Model Overview
Model Features
Model Capabilities
Use Cases
đ wav2vec2-bert-CV16-en
This model is a fine - tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA - FOUNDATION/COMMON_VOICE_16_0 - EN dataset. It's designed for automatic speech recognition, aiming to provide more accurate speech - to - text conversion.
đ Quick Start
This model can be used for automatic speech recognition tasks. You can fine - tune it on your own dataset or use it directly for inference.
⨠Features
- Fine - tuned: Based on the pre - trained model ylacombe/w2v-bert-2.0, it's fine - tuned on the MOZILLA - FOUNDATION/COMMON_VOICE_16_0 - EN dataset.
- Performance Metrics: Achieves good results on evaluation set, with Loss: 0.2427, Wer: 0.1455, and Cer: 0.0580.
đĻ Installation
The installation steps are not provided in the original README. You may need to refer to the official documentation of the relevant framework (e.g., Hugging Face Transformers) to install the necessary libraries for using this model.
đ Documentation
Model description
This model is a fine - tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA - FOUNDATION/COMMON_VOICE_16_0 - EN dataset.
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: 5e - 05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi - GPU
- num_devices: 3
- total_train_batch_size: 36
- total_eval_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
2.9554 | 0.01 | 250 | 3.1731 | 0.9999 | 0.9942 |
2.7058 | 0.02 | 500 | 2.6717 | 1.0307 | 0.7486 |
0.9641 | 0.02 | 750 | 0.9895 | 0.6091 | 0.2035 |
0.6935 | 0.03 | 1000 | 0.7740 | 0.4821 | 0.1562 |
0.617 | 0.04 | 1250 | 0.6751 | 0.4008 | 0.1303 |
0.4826 | 0.05 | 1500 | 0.5920 | 0.3499 | 0.1170 |
0.4252 | 0.06 | 1750 | 0.5659 | 0.3056 | 0.1053 |
0.472 | 0.07 | 2000 | 0.5066 | 0.2869 | 0.1007 |
0.4042 | 0.07 | 2250 | 0.4604 | 0.2662 | 0.0950 |
0.4279 | 0.08 | 2500 | 0.5165 | 0.2587 | 0.0948 |
0.3586 | 0.09 | 2750 | 0.4440 | 0.2461 | 0.0895 |
0.2715 | 0.1 | 3000 | 0.5096 | 0.2468 | 0.0904 |
0.413 | 0.11 | 3250 | 0.4416 | 0.2350 | 0.0879 |
0.3142 | 0.11 | 3500 | 0.4591 | 0.2280 | 0.0856 |
0.286 | 0.12 | 3750 | 0.4529 | 0.2284 | 0.0860 |
0.3112 | 0.13 | 4000 | 0.4621 | 0.2320 | 0.0875 |
0.3294 | 0.14 | 4250 | 0.4528 | 0.2294 | 0.0862 |
0.3522 | 0.15 | 4500 | 0.4279 | 0.2287 | 0.0861 |
0.2977 | 0.15 | 4750 | 0.4403 | 0.2200 | 0.0830 |
0.2391 | 0.16 | 5000 | 0.4360 | 0.2161 | 0.0831 |
0.3025 | 0.17 | 5250 | 0.4214 | 0.2157 | 0.0831 |
0.309 | 0.18 | 5500 | 0.4060 | 0.2125 | 0.0818 |
0.2872 | 0.19 | 5750 | 0.4233 | 0.2189 | 0.0824 |
