,046 Bytes
873a17c ae7345a 873a17c ae7345a ba3b0c8 873a17c ae7345a 873a17c ae7345a 73e8d12 ae7345a 873a17c |
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 97 98 99 100 101 |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-urdu
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Urdu Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: common_voice # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Urdu # Required. Example: Common Voice zh-CN
args: ur # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 100 # Required. Example: 20.90
name: Test WER # Optional. Example: Test WER
args:
- 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: 10
- num_epochs: 30
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
---
<!-- 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-urdu
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6772
- Wer: 1.0
## 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: 10
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 11.1125 | 3.33 | 40 | 3.2875 | 1.0 |
| 3.2077 | 6.67 | 80 | 3.1499 | 1.0 |
| 3.1725 | 10.0 | 120 | 3.1484 | 1.0 |
| 3.148 | 13.33 | 160 | 3.0948 | 1.0 |
| 3.1098 | 16.67 | 200 | 3.0897 | 1.0 |
| 3.085 | 20.0 | 240 | 3.0609 | 1.0 |
| 3.0315 | 23.33 | 280 | 2.9636 | 1.0 |
| 2.9038 | 26.67 | 320 | 2.7838 | 1.0 |
| 2.7599 | 30.0 | 360 | 2.6772 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|