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---
language:
- hi
license: apache-2.0
tags:
- Openslr Multilingual
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2_xls_r_300m_hi_final
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 34.21
---
<!-- 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_xls_r_300m_hi_final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ['Openslr Multilingual and code-switching ASR challenge'](http://www.openslr.org/103/) dataset and ['mozilla-foundation/common_voice_7_0'](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3035
- Wer: 0.3137
- Cer: 0.0972
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.9821 | 0.64 | 400 | 0.5059 | 0.4783 | 0.1573 |
| 0.6861 | 1.28 | 800 | 0.4201 | 0.4247 | 0.1356 |
| 0.585 | 1.92 | 1200 | 0.3797 | 0.3811 | 0.1210 |
| 0.5193 | 2.56 | 1600 | 0.3577 | 0.3652 | 0.1152 |
| 0.4583 | 3.21 | 2000 | 0.3422 | 0.3519 | 0.1111 |
| 0.4282 | 3.85 | 2400 | 0.3261 | 0.3450 | 0.1071 |
| 0.3951 | 4.49 | 2800 | 0.3201 | 0.3325 | 0.1048 |
| 0.3619 | 5.13 | 3200 | 0.3167 | 0.3296 | 0.1030 |
| 0.345 | 5.77 | 3600 | 0.3157 | 0.3210 | 0.1013 |
| 0.338 | 6.41 | 4000 | 0.3051 | 0.3143 | 0.0982 |
| 0.3155 | 7.05 | 4400 | 0.3059 | 0.3154 | 0.0986 |
| 0.3057 | 7.69 | 4800 | 0.3035 | 0.3137 | 0.0972 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0 |