--- language: - bg license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer - bg - robust-speech-event - model_for_talk datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: XLS-R-300M - Bulgarian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: bg metrics: - name: Test WER type: wer value: 46.68 - name: Test CER type: cer value: 10.75 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: bg metrics: - name: Test WER type: wer value: 63.68 - name: Test CER type: cer value: 19.88 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: bg metrics: - name: Test WER type: wer value: 64.08 --- # wav2vec2-large-xls-r-300m-bulgarian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.4487 - Wer: 0.4674 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9774 | 6.33 | 500 | 2.9769 | 1.0 | | 1.3453 | 12.66 | 1000 | 0.6523 | 0.6980 | | 1.1658 | 18.99 | 1500 | 0.5636 | 0.6359 | | 1.0797 | 25.32 | 2000 | 0.5004 | 0.5759 | | 1.044 | 31.65 | 2500 | 0.4958 | 0.5569 | | 0.9915 | 37.97 | 3000 | 0.4971 | 0.5350 | | 0.9429 | 44.3 | 3500 | 0.4829 | 0.5229 | | 0.9266 | 50.63 | 4000 | 0.4515 | 0.5074 | | 0.8965 | 56.96 | 4500 | 0.4599 | 0.5039 | | 0.878 | 63.29 | 5000 | 0.4735 | 0.4954 | | 0.8494 | 69.62 | 5500 | 0.4460 | 0.4878 | | 0.8343 | 75.95 | 6000 | 0.4510 | 0.4795 | | 0.8236 | 82.28 | 6500 | 0.4538 | 0.4789 | | 0.8069 | 88.61 | 7000 | 0.4526 | 0.4748 | | 0.7958 | 94.94 | 7500 | 0.4496 | 0.4700 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0