0.2796 | 0.2 | 6000 | 0.4055 | 0.2151 | 0.0823 |
0.2609 | 0.2 | 6250 | 0.4374 | 0.2194 | 0.0853 |
0.283 | 0.21 | 6500 | 0.4288 | 0.2215 | 0.0877 |
0.3028 | 0.22 | 6750 | 0.4180 | 0.2166 | 0.0837 |
0.2565 | 0.23 | 7000 | 0.4476 | 0.2268 | 0.0892 |
0.2824 | 0.24 | 7250 | 0.4057 | 0.2195 | 0.0850 |
0.325 | 0.24 | 7500 | 0.3926 | 0.2157 | 0.0849 |
0.336 | 0.25 | 7750 | 0.4469 | 0.2208 | 0.0879 |
0.304 | 0.26 | 8000 | 0.4292 | 0.2245 | 0.0886 |
0.2457 | 0.27 | 8250 | 0.4198 | 0.2204 | 0.0856 |
0.2768 | 0.28 | 8500 | 0.4330 | 0.2184 | 0.0859 |
0.2165 | 0.29 | 8750 | 0.4276 | 0.2173 | 0.0864 |
0.3015 | 0.29 | 9000 | 0.4255 | 0.2223 | 0.0882 |
0.308 | 0.3 | 9250 | 0.4356 | 0.2318 | 0.0925 |
0.2981 | 0.31 | 9500 | 0.4514 | 0.2226 | 0.0884 |
0.2944 | 0.32 | 9750 | 0.4182 | 0.2293 | 0.0901 |
0.3298 | 0.33 | 10000 | 0.4290 | 0.2275 | 0.0892 |
0.2523 | 0.33 | 10250 | 0.4032 | 0.2191 | 0.0865 |
0.2887 | 0.34 | 10500 | 0.4218 | 0.2284 | 0.0917 |
0.3156 | 0.35 | 10750 | 0.3930 | 0.2271 | 0.0898 |
0.2526 | 0.36 | 11000 | 0.4367 | 0.2304 | 0.0928 |
0.2561 | 0.37 | 11250 | 0.4261 | 0.2279 | 0.0916 |
0.2291 | 0.37 | 11500 | 0.4401 | 0.2231 | 0.0899 |
0.2521 | 0.38 | 11750 | 0.4101 | 0.2232 | 0.0895 |
0.2249 | 0.39 | 12000 | 0.4021 | 0.2270 | 0.0913 |
0.2917 | 0.4 | 12250 | 0.4124 | 0.2267 | 0.0915 |
0.2436 | 0.41 | 12500 | 0.4197 | 0.2257 | 0.0903 |
0.2976 | 0.42 | 12750 | 0.3951 | 0.2230 | 0.0896 |
0.2333 | 0.42 | 13000 | 0.4099 | 0.2250 | 0.0901 |
0.2261 | 0.43 | 13250 | 0.4328 | 0.2168 | 0.0876 |
0.2514 | 0.44 | 13500 | 0.3947 | 0.2208 | 0.0895 |
0.296 | 0.45 | 13750 | 0.3953 | 0.2149 | 0.0859 |
0.2426 | 0.46 | 14000 | 0.3831 | 0.2119 | 0.0852 |
0.2258 | 0.46 | 14250 | 0.4060 | 0.2263 | 0.0915 |
0.2565 | 0.47 | 14500 | 0.4057 | 0.2237 | 0.0901 |
0.2834 | 0.48 | 14750 | 0.4112 | 0.2167 | 0.0876 |
0.234 | 0.49 | 15000 | 0.3802 | 0.2133 | 0.0852 |
0.3084 | 0.5 | 15250 | 0.3837 | 0.2151 | 0.0871 |
0.3051 | 0.51 | 15500 | 0.3848 | 0.2145 | 0.0867 |
0.2364 | 0.51 | 15750 | 0.3817 | 0.2134 | 0.0870 |
0.2345 | 0.52 | 16000 | 0.3883 | 0.2163 | 0.0874 |
0.2235 | 0.53 | 16250 | 0.3740 | 0.2136 | 0.0869 |
0.2365 | 0.54 | 16500 | 0.3711 | 0.2112 | 0.0850 |
0.2449 | 0.55 | 16750 | 0.3805 | 0.2127 | 0.0858 |
0.2569 | 0.55 | 17000 | 0.3794 | 0.2124 | 0.0863 |
0.2273 | 0.56 | 17250 | 0.3922 | 0.2207 | 0.0895 |
0.2492 | 0.57 | 17500 | 0.3670 | 0.2195 | 0.0874 |
0.236 | 0.58 | 17750 | 0.3799 | 0.2120 | 0.0862 |
0.2823 | 0.59 | 18000 | 0.3734 | 0.2144 | 0.0867 |
0.2349 | 0.59 | 18250 | 0.3972 | 0.2175 | 0.0889 |
