readme: ---
  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.