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--- |
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language: |
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- sk |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: XLS-R-300M - Slovak |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: sk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 18.609 |
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- name: Test CER |
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type: cer |
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value: 5.488 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: sk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 40.548 |
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- name: Test CER |
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type: cer |
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value: 17.733 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: sk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 44.1 |
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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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# XLS-R-300M - Slovak |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SK dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3067 |
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- Wer: 0.2678 |
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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: 7.5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 1500 |
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- num_epochs: 60.0 |
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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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| 5.175 | 2.41 | 400 | 4.6909 | 1.0 | |
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| 3.3785 | 4.82 | 800 | 3.3080 | 1.0 | |
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| 2.6964 | 7.23 | 1200 | 2.0651 | 1.1055 | |
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| 1.3008 | 9.64 | 1600 | 0.5845 | 0.6207 | |
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| 1.1185 | 12.05 | 2000 | 0.4195 | 0.4193 | |
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| 1.0252 | 14.46 | 2400 | 0.3824 | 0.3570 | |
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| 0.935 | 16.87 | 2800 | 0.3693 | 0.3462 | |
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| 0.8818 | 19.28 | 3200 | 0.3587 | 0.3318 | |
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| 0.8534 | 21.69 | 3600 | 0.3420 | 0.3180 | |
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| 0.8137 | 24.1 | 4000 | 0.3426 | 0.3130 | |
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| 0.7968 | 26.51 | 4400 | 0.3349 | 0.3102 | |
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| 0.7558 | 28.92 | 4800 | 0.3216 | 0.3019 | |
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| 0.7313 | 31.33 | 5200 | 0.3451 | 0.3060 | |
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| 0.7358 | 33.73 | 5600 | 0.3272 | 0.2967 | |
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| 0.718 | 36.14 | 6000 | 0.3315 | 0.2882 | |
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| 0.6991 | 38.55 | 6400 | 0.3299 | 0.2830 | |
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| 0.6529 | 40.96 | 6800 | 0.3140 | 0.2836 | |
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| 0.6225 | 43.37 | 7200 | 0.3128 | 0.2751 | |
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| 0.633 | 45.78 | 7600 | 0.3211 | 0.2774 | |
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| 0.5876 | 48.19 | 8000 | 0.3162 | 0.2764 | |
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| 0.588 | 50.6 | 8400 | 0.3082 | 0.2722 | |
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| 0.5915 | 53.01 | 8800 | 0.3120 | 0.2681 | |
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| 0.5798 | 55.42 | 9200 | 0.3133 | 0.2709 | |
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| 0.5736 | 57.83 | 9600 | 0.3086 | 0.2676 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.4.dev0 |
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- Tokenizers 0.11.0 |
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#### Evaluation Commands |
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1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
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```bash |
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python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sk --split test |
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``` |
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2. To evaluate on `speech-recognition-community-v2/dev_data` |
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```bash |
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python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sk --split validation --chunk_length_s 5.0 --stride_length_s 1.0 |
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``` |
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### Inference With LM |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import AutoModelForCTC, AutoProcessor |
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import torchaudio.functional as F |
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model_id = "anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test", streaming=True, use_auth_token=True)) |
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sample = next(sample_iter) |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() |
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model = AutoModelForCTC.from_pretrained(model_id) |
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processor = AutoProcessor.from_pretrained(model_id) |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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transcription = processor.batch_decode(logits.numpy()).text |
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# => "" |
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``` |
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### Eval results on Common Voice 8 "test" (WER): |
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| Without LM | With LM (run `./eval.py`) | |
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|---|---| |
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| 26.707 | 18.609 | |
