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---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- hi
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hi
metrics:
- name: Test WER
type: wer
value: 0.3628727037755008
- name: Test CER
type: cer
value: 0.11933724247521164
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- 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-xls-r-300m-hi-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6510
- Wer: 0.3179
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1
Note: Hindi language not found in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- 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: 2000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 12.5576 | 1.04 | 200 | 6.6594 | 1.0 |
| 4.4069 | 2.07 | 400 | 3.6011 | 1.0 |
| 3.4273 | 3.11 | 600 | 3.3370 | 1.0 |
| 2.1108 | 4.15 | 800 | 1.0641 | 0.6562 |
| 0.8817 | 5.18 | 1000 | 0.7178 | 0.5172 |
| 0.6508 | 6.22 | 1200 | 0.6612 | 0.4839 |
| 0.5524 | 7.25 | 1400 | 0.6458 | 0.4889 |
| 0.4992 | 8.29 | 1600 | 0.5791 | 0.4382 |
| 0.4669 | 9.33 | 1800 | 0.6039 | 0.4352 |
| 0.4441 | 10.36 | 2000 | 0.6276 | 0.4297 |
| 0.4172 | 11.4 | 2200 | 0.6183 | 0.4474 |
| 0.3872 | 12.44 | 2400 | 0.5886 | 0.4231 |
| 0.3692 | 13.47 | 2600 | 0.6448 | 0.4399 |
| 0.3385 | 14.51 | 2800 | 0.6344 | 0.4075 |
| 0.3246 | 15.54 | 3000 | 0.5896 | 0.4087 |
| 0.3026 | 16.58 | 3200 | 0.6158 | 0.4016 |
| 0.284 | 17.62 | 3400 | 0.6038 | 0.3906 |
| 0.2682 | 18.65 | 3600 | 0.6165 | 0.3900 |
| 0.2577 | 19.69 | 3800 | 0.5754 | 0.3805 |
| 0.2509 | 20.73 | 4000 | 0.6028 | 0.3925 |
| 0.2426 | 21.76 | 4200 | 0.6335 | 0.4138 |
| 0.2346 | 22.8 | 4400 | 0.6128 | 0.3870 |
| 0.2205 | 23.83 | 4600 | 0.6223 | 0.3831 |
| 0.2104 | 24.87 | 4800 | 0.6122 | 0.3781 |
| 0.1992 | 25.91 | 5000 | 0.6467 | 0.3792 |
| 0.1916 | 26.94 | 5200 | 0.6277 | 0.3636 |
| 0.1835 | 27.98 | 5400 | 0.6317 | 0.3773 |
| 0.1776 | 29.02 | 5600 | 0.6124 | 0.3614 |
| 0.1751 | 30.05 | 5800 | 0.6475 | 0.3628 |
| 0.1662 | 31.09 | 6000 | 0.6266 | 0.3504 |
| 0.1584 | 32.12 | 6200 | 0.6347 | 0.3532 |
| 0.1494 | 33.16 | 6400 | 0.6636 | 0.3491 |
| 0.1457 | 34.2 | 6600 | 0.6334 | 0.3507 |
| 0.1427 | 35.23 | 6800 | 0.6397 | 0.3442 |
| 0.1397 | 36.27 | 7000 | 0.6468 | 0.3496 |
| 0.1283 | 37.31 | 7200 | 0.6291 | 0.3416 |
| 0.1255 | 38.34 | 7400 | 0.6652 | 0.3461 |
| 0.1195 | 39.38 | 7600 | 0.6587 | 0.3342 |
| 0.1169 | 40.41 | 7800 | 0.6478 | 0.3319 |
| 0.1126 | 41.45 | 8000 | 0.6280 | 0.3291 |
| 0.1112 | 42.49 | 8200 | 0.6434 | 0.3290 |
| 0.1069 | 43.52 | 8400 | 0.6542 | 0.3268 |
| 0.1027 | 44.56 | 8600 | 0.6536 | 0.3239 |
| 0.0993 | 45.6 | 8800 | 0.6622 | 0.3257 |
| 0.0973 | 46.63 | 9000 | 0.6572 | 0.3192 |
| 0.0911 | 47.67 | 9200 | 0.6522 | 0.3175 |
| 0.0897 | 48.7 | 9400 | 0.6521 | 0.3200 |
| 0.0905 | 49.74 | 9600 | 0.6510 | 0.3179 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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