--- language: - mr license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - robust-speech-event datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-mr results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: mozilla-foundation/common_voice_8_0 name: Common Voice 8 args: mr metrics: - type: wer # Required. Example: wer value: 31.57 # Required. Example: 20.90 name: Test WER # Optional. Example: Test WER - name: Test CER type: cer value: 6.93 --- # 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_8_0 - MR dataset. It achieves the following results on the evaluation set: - Loss: 0.494580 - Wer: 0.395909 ## 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: 0.0001 - 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: 1000 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results [12991/14200 9:46:02 < 54:32, 0.37 it/s, Epoch 182.96/200] Step Training Loss Validation Loss Wer 400 3.794000 3.532227 1.000000 800 3.362400 3.359044 1.000000 1200 2.293900 1.011279 0.829924 1600 1.233000 0.502743 0.593662 2000 0.962600 0.412519 0.496992 2400 0.831800 0.402903 0.493783 2800 0.737000 0.389773 0.469314 3200 0.677100 0.373987 0.436021 3600 0.634400 0.383823 0.432010 4000 0.586000 0.375610 0.419575 4400 0.561000 0.387891 0.418371 4800 0.518500 0.386357 0.417569 5200 0.515300 0.415069 0.430004 5600 0.478100 0.399211 0.408744 6000 0.468100 0.424542 0.402327 6400 0.439400 0.430979 0.410750 6800 0.429600 0.427700 0.409146 7200 0.400300 0.451111 0.419976 7600 0.395100 0.463446 0.405134 8000 0.381800 0.454752 0.407942 8400 0.371500 0.461547 0.404733 8800 0.362500 0.461543 0.411151 9200 0.338200 0.468299 0.417168 9600 0.338800 0.480989 0.412355 10000 0.317600 0.475700 0.410750 10400 0.315100 0.478920 0.403530 10800 0.296200 0.480600 0.398315 11200 0.299000 0.477083 0.393502 11600 0.290000 0.465646 0.393903 12000 0.290900 0.490041 0.405937 12400 0.275600 0.489354 0.399519 12800 0.272600 0.494580 0.395909 ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3.dev0 - Tokenizers 0.11.0