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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# model |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2220 |
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- Wer: 0.1301 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 5.9743 | 0.18 | 400 | 2.1457 | 1.0000 | |
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| 0.5747 | 0.36 | 800 | 0.3415 | 0.3456 | |
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| 0.3383 | 0.54 | 1200 | 0.2797 | 0.3095 | |
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| 0.2967 | 0.72 | 1600 | 0.2464 | 0.2568 | |
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| 0.2747 | 0.9 | 2000 | 0.2341 | 0.2466 | |
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| 0.2501 | 1.08 | 2400 | 0.2299 | 0.2317 | |
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| 0.2309 | 1.26 | 2800 | 0.2306 | 0.2328 | |
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| 0.2273 | 1.44 | 3200 | 0.2212 | 0.2375 | |
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| 0.225 | 1.62 | 3600 | 0.2193 | 0.2267 | |
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| 0.2204 | 1.8 | 4000 | 0.2157 | 0.2295 | |
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| 0.2256 | 1.98 | 4400 | 0.2165 | 0.2260 | |
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| 0.1941 | 2.17 | 4800 | 0.2105 | 0.2163 | |
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| 0.1925 | 2.35 | 5200 | 0.2098 | 0.2153 | |
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| 0.1925 | 2.53 | 5600 | 0.2120 | 0.2148 | |
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| 0.1952 | 2.71 | 6000 | 0.2063 | 0.2178 | |
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| 0.1971 | 2.89 | 6400 | 0.2100 | 0.2158 | |
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| 0.1888 | 3.07 | 6800 | 0.2131 | 0.2172 | |
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| 0.1702 | 3.25 | 7200 | 0.2155 | 0.2203 | |
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| 0.173 | 3.43 | 7600 | 0.2141 | 0.2254 | |
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| 0.174 | 3.61 | 8000 | 0.2017 | 0.2100 | |
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| 0.1802 | 3.79 | 8400 | 0.1998 | 0.2043 | |
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| 0.1717 | 3.97 | 8800 | 0.2070 | 0.2110 | |
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| 0.162 | 4.15 | 9200 | 0.2082 | 0.2157 | |
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| 0.154 | 4.33 | 9600 | 0.2163 | 0.2161 | |
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| 0.1598 | 4.51 | 10000 | 0.2070 | 0.2171 | |
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| 0.1576 | 4.69 | 10400 | 0.2034 | 0.2116 | |
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| 0.1601 | 4.87 | 10800 | 0.1990 | 0.2009 | |
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| 0.152 | 5.05 | 11200 | 0.1994 | 0.2039 | |
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| 0.1395 | 5.23 | 11600 | 0.2013 | 0.2046 | |
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| 0.1407 | 5.41 | 12000 | 0.2009 | 0.2022 | |
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| 0.1449 | 5.59 | 12400 | 0.1982 | 0.1961 | |
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| 0.1483 | 5.77 | 12800 | 0.2082 | 0.2054 | |
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| 0.1514 | 5.95 | 13200 | 0.1953 | 0.1985 | |
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| 0.138 | 6.13 | 13600 | 0.2046 | 0.1965 | |
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| 0.1322 | 6.31 | 14000 | 0.2076 | 0.1948 | |
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| 0.1372 | 6.5 | 14400 | 0.1968 | 0.1944 | |
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| 0.136 | 6.68 | 14800 | 0.1971 | 0.1963 | |
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| 0.1382 | 6.86 | 15200 | 0.2001 | 0.1990 | |
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| 0.1335 | 7.04 | 15600 | 0.2026 | 0.1935 | |
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| 0.1206 | 7.22 | 16000 | 0.1986 | 0.1938 | |
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| 0.1239 | 7.4 | 16400 | 0.2054 | 0.1919 | |
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| 0.1254 | 7.58 | 16800 | 0.1918 | 0.1939 | |
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| 0.1262 | 7.76 | 17200 | 0.1960 | 0.1947 | |
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| 0.126 | 7.94 | 17600 | 0.1932 | 0.1906 | |
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| 0.1169 | 8.12 | 18000 | 0.2037 | 0.1916 | |
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| 0.1142 | 8.3 | 18400 | 0.1999 | 0.1900 | |
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| 0.1151 | 8.48 | 18800 | 0.1920 | 0.1855 | |
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| 0.1121 | 8.66 | 19200 | 0.2007 | 0.1859 | |
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| 0.1135 | 8.84 | 19600 | 0.1932 | 0.1879 | |
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| 0.1158 | 9.02 | 20000 | 0.1916 | 0.1859 | |
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| 0.105 | 9.2 | 20400 | 0.1961 | 0.1831 | |
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| 0.1023 | 9.38 | 20800 | 0.1914 | 0.1791 | |
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| 0.1004 | 9.56 | 21200 | 0.1881 | 0.1787 | |
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| 0.1023 | 9.74 | 21600 | 0.1963 | 0.1817 | |
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| 0.1075 | 9.92 | 22000 | 0.1889 | 0.1861 | |
