pytorch tensorboard wav2vec2 automatic-speech-recognition dataset:common_voice transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: test-model
  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. -->

# test-model

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0161
- Wer: 0.0141

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8062        | 0.29  | 400   | 2.0576          | 1.0    |
| 0.9633        | 0.57  | 800   | 0.5862          | 0.6023 |
| 0.6079        | 0.86  | 1200  | 0.4897          | 0.4824 |
| 0.4993        | 1.14  | 1600  | 0.3823          | 0.3989 |
| 0.4269        | 1.43  | 2000  | 0.3749          | 0.3761 |
| 0.4049        | 1.72  | 2400  | 0.3501          | 0.3536 |
| 0.3998        | 2.0   | 2800  | 0.3527          | 0.3381 |
| 0.3172        | 2.29  | 3200  | 0.3188          | 0.3257 |
| 0.3161        | 2.57  | 3600  | 0.3217          | 0.3185 |
| 0.3213        | 2.86  | 4000  | 0.2988          | 0.3007 |
| 0.3035        | 3.15  | 4400  | 0.3036          | 0.3288 |
| 0.261         | 3.43  | 4800  | 0.3095          | 0.2947 |
| 0.2639        | 3.72  | 5200  | 0.2818          | 0.2767 |
| 0.2771        | 4.0   | 5600  | 0.2739          | 0.2812 |
| 0.2343        | 4.29  | 6000  | 0.2820          | 0.2700 |
| 0.2452        | 4.57  | 6400  | 0.2663          | 0.2697 |
| 0.2344        | 4.86  | 6800  | 0.2679          | 0.2666 |
| 0.2215        | 5.15  | 7200  | 0.2687          | 0.2571 |
| 0.2032        | 5.43  | 7600  | 0.2791          | 0.2624 |
| 0.2092        | 5.72  | 8000  | 0.2682          | 0.2616 |
| 0.2122        | 6.0   | 8400  | 0.2770          | 0.2591 |
| 0.1878        | 6.29  | 8800  | 0.2760          | 0.2584 |
| 0.1884        | 6.58  | 9200  | 0.2641          | 0.2515 |
| 0.194         | 6.86  | 9600  | 0.2500          | 0.2415 |
| 0.175         | 7.15  | 10000 | 0.2635          | 0.2532 |
| 0.1658        | 7.43  | 10400 | 0.2588          | 0.2371 |
| 0.177         | 7.72  | 10800 | 0.2813          | 0.2493 |
| 0.1786        | 8.01  | 11200 | 0.2628          | 0.2437 |
| 0.1509        | 8.29  | 11600 | 0.2592          | 0.2453 |
| 0.1597        | 8.58  | 12000 | 0.2737          | 0.2523 |
| 0.1646        | 8.86  | 12400 | 0.2556          | 0.2436 |
| 0.1587        | 9.15  | 12800 | 0.2669          | 0.2453 |
| 0.1489        | 9.44  | 13200 | 0.2596          | 0.2353 |
| 0.1468        | 9.72  | 13600 | 0.2620          | 0.2419 |
| 0.1482        | 10.01 | 14000 | 0.2622          | 0.2334 |
| 0.1285        | 10.29 | 14400 | 0.2531          | 0.2258 |
| 0.1335        | 10.58 | 14800 | 0.2512          | 0.2273 |
| 0.1335        | 10.86 | 15200 | 0.2475          | 0.2246 |
| 0.132         | 11.15 | 15600 | 0.2575          | 0.2275 |
| 0.1249        | 11.44 | 16000 | 0.2503          | 0.2223 |
| 0.1229        | 11.72 | 16400 | 0.2817          | 0.2297 |
| 0.1274        | 12.01 | 16800 | 0.2707          | 0.2211 |
| 0.1115        | 12.29 | 17200 | 0.2647          | 0.2175 |
| 0.117         | 12.58 | 17600 | 0.2501          | 0.2178 |
| 0.1164        | 12.87 | 18000 | 0.2579          | 0.2216 |
| 0.1085        | 13.15 | 18400 | 0.2636          | 0.2130 |
| 0.1033        | 13.44 | 18800 | 0.2643          | 0.2184 |
| 0.1066        | 13.72 | 19200 | 0.2519          | 0.2158 |
| 0.1032        | 14.01 | 19600 | 0.2322          | 0.2082 |
| 0.0981        | 14.3  | 20000 | 0.2613          | 0.2125 |
