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--- |
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language: |
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- it |
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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_7_0 |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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model-index: |
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- name: XLS-R-300m - Italian |
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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 7 |
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type: mozilla-foundation/common_voice_7_0 |
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args: it |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 19.44 |
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- name: Test CER |
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type: cer |
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value: 4.47 |
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- name: Test WER (+LM) |
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type: wer |
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value: 14.08 |
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- name: Test CER (+LM) |
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type: cer |
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value: 3.67 |
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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: it |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 31.01 |
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- name: Test CER |
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type: cer |
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value: 9.27 |
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- name: Test WER (+LM) |
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type: wer |
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value: 22.09 |
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- name: Test CER (+LM) |
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type: cer |
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value: 7.9 |
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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: it |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 38.07 |
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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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# wav2vec2-xls-r-300m-italian |
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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_7_0 - IT dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: inf |
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- Wer: 0.1710 |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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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: 500 |
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- num_epochs: 5.0 |
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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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| No log | 0.04 | 100 | inf | 1.0 | |
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| No log | 0.09 | 200 | inf | 0.9983 | |
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| No log | 0.13 | 300 | inf | 0.7672 | |
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| No log | 0.18 | 400 | inf | 0.6919 | |
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| 2.9929 | 0.22 | 500 | inf | 0.6266 | |
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| 2.9929 | 0.26 | 600 | inf | 0.5513 | |
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| 2.9929 | 0.31 | 700 | inf | 0.5081 | |
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| 2.9929 | 0.35 | 800 | inf | 0.4945 | |
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| 2.9929 | 0.39 | 900 | inf | 0.4720 | |
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| 0.5311 | 0.44 | 1000 | inf | 0.4387 | |
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| 0.5311 | 0.48 | 1100 | inf | 0.4411 | |
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| 0.5311 | 0.53 | 1200 | inf | 0.4429 | |
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| 0.5311 | 0.57 | 1300 | inf | 0.4322 | |
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| 0.5311 | 0.61 | 1400 | inf | 0.4532 | |
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| 0.4654 | 0.66 | 1500 | inf | 0.4492 | |
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| 0.4654 | 0.7 | 1600 | inf | 0.3879 | |
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| 0.4654 | 0.75 | 1700 | inf | 0.3836 | |
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| 0.4654 | 0.79 | 1800 | inf | 0.3743 | |
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| 0.4654 | 0.83 | 1900 | inf | 0.3687 | |
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| 0.4254 | 0.88 | 2000 | inf | 0.3793 | |
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| 0.4254 | 0.92 | 2100 | inf | 0.3766 | |
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| 0.4254 | 0.97 | 2200 | inf | 0.3705 | |
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| 0.4254 | 1.01 | 2300 | inf | 0.3272 | |
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| 0.4254 | 1.05 | 2400 | inf | 0.3185 | |
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| 0.3997 | 1.1 | 2500 | inf | 0.3244 | |
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| 0.3997 | 1.14 | 2600 | inf | 0.3082 | |
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| 0.3997 | 1.18 | 2700 | inf | 0.3040 | |
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| 0.3997 | 1.23 | 2800 | inf | 0.3028 | |
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| 0.3997 | 1.27 | 2900 | inf | 0.3112 | |
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| 0.3668 | 1.32 | 3000 | inf | 0.3110 | |
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| 0.3668 | 1.36 | 3100 | inf | 0.3067 | |
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| 0.3668 | 1.4 | 3200 | inf | 0.2961 | |
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| 0.3668 | 1.45 | 3300 | inf | 0.3081 | |
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| 0.3668 | 1.49 | 3400 | inf | 0.2936 | |
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| 0.3645 | 1.54 | 3500 | inf | 0.3037 | |
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| 0.3645 | 1.58 | 3600 | inf | 0.2974 | |
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| 0.3645 | 1.62 | 3700 | inf | 0.3010 | |
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| 0.3645 | 1.67 | 3800 | inf | 0.2985 | |
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| 0.3645 | 1.71 | 3900 | inf | 0.2976 | |
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| 0.3624 | 1.76 | 4000 | inf | 0.2928 | |