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| 0.103 | 10.1 | 22400 | 0.1975 | 0.1791 | |
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| 0.0952 | 10.28 | 22800 | 0.1979 | 0.1787 | |
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| 0.0957 | 10.46 | 23200 | 0.1922 | 0.1817 | |
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| 0.0966 | 10.65 | 23600 | 0.1953 | 0.1857 | |
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| 0.0997 | 10.83 | 24000 | 0.1902 | 0.1783 | |
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| 0.0981 | 11.01 | 24400 | 0.1959 | 0.1780 | |
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| 0.0868 | 11.19 | 24800 | 0.2056 | 0.1783 | |
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| 0.0905 | 11.37 | 25200 | 0.1958 | 0.1777 | |
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| 0.0892 | 11.55 | 25600 | 0.1935 | 0.1796 | |
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| 0.0891 | 11.73 | 26000 | 0.1968 | 0.1763 | |
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| 0.0888 | 11.91 | 26400 | 0.2043 | 0.1804 | |
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| 0.0842 | 12.09 | 26800 | 0.2043 | 0.1733 | |
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| 0.0828 | 12.27 | 27200 | 0.1964 | 0.1715 | |
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| 0.0827 | 12.45 | 27600 | 0.1991 | 0.1749 | |
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| 0.0844 | 12.63 | 28000 | 0.2014 | 0.1695 | |
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| 0.0837 | 12.81 | 28400 | 0.1973 | 0.1759 | |
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| 0.0872 | 12.99 | 28800 | 0.1975 | 0.1689 | |
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| 0.0778 | 13.17 | 29200 | 0.1979 | 0.1740 | |
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| 0.0759 | 13.35 | 29600 | 0.2093 | 0.1753 | |
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| 0.076 | 13.53 | 30000 | 0.1990 | 0.1731 | |
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| 0.0762 | 13.71 | 30400 | 0.2024 | 0.1690 | |
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| 0.0764 | 13.89 | 30800 | 0.2037 | 0.1709 | |
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| 0.0756 | 14.07 | 31200 | 0.2007 | 0.1716 | |
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| 0.0702 | 14.25 | 31600 | 0.2011 | 0.1680 | |
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| 0.0694 | 14.43 | 32000 | 0.2061 | 0.1683 | |
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| 0.0713 | 14.61 | 32400 | 0.2014 | 0.1687 | |
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| 0.0693 | 14.79 | 32800 | 0.1961 | 0.1658 | |
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| 0.071 | 14.98 | 33200 | 0.1921 | 0.1645 | |
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| 0.0659 | 15.16 | 33600 | 0.2079 | 0.1682 | |
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| 0.0659 | 15.34 | 34000 | 0.2046 | 0.1649 | |
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| 0.0685 | 15.52 | 34400 | 0.1994 | 0.1660 | |
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| 0.0663 | 15.7 | 34800 | 0.1970 | 0.1652 | |
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| 0.0678 | 15.88 | 35200 | 0.1961 | 0.1634 | |
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| 0.0644 | 16.06 | 35600 | 0.2141 | 0.1644 | |
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| 0.0596 | 16.24 | 36000 | 0.2098 | 0.1628 | |
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| 0.0629 | 16.42 | 36400 | 0.1969 | 0.1616 | |
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| 0.0598 | 16.6 | 36800 | 0.2026 | 0.1604 | |
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| 0.0628 | 16.78 | 37200 | 0.2050 | 0.1620 | |
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| 0.0616 | 16.96 | 37600 | 0.1958 | 0.1618 | |
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| 0.0538 | 17.14 | 38000 | 0.2093 | 0.1588 | |
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| 0.0573 | 17.32 | 38400 | 0.1995 | 0.1588 | |
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| 0.0555 | 17.5 | 38800 | 0.2077 | 0.1608 | |
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| 0.0555 | 17.68 | 39200 | 0.2036 | 0.1571 | |
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| 0.0578 | 17.86 | 39600 | 0.2045 | 0.1572 | |
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| 0.056 | 18.04 | 40000 | 0.2065 | 0.1593 | |
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| 0.0525 | 18.22 | 40400 | 0.2093 | 0.1580 | |
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| 0.0527 | 18.4 | 40800 | 0.2141 | 0.1585 | |
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| 0.0529 | 18.58 | 41200 | 0.2137 | 0.1585 | |
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| 0.0533 | 18.76 | 41600 | 0.2021 | 0.1558 | |
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| 0.0529 | 18.94 | 42000 | 0.2108 | 0.1535 | |
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| 0.05 | 19.12 | 42400 | 0.2114 | 0.1555 | |
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| 0.0479 | 19.31 | 42800 | 0.2091 | 0.1549 | |
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| 0.0509 | 19.49 | 43200 | 0.2145 | 0.1554 | |
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| 0.0486 | 19.67 | 43600 | 0.2061 | 0.1536 | |
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| 0.049 | 19.85 | 44000 | 0.2132 | 0.1548 | |
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| 0.0484 | 20.03 | 44400 | 0.2077 | 0.1523 | |