0.2156 | 0.6 | 18500 | 0.3729 | 0.2157 | 0.0867 |
0.2812 | 0.61 | 18750 | 0.3905 | 0.2117 | 0.0854 |
0.242 | 0.62 | 19000 | 0.3912 | 0.2114 | 0.0855 |
0.2237 | 0.63 | 19250 | 0.3794 | 0.2155 | 0.0877 |
0.255 | 0.64 | 19500 | 0.3770 | 0.2079 | 0.0840 |
0.1899 | 0.64 | 19750 | 0.3796 | 0.2145 | 0.0868 |
0.2793 | 0.65 | 20000 | 0.3784 | 0.2145 | 0.0863 |
0.2099 | 0.66 | 20250 | 0.3956 | 0.2161 | 0.0875 |
0.22 | 0.67 | 20500 | 0.3804 | 0.2135 | 0.0875 |
0.2213 | 0.68 | 20750 | 0.3803 | 0.2100 | 0.0849 |
0.245 | 0.68 | 21000 | 0.3783 | 0.2142 | 0.0870 |
0.2188 | 0.69 | 21250 | 0.3873 | 0.2163 | 0.0861 |
0.2613 | 0.7 | 21500 | 0.3646 | 0.2105 | 0.0844 |
0.1907 | 0.71 | 21750 | 0.3830 | 0.2101 | 0.0853 |
0.2095 | 0.72 | 22000 | 0.3794 | 0.2087 | 0.0849 |
0.2319 | 0.73 | 22250 | 0.3548 | 0.2087 | 0.0842 |
0.2049 | 0.73 | 22500 | 0.3782 | 0.2075 | 0.0837 |
0.2248 | 0.74 | 22750 | 0.3736 | 0.2100 | 0.0845 |
0.2277 | 0.75 | 23000 | 0.3712 | 0.2105 | 0.0845 |
0.2115 | 0.76 | 23250 | 0.3722 | 0.2124 | 0.0859 |
0.2001 | 0.77 | 23500 | 0.3602 | 0.2072 | 0.0832 |
0.2095 | 0.77 | 23750 | 0.3607 | 0.2106 | 0.0851 |
0.2286 | 0.78 | 24000 | 0.3810 | 0.2132 | 0.0876 |
0.2284 | 0.79 | 24250 | 0.3677 | 0.2066 | 0.0847 |
0.2003 | 0.8 | 24500 | 0.3650 | 0.2098 | 0.0847 |
0.1992 | 0.81 | 24750 | 0.3491 | 0.2019 | 0.0813 |
0.224 | 0.81 | 25000 | 0.3602 | 0.2043 | 0.0825 |
0.2181 | 0.82 | 25250 | 0.3712 | 0.2120 | 0.0867 |
0.2226 | 0.83 | 25500 | 0.3657 | 0.2028 | 0.0830 |
0.1912 | 0.84 | 25750 | 0.3662 | 0.2076 | 0.0846 |
0.2283 | 0.85 | 26000 | 0.3505 | 0.2049 | 0.0825 |
0.2068 | 0.86 | 26250 | 0.3622 | 0.2111 | 0.0852 |
0.2444 | 0.86 | 26500 | 0.3660 | 0.2055 | 0.0840 |
0.2055 | 0.87 | 26750 | 0.3625 | 0.2055 | 0.0830 |
0.2074 | 0.88 | 27000 | 0.3566 | 0.1981 | 0.0812 |
0.2019 | 0.89 | 27250 | 0.3537 | 0.2038 | 0.0822 |
0.2174 | 0.9 | 27500 | 0.3664 | 0.1990 | 0.0809 |
0.2009 | 0.9 | 27750 | 0.3512 | 0.2035 | 0.0821 |
0.211 | 0.91 | 28000 | 0.3707 | 0.2068 | 0.0846 |
0.2541 | 0.92 | 28250 | 0.3435 | 0.1992 | 0.0812 |
0.2108 | 0.93 | 28500 | 0.3461 | 0.2046 | 0.0828 |
0.2274 | 0.94 | 28750 | 0.3364 | 0.1998 | 0.0812 |
0.2175 | 0.95 | 29000 | 0.3742 | 0.2113 | 0.0864 |
0.2368 | 0.95 | 29250 | 0.3431 | 0.2051 | 0.0833 |
0.1831 | 0.96 | 29500 | 0.3468 | 0.2034 | 0.0825 |
0.2202 | 0.97 | 29750 | 0.3342 | 0.1964 | 0.0791 |
0.183 | 0.98 | 30000 | 0.3413 | 0.1966 | 0.0792 |
0.1958 | 0.99 | 30250 | 0.3466 | 0.1991 | 0.0809 |
0.2167 | 0.99 | 30500 | 0.3530 | 0.2024 | 0.0816 |
0.2057 | 1.0 | 30750 | 0.3334 | 0.1960 | 0.0788 |
