---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
  results: []
---

<!-- 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. -->

# 

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_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9201
- Wer: 1.0091

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.34  | 100  | 4.2570          | 0.9999 |
| No log        | 0.68  | 200  | 3.3222          | 1.0005 |
| No log        | 1.02  | 300  | 2.3755          | 1.0    |
| No log        | 1.36  | 400  | 1.4071          | 1.0037 |
| 3.8379        | 1.69  | 500  | 1.2223          | 1.0091 |
| 3.8379        | 2.03  | 600  | 1.0885          | 1.0273 |
| 3.8379        | 2.37  | 700  | 0.9818          | 1.0098 |
| 3.8379        | 2.71  | 800  | 0.9154          | 1.0110 |
| 3.8379        | 3.05  | 900  | 0.8869          | 1.0154 |
| 0.5888        | 3.39  | 1000 | 0.8976          | 1.0149 |
| 0.5888        | 3.73  | 1100 | 0.8323          | 1.0124 |
| 0.5888        | 4.07  | 1200 | 0.8488          | 1.0087 |
| 0.5888        | 4.41  | 1300 | 0.8659          | 1.0069 |
| 0.5888        | 4.75  | 1400 | 0.8612          | 1.0043 |
| 0.3706        | 5.08  | 1500 | 0.8300          | 1.0183 |
| 0.3706        | 5.42  | 1600 | 0.8417          | 1.0091 |
| 0.3706        | 5.76  | 1700 | 0.8261          | 1.0087 |
| 0.3706        | 6.1   | 1800 | 0.8548          | 1.0068 |
| 0.3706        | 6.44  | 1900 | 0.7984          | 1.0110 |
| 0.2671        | 6.78  | 2000 | 0.8388          | 1.0117 |
| 0.2671        | 7.12  | 2100 | 0.8499          | 1.0073 |
| 0.2671        | 7.46  | 2200 | 0.8480          | 1.0112 |
| 0.2671        | 7.8   | 2300 | 0.7929          | 1.0099 |
| 0.2671        | 8.14  | 2400 | 0.8659          | 1.0089 |
| 0.2017        | 8.47  | 2500 | 0.8583          | 1.0063 |
| 0.2017        | 8.81  | 2600 | 0.8326          | 1.0110 |
| 0.2017        | 9.15  | 2700 | 0.8759          | 1.0037 |
| 0.2017        | 9.49  | 2800 | 0.8576          | 1.0100 |
| 0.2017        | 9.83  | 2900 | 0.8777          | 1.0224 |
| 0.1682        | 10.17 | 3000 | 0.8865          | 1.0280 |
| 0.1682        | 10.51 | 3100 | 0.9213          | 1.0068 |
| 0.1682        | 10.85 | 3200 | 0.8881          | 1.0152 |
| 0.1682        | 11.19 | 3300 | 0.9089          | 1.0100 |
| 0.1682        | 11.53 | 3400 | 0.8974          | 1.0127 |
| 0.1347        | 11.86 | 3500 | 0.9129          | 1.0123 |
| 0.1347        | 12.2  | 3600 | 0.9939          | 1.0169 |
| 0.1347        | 12.54 | 3700 | 0.9135          | 1.0083 |
| 0.1347        | 12.88 | 3800 | 0.9229          | 1.0118 |
| 0.1347        | 13.22 | 3900 | 0.9610          | 1.0107 |
| 0.1049        | 13.56 | 4000 | 0.9236          | 1.0099 |
| 0.1049        | 13.9  | 4100 | 0.8967          | 1.0085 |
| 0.1049        | 14.24 | 4200 | 0.8980          | 1.0081 |
| 0.1049        | 14.58 | 4300 | 0.9023          | 1.0081 |
| 0.1049        | 14.92 | 4400 | 0.9216          | 1.0079 |
| 0.0917        | 15.25 | 4500 | 0.9443          | 1.0090 |
| 0.0917        | 15.59 | 4600 | 0.9390          | 1.0091 |
| 0.0917        | 15.93 | 4700 | 0.9153          | 1.0083 |
| 0.0917        | 16.27 | 4800 | 0.9190          | 1.0092 |
| 0.0917        | 16.61 | 4900 | 0.9194          | 1.0091 |
| 0.0786        | 16.95 | 5000 | 0.9201          | 1.0091 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0