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| 0.0449 | 20.21 | 44800 | 0.2177 | 0.1529 | |
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| 0.0452 | 20.39 | 45200 | 0.2204 | 0.1517 | |
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| 0.0477 | 20.57 | 45600 | 0.2132 | 0.1517 | |
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| 0.048 | 20.75 | 46000 | 0.2119 | 0.1532 | |
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| 0.0469 | 20.93 | 46400 | 0.2109 | 0.1524 | |
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| 0.0439 | 21.11 | 46800 | 0.2118 | 0.1503 | |
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| 0.044 | 21.29 | 47200 | 0.2033 | 0.1474 | |
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| 0.0435 | 21.47 | 47600 | 0.2066 | 0.1485 | |
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| 0.0418 | 21.65 | 48000 | 0.2125 | 0.1491 | |
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| 0.0417 | 21.83 | 48400 | 0.2139 | 0.1487 | |
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| 0.0446 | 22.01 | 48800 | 0.2054 | 0.1493 | |
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| 0.039 | 22.19 | 49200 | 0.2179 | 0.1459 | |
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| 0.0414 | 22.37 | 49600 | 0.2118 | 0.1466 | |
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| 0.0394 | 22.55 | 50000 | 0.2104 | 0.1444 | |
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| 0.0381 | 22.73 | 50400 | 0.2095 | 0.1458 | |
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| 0.0382 | 22.91 | 50800 | 0.2193 | 0.1471 | |
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| 0.0391 | 23.09 | 51200 | 0.2143 | 0.1455 | |
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| 0.0365 | 23.27 | 51600 | 0.2198 | 0.1445 | |
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| 0.0368 | 23.46 | 52000 | 0.2151 | 0.1444 | |
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| 0.038 | 23.64 | 52400 | 0.2094 | 0.1439 | |
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| 0.038 | 23.82 | 52800 | 0.2137 | 0.1422 | |
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| 0.0374 | 24.0 | 53200 | 0.2180 | 0.1425 | |
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| 0.0352 | 24.18 | 53600 | 0.2207 | 0.1422 | |
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| 0.0343 | 24.36 | 54000 | 0.2269 | 0.1445 | |
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| 0.0353 | 24.54 | 54400 | 0.2222 | 0.1438 | |
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| 0.0348 | 24.72 | 54800 | 0.2224 | 0.1413 | |
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| 0.0342 | 24.9 | 55200 | 0.2146 | 0.1401 | |
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| 0.0337 | 25.08 | 55600 | 0.2246 | 0.1408 | |
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| 0.0327 | 25.26 | 56000 | 0.2161 | 0.1401 | |
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| 0.0339 | 25.44 | 56400 | 0.2212 | 0.1402 | |
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| 0.0324 | 25.62 | 56800 | 0.2203 | 0.1394 | |
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| 0.0319 | 25.8 | 57200 | 0.2145 | 0.1376 | |
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| 0.0317 | 25.98 | 57600 | 0.2147 | 0.1375 | |
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| 0.0302 | 26.16 | 58000 | 0.2213 | 0.1362 | |
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| 0.0309 | 26.34 | 58400 | 0.2218 | 0.1365 | |
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| 0.0308 | 26.52 | 58800 | 0.2167 | 0.1362 | |
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| 0.0294 | 26.7 | 59200 | 0.2169 | 0.1368 | |
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| 0.0297 | 26.88 | 59600 | 0.2163 | 0.1350 | |
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| 0.0289 | 27.06 | 60000 | 0.2188 | 0.1348 | |
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| 0.0284 | 27.24 | 60400 | 0.2172 | 0.1338 | |
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| 0.0278 | 27.42 | 60800 | 0.2230 | 0.1342 | |
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| 0.0283 | 27.6 | 61200 | 0.2233 | 0.1342 | |
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| 0.0292 | 27.79 | 61600 | 0.2238 | 0.1335 | |
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| 0.0286 | 27.97 | 62000 | 0.2218 | 0.1327 | |
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| 0.0262 | 28.15 | 62400 | 0.2220 | 0.1324 | |
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| 0.0274 | 28.33 | 62800 | 0.2182 | 0.1323 | |
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| 0.0279 | 28.51 | 63200 | 0.2170 | 0.1314 | |
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| 0.0269 | 28.69 | 63600 | 0.2228 | 0.1313 | |
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| 0.0264 | 28.87 | 64000 | 0.2209 | 0.1313 | |
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| 0.0254 | 29.05 | 64400 | 0.2224 | 0.1304 | |
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| 0.026 | 29.23 | 64800 | 0.2220 | 0.1302 | |
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| 0.0253 | 29.41 | 65200 | 0.2229 | 0.1304 | |
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| 0.0244 | 29.59 | 65600 | 0.2217 | 0.1298 | |
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| 0.025 | 29.77 | 66000 | 0.2223 | 0.1303 | |
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| 0.0255 | 29.95 | 66400 | 0.2220 | 0.1301 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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