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| 0.3624 | 1.8 | 4100 | inf | 0.2860 | |
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| 0.3624 | 1.84 | 4200 | inf | 0.2922 | |
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| 0.3624 | 1.89 | 4300 | inf | 0.2866 | |
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| 0.3624 | 1.93 | 4400 | inf | 0.2776 | |
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| 0.3527 | 1.97 | 4500 | inf | 0.2792 | |
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| 0.3527 | 2.02 | 4600 | inf | 0.2858 | |
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| 0.3527 | 2.06 | 4700 | inf | 0.2767 | |
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| 0.3527 | 2.11 | 4800 | inf | 0.2824 | |
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| 0.3527 | 2.15 | 4900 | inf | 0.2799 | |
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| 0.3162 | 2.19 | 5000 | inf | 0.2673 | |
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| 0.3162 | 2.24 | 5100 | inf | 0.2962 | |
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| 0.3162 | 2.28 | 5200 | inf | 0.2736 | |
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| 0.3162 | 2.33 | 5300 | inf | 0.2652 | |
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| 0.3162 | 2.37 | 5400 | inf | 0.2551 | |
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| 0.3063 | 2.41 | 5500 | inf | 0.2680 | |
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| 0.3063 | 2.46 | 5600 | inf | 0.2558 | |
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| 0.3063 | 2.5 | 5700 | inf | 0.2598 | |
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| 0.3063 | 2.54 | 5800 | inf | 0.2518 | |
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| 0.3063 | 2.59 | 5900 | inf | 0.2541 | |
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| 0.2913 | 2.63 | 6000 | inf | 0.2507 | |
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| 0.2913 | 2.68 | 6100 | inf | 0.2500 | |
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| 0.2913 | 2.72 | 6200 | inf | 0.2435 | |
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| 0.2913 | 2.76 | 6300 | inf | 0.2376 | |
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| 0.2913 | 2.81 | 6400 | inf | 0.2348 | |
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| 0.2797 | 2.85 | 6500 | inf | 0.2512 | |
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| 0.2797 | 2.9 | 6600 | inf | 0.2382 | |
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| 0.2797 | 2.94 | 6700 | inf | 0.2523 | |
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| 0.2797 | 2.98 | 6800 | inf | 0.2522 | |
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| 0.2797 | 3.03 | 6900 | inf | 0.2409 | |
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| 0.2766 | 3.07 | 7000 | inf | 0.2453 | |
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| 0.2766 | 3.12 | 7100 | inf | 0.2326 | |
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| 0.2766 | 3.16 | 7200 | inf | 0.2286 | |
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| 0.2766 | 3.2 | 7300 | inf | 0.2342 | |
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| 0.2766 | 3.25 | 7400 | inf | 0.2305 | |
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| 0.2468 | 3.29 | 7500 | inf | 0.2238 | |
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| 0.2468 | 3.33 | 7600 | inf | 0.2321 | |
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| 0.2468 | 3.38 | 7700 | inf | 0.2305 | |
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| 0.2468 | 3.42 | 7800 | inf | 0.2174 | |
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| 0.2468 | 3.47 | 7900 | inf | 0.2201 | |
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| 0.2439 | 3.51 | 8000 | inf | 0.2133 | |
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| 0.2439 | 3.55 | 8100 | inf | 0.2217 | |
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| 0.2439 | 3.6 | 8200 | inf | 0.2189 | |
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| 0.2439 | 3.64 | 8300 | inf | 0.2105 | |
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| 0.2439 | 3.69 | 8400 | inf | 0.2118 | |
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| 0.2357 | 3.73 | 8500 | inf | 0.2093 | |
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| 0.2357 | 3.77 | 8600 | inf | 0.2103 | |
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| 0.2357 | 3.82 | 8700 | inf | 0.2035 | |
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| 0.2357 | 3.86 | 8800 | inf | 0.2019 | |
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| 0.2357 | 3.91 | 8900 | inf | 0.2032 | |
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| 0.2217 | 3.95 | 9000 | inf | 0.2056 | |
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| 0.2217 | 3.99 | 9100 | inf | 0.2022 | |
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| 0.2217 | 4.04 | 9200 | inf | 0.1932 | |
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| 0.2217 | 4.08 | 9300 | inf | 0.1935 | |
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| 0.2217 | 4.12 | 9400 | inf | 0.1906 | |
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| 0.2025 | 4.17 | 9500 | inf | 0.1879 | |
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| 0.2025 | 4.21 | 9600 | inf | 0.1882 | |
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| 0.2025 | 4.26 | 9700 | inf | 0.1854 | |
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| 0.2025 | 4.3 | 9800 | inf | 0.1865 | |
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| 0.2025 | 4.34 | 9900 | inf | 0.1844 | |
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| 0.1869 | 4.39 | 10000 | inf | 0.1822 | |
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| 0.1869 | 4.43 | 10100 | inf | 0.1815 | |
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| 0.1869 | 4.48 | 10200 | inf | 0.1812 | |
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| 0.1869 | 4.52 | 10300 | inf | 0.1792 | |
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| 0.1869 | 4.56 | 10400 | inf | 0.1797 | |
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| 0.1863 | 4.61 | 10500 | inf | 0.1774 | |
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| 0.1863 | 4.65 | 10600 | inf | 0.1767 | |
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| 0.1863 | 4.7 | 10700 | inf | 0.1765 | |
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| 0.1863 | 4.74 | 10800 | inf | 0.1753 | |
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| 0.1863 | 4.78 | 10900 | inf | 0.1731 | |
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| 0.178 | 4.83 | 11000 | inf | 0.1727 | |
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| 0.178 | 4.87 | 11100 | inf | 0.1724 | |
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| 0.178 | 4.91 | 11200 | inf | 0.1722 | |
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| 0.178 | 4.96 | 11300 | inf | 0.1712 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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