| 0.1009        | 14.58 | 20400 | 0.2479          | 0.2076 |
| 0.1           | 14.87 | 20800 | 0.2464          | 0.2058 |
| 0.0886        | 15.15 | 21200 | 0.2595          | 0.2014 |
| 0.0888        | 15.44 | 21600 | 0.2565          | 0.2048 |
| 0.0916        | 15.73 | 22000 | 0.2470          | 0.2000 |
| 0.095         | 16.01 | 22400 | 0.2539          | 0.1997 |
| 0.0875        | 16.3  | 22800 | 0.2576          | 0.1995 |
| 0.0833        | 16.58 | 23200 | 0.2514          | 0.1990 |
| 0.0813        | 16.87 | 23600 | 0.2522          | 0.2020 |
| 0.0845        | 17.16 | 24000 | 0.2522          | 0.2045 |
| 0.0879        | 17.44 | 24400 | 0.2629          | 0.2183 |
| 0.0854        | 17.73 | 24800 | 0.2464          | 0.2000 |
| 0.0795        | 18.01 | 25200 | 0.2526          | 0.2078 |
| 0.075         | 18.3  | 25600 | 0.2519          | 0.1971 |
| 0.0724        | 18.58 | 26000 | 0.2551          | 0.1965 |
| 0.0735        | 18.87 | 26400 | 0.2536          | 0.1934 |
| 0.0735        | 19.16 | 26800 | 0.2504          | 0.1916 |
| 0.0676        | 19.44 | 27200 | 0.2532          | 0.1884 |
| 0.0687        | 19.73 | 27600 | 0.2498          | 0.1849 |
| 0.0652        | 20.01 | 28000 | 0.2490          | 0.1847 |
| 0.0617        | 20.3  | 28400 | 0.2547          | 0.1899 |
| 0.0627        | 20.59 | 28800 | 0.2509          | 0.1834 |
| 0.0639        | 20.87 | 29200 | 0.2472          | 0.1812 |
| 0.0611        | 21.16 | 29600 | 0.2486          | 0.1827 |
| 0.0559        | 21.44 | 30000 | 0.2530          | 0.1825 |
| 0.0564        | 21.73 | 30400 | 0.2484          | 0.1785 |
| 0.0593        | 22.02 | 30800 | 0.2425          | 0.1781 |
| 0.0517        | 22.3  | 31200 | 0.2613          | 0.1775 |
| 0.0528        | 22.59 | 31600 | 0.2517          | 0.1759 |
| 0.0556        | 22.87 | 32000 | 0.2494          | 0.1811 |
| 0.0507        | 23.16 | 32400 | 0.2522          | 0.1761 |
| 0.0485        | 23.45 | 32800 | 0.2344          | 0.1717 |
| 0.0504        | 23.73 | 33200 | 0.2458          | 0.1772 |
| 0.0485        | 24.02 | 33600 | 0.2497          | 0.1748 |
| 0.0436        | 24.3  | 34000 | 0.2405          | 0.1738 |
| 0.0468        | 24.59 | 34400 | 0.2446          | 0.1735 |
| 0.0443        | 24.87 | 34800 | 0.2514          | 0.1709 |
| 0.0417        | 25.16 | 35200 | 0.2515          | 0.1711 |
| 0.0399        | 25.45 | 35600 | 0.2452          | 0.1664 |
| 0.0416        | 25.73 | 36000 | 0.2438          | 0.1664 |
| 0.0412        | 26.02 | 36400 | 0.2457          | 0.1662 |
| 0.0406        | 26.3  | 36800 | 0.2475          | 0.1659 |
| 0.0376        | 26.59 | 37200 | 0.2454          | 0.1682 |
| 0.0365        | 26.88 | 37600 | 0.2511          | 0.1650 |
| 0.0355        | 27.16 | 38000 | 0.2518          | 0.1633 |
| 0.032         | 27.45 | 38400 | 0.2479          | 0.1604 |
| 0.0348        | 27.73 | 38800 | 0.2391          | 0.1599 |
| 0.0331        | 28.02 | 39200 | 0.2417          | 0.1617 |
| 0.0349        | 28.31 | 39600 | 0.2358          | 0.1590 |
| 0.0347        | 28.59 | 40000 | 0.2388          | 0.1582 |
| 0.0325        | 28.88 | 40400 | 0.2412          | 0.1564 |
| 0.0332        | 29.16 | 40800 | 0.2390          | 0.1545 |
| 0.0613        | 29.45 | 41200 | 0.0167          | 0.0141 |
| 0.0563        | 29.74 | 41600 | 0.0161          | 0.0141 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.8.1+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