0.1982 | 1.01 | 31000 | 0.3312 | 0.1951 | 0.0789 |
0.2123 | 1.02 | 31250 | 0.3285 | 0.1955 | 0.0785 |
0.2269 | 1.03 | 31500 | 0.3548 | 0.2034 | 0.0812 |
0.2056 | 1.03 | 31750 | 0.3433 | 0.1969 | 0.0793 |
0.2234 | 1.04 | 32000 | 0.3446 | 0.1981 | 0.0805 |
0.1913 | 1.05 | 32250 | 0.3465 | 0.1969 | 0.0792 |
0.2005 | 1.06 | 32500 | 0.3348 | 0.1947 | 0.0784 |
0.2017 | 1.07 | 32750 | 0.3567 | 0.1972 | 0.0796 |
0.2523 | 1.08 | 33000 | 0.3367 | 0.1971 | 0.0801 |
0.1716 | 1.08 | 33250 | 0.3476 | 0.1975 | 0.0799 |
0.168 | 1.09 | 33500 | 0.3346 | 0.1951 | 0.0790 |
0.1995 | 1.1 | 33750 | 0.3564 | 0.1971 | 0.0794 |
0.198 | 1.11 | 34000 | 0.3409 | 0.1988 | 0.0796 |
0.1801 | 1.12 | 34250 | 0.3303 | 0.1995 | 0.0798 |
0.181 | 1.12 | 34500 | 0.3363 | 0.1967 | 0.0794 |
0.1966 | 1.13 | 34750 | 0.3375 | 0.1947 | 0.0784 |
0.2163 | 1.14 | 35000 | 0.3441 | 0.2011 | 0.0810 |
0.2285 | 1.15 | 35250 | 0.3303 | 0.1972 | 0.0801 |
0.1814 | 1.16 | 35500 | 0.3462 | 0.1895 | 0.0772 |
0.2127 | 1.17 | 35750 | 0.3393 | 0.1904 | 0.0775 |
0.1795 | 1.17 | 36000 | 0.3374 | 0.1928 | 0.0780 |
0.2062 | 1.18 | 36250 | 0.3286 | 0.1929 | 0.0783 |
0.172 | 1.19 | 36500 | 0.3334 | 0.1929 | 0.0781 |
0.1534 | 1.2 | 36750 | 0.3287 | 0.1895 | 0.0763 |
0.2101 | 1.21 | 37000 | 0.3261 | 0.1888 | 0.0764 |
0.2342 | 1.21 | 37250 | 0.3413 | 0.2007 | 0.0812 |
0.1692 | 1.22 | 37500 | 0.3375 | 0.1932 | 0.0780 |
0.165 | 1.23 | 37750 | 0.3220 | 0.1903 | 0.0767 |
0.2067 | 1.24 | 38000 | 0.3212 | 0.1855 | 0.0754 |
0.1984 | 1.25 | 38250 | 0.3339 | 0.1890 | 0.0762 |
0.2117 | 1.25 | 38500 | 0.3224 | 0.1900 | 0.0761 |
0.2036 | 1.26 | 38750 | 0.3410 | 0.1923 | 0.0790 |
0.2072 | 1.27 | 39000 | 0.3291 | 0.1904 | 0.0770 |
0.1962 | 1.28 | 39250 | 0.3237 | 0.1908 | 0.0770 |
0.2055 | 1.29 | 39500 | 0.3260 | 0.1896 | 0.0767 |
0.1753 | 1.3 | 39750 | 0.3375 | 0.1915 | 0.0777 |
0.1983 | 1.3 | 40000 | 0.3236 | 0.1850 | 0.0750 |
0.173 | 1.31 | 40250 | 0.3253 | 0.1870 | 0.0754 |
0.1773 | 1.32 | 40500 | 0.3316 | 0.1923 | 0.0766 |
0.1649 | 1.33 | 40750 | 0.3218 | 0.1842 | 0.0749 |
0.1806 | 1.34 | 41000 | 0.3161 | 0.1907 | 0.0769 |
0.1639 | ... (The table continues) | ... | ... | ... | ... |
đ§ Technical Details
The model is based on the pre - trained model ylacombe/w2v-bert-2.0 and fine - tuned on the MOZILLA - FOUNDATION/COMMON_VOICE_16_0 - EN dataset. The training process uses a series of hyperparameters to optimize the model's performance.
đ License
The license information is not provided in the original README. You may need to check the official repository for relevant license details.

