readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: 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. -->
  
  # 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: 0.2220
  - Wer: 0.1301
  
  ## 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
  - 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.9743        | 0.18  | 400   | 2.1457          | 1.0000 |
  | 0.5747        | 0.36  | 800   | 0.3415          | 0.3456 |
  | 0.3383        | 0.54  | 1200  | 0.2797          | 0.3095 |
  | 0.2967        | 0.72  | 1600  | 0.2464          | 0.2568 |
  | 0.2747        | 0.9   | 2000  | 0.2341          | 0.2466 |
  | 0.2501        | 1.08  | 2400  | 0.2299          | 0.2317 |
  | 0.2309        | 1.26  | 2800  | 0.2306          | 0.2328 |
  | 0.2273        | 1.44  | 3200  | 0.2212          | 0.2375 |
  | 0.225         | 1.62  | 3600  | 0.2193          | 0.2267 |
  | 0.2204        | 1.8   | 4000  | 0.2157          | 0.2295 |
  | 0.2256        | 1.98  | 4400  | 0.2165          | 0.2260 |
  | 0.1941        | 2.17  | 4800  | 0.2105          | 0.2163 |
  | 0.1925        | 2.35  | 5200  | 0.2098          | 0.2153 |
  | 0.1925        | 2.53  | 5600  | 0.2120          | 0.2148 |
  | 0.1952        | 2.71  | 6000  | 0.2063          | 0.2178 |
  | 0.1971        | 2.89  | 6400  | 0.2100          | 0.2158 |
  | 0.1888        | 3.07  | 6800  | 0.2131          | 0.2172 |
  | 0.1702        | 3.25  | 7200  | 0.2155          | 0.2203 |
  | 0.173         | 3.43  | 7600  | 0.2141          | 0.2254 |
  | 0.174         | 3.61  | 8000  | 0.2017          | 0.2100 |
  | 0.1802        | 3.79  | 8400  | 0.1998          | 0.2043 |
  | 0.1717        | 3.97  | 8800  | 0.2070          | 0.2110 |
  | 0.162         | 4.15  | 9200  | 0.2082          | 0.2157 |
  | 0.154         | 4.33  | 9600  | 0.2163          | 0.2161 |
  | 0.1598        | 4.51  | 10000 | 0.2070          | 0.2171 |
  | 0.1576        | 4.69  | 10400 | 0.2034          | 0.2116 |
  | 0.1601        | 4.87  | 10800 | 0.1990          | 0.2009 |
  | 0.152         | 5.05  | 11200 | 0.1994          | 0.2039 |
  | 0.1395        | 5.23  | 11600 | 0.2013          | 0.2046 |
  | 0.1407        | 5.41  | 12000 | 0.2009          | 0.2022 |
  | 0.1449        | 5.59  | 12400 | 0.1982          | 0.1961 |
  | 0.1483        | 5.77  | 12800 | 0.2082          | 0.2054 |
  | 0.1514        | 5.95  | 13200 | 0.1953          | 0.1985 |
  | 0.138         | 6.13  | 13600 | 0.2046          | 0.1965 |
  | 0.1322        | 6.31  | 14000 | 0.2076          | 0.1948 |
  | 0.1372        | 6.5   | 14400 | 0.1968          | 0.1944 |
  | 0.136         | 6.68  | 14800 | 0.1971          | 0.1963 |
  | 0.1382        | 6.86  | 15200 | 0.2001          | 0.1990 |
  | 0.1335        | 7.04  | 15600 | 0.2026          | 0.1935 |
  | 0.1206        | 7.22  | 16000 | 0.1986          | 0.1938 |
  | 0.1239        | 7.4   | 16400 | 0.2054          | 0.1919 |
  | 0.1254        | 7.58  | 16800 | 0.1918          | 0.1939 |
  | 0.1262        | 7.76  | 17200 | 0.1960          | 0.1947 |
  | 0.126         | 7.94  | 17600 | 0.1932          | 0.1906 |
  | 0.1169        | 8.12  | 18000 | 0.2037          | 0.1916 |
  | 0.1142        | 8.3   | 18400 | 0.1999          | 0.1900 |
  | 0.1151        | 8.48  | 18800 | 0.1920          | 0.1855 |
  | 0.1121        | 8.66  | 19200 | 0.2007          | 0.1859 |
  | 0.1135        | 8.84  | 19600 | 0.1932          | 0.1879 |
  | 0.1158        | 9.02  | 20000 | 0.1916          | 0.1859 |
  | 0.105         | 9.2   | 20400 | 0.1961          | 0.1831 |
  | 0.1023        | 9.38  | 20800 | 0.1914          | 0.1791 |
  | 0.1004        | 9.56  | 21200 | 0.1881          | 0.1787 |
  | 0.1023        | 9.74  | 21600 | 0.1963          | 0.1817 |
  | 0.1075        | 9.92  | 22000 | 0.1889          | 0.1861 |
  | 0.103         | 10.1  | 22400 | 0.1975          | 0.1791 |
  | 0.0952        | 10.28 | 22800 | 0.1979          | 0.1787 |
  | 0.0957        | 10.46 | 23200 | 0.1922          | 0.1817 |
  | 0.0966        | 10.65 | 23600 | 0.1953          | 0.1857 |
  | 0.0997        | 10.83 | 24000 | 0.1902          | 0.1783 |
  | 0.0981        | 11.01 | 24400 | 0.1959     