--- license: apache-2.0 language: fi metrics: - wer - cer tags: - generated_from_trainer - automatic-speech-recognition - fi - finnish - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-300m-finnish-lm results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 8.16 - name: Test CER type: cer value: 1.97 --- # wav2vec2-xlsr-300m-finnish-lm This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. It achieves the following results on the Common Voice 7 test set together with language model (Finnish KenLM): - Wer: 8.16 - Cer: 1.97 ## Model description TODO ## Intended uses & limitations TODO ## Training and evaluation data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:| | [Common Voice 7.0 Finnish train+evaluation+other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.973 | 0.17 | 500 | 0.5750 | 0.6844 | | 0.713 | 0.34 | 1000 | 0.3356 | 0.4518 | | 0.6563 | 0.5 | 1500 | 0.3007 | 0.4039 | | 0.642 | 0.67 | 2000 | 0.2619 | 0.3674 | | 0.6203 | 0.84 | 2500 | 0.2488 | 0.3558 | | 0.6016 | 1.01 | 3000 | 0.2795 | 0.3835 | | 0.5423 | 1.17 | 3500 | 0.2652 | 0.3310 | | 0.5639 | 1.34 | 4000 | 0.2479 | 0.3462 | | 0.586 | 1.51 | 4500 | 0.2409 | 0.3295 | | 0.5169 | 1.68 | 5000 | 0.2728 | 0.3352 | | 0.5176 | 1.84 | 5500 | 0.2254 | 0.3149 | | 0.4983 | 2.01 | 6000 | 0.2169 | 0.3009 | | 0.4982 | 2.18 | 6500 | 0.2215 | 0.3079 | | 0.4898 | 2.35 | 7000 | 0.2174 | 0.3023 | | 0.4922 | 2.51 | 7500 | 0.2217 | 0.3081 | | 0.5025 | 2.68 | 8000 | 0.2002 | 0.2710 | | 0.4745 | 2.85 | 8500 | 0.1935 | 0.2783 | | 0.4377 | 3.02 | 9000 | 0.1859 | 0.2742 | | 0.4511 | 3.18 | 9500 | 0.2038 | 0.2786 | | 0.4411 | 3.35 | 10000 | 0.1863 | 0.2651 | | 0.4501 | 3.52 | 10500 | 0.1948 | 0.2605 | | 0.4557 | 3.69 | 11000 | 0.1872 | 0.2695 | | 0.4493 | 3.85 | 11500 | 0.1888 | 0.2632 | | 0.4047 | 4.02 | 12000 | 0.1818 | 0.2559 | | 0.4319 | 4.19 | 12500 | 0.1896 | 0.2648 | | 0.4162 | 4.36 | 13000 | 0.1953 | 0.2595 | | 0.4046 | 4.52 | 13500 | 0.1864 | 0.2606 | | 0.4195 | 4.69 | 14000 | 0.1843 | 0.2467 | | 0.4146 | 4.86 | 14500 | 0.1686 | 0.2450 | | 0.378 | 5.03 | 15000 | 0.1731 | 0.2401 | | 0.3792 | 5.19 | 15500 | 0.1676 | 0.2325 | | 0.3855 | 5.36 | 16000 | 0.1740 | 0.2326 | | 0.4029 | 5.53 | 16500 | 0.1674 | 0.2345 | | 0.386 | 5.7 | 17000 | 0.1735 | 0.2280 | | 0.3811 | 5.86 | 17500 | 0.1692 | 0.2258 | | 0.3607 | 6.03 | 18000 | 0.1797 | 0.2279 | | 0.3604 | 6.2 | 18500 | 0.1651 | 0.2206 | | 0.3362 | 6.37 | 19000 | 0.1627 | 0.2199 | | 0.3611 | 6.53 | 19500 | 0.1652 | 0.2172 | | 0.3671 | 6.7 | 20000 | 0.1564 | 0.2140 | | 0.3769 | 6.87 | 20500 | 0.1525 | 0.2101 | | 0.3539 | 7.04 | 21000 | 0.1639 | 0.2096 | | 0.3225 | 7.21 | 21500 | 0.1611 | 0.2087 | | 0.3323 | 7.37 | 22000 | 0.1633 | 0.2008 | | 0.3327 | 7.54 | 22500 | 0.1692 | 0.1975 | | 0.3456 | 7.71 | 23000 | 0.1555 | 0.1991 | | 0.3058 | 7.88 | 23500 | 0.1590 | 0.1959 | | 0.3034 | 8.04 | 24000 | 0.1531 | 0.1973 | | 0.2925 | 8.21 | 24500 | 0.1583 | 0.1978 | | 0.2967 | 8.38 | 25000 | 0.1546 | 0.1906 | | 0.2974 | 8.55 | 25500 | 0.1540 | 0.1869 | | 0.3131 | 8.71 | 26000 | 0.1534 | 0.1850 | | 0.3306 | 8.88 | 26500 | 0.1482 | 0.1844 | | 0.2842 | 9.05 | 27000 | 0.1490 | 0.1854 | | 0.2879 | 9.22 | 27500 | 0.1463 | 0.1799 | | 0.27 | 9.38 | 28000 | 0.1454 | 0.1798 | | 0.2874 | 9.55 | 28500 | 0.1504 | 0.1787 | | 0.2757 | 9.72 | 29000 | 0.1512 | 0.1784 | | 0.3017 | 9.89 | 29500 | 0.1484 | 0.1800 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0