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--- |
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language: |
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- sv-SE |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- mozilla-foundation/common_voice_7_0 |
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- generated_from_trainer |
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- sv |
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- robust-speech-event |
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- model_for_talk |
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datasets: |
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- common_voice |
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model-index: |
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- name: '' |
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results: [] |
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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-SV |
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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 - SV-SE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3171 |
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- Wer: 0.2730 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 2000 |
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- num_epochs: 50.0 |
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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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| 3.3349 | 1.45 | 500 | 3.2858 | 1.0 | |
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| 2.9298 | 2.91 | 1000 | 2.9225 | 1.0000 | |
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| 2.0839 | 4.36 | 1500 | 1.1546 | 0.8295 | |
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| 1.7093 | 5.81 | 2000 | 0.6827 | 0.5701 | |
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| 1.5855 | 7.27 | 2500 | 0.5597 | 0.4947 | |
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| 1.4831 | 8.72 | 3000 | 0.4923 | 0.4527 | |
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| 1.4416 | 10.17 | 3500 | 0.4670 | 0.4270 | |
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| 1.3848 | 11.63 | 4000 | 0.4341 | 0.3980 | |
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| 1.3749 | 13.08 | 4500 | 0.4203 | 0.4011 | |
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| 1.3311 | 14.53 | 5000 | 0.4310 | 0.3961 | |
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| 1.317 | 15.99 | 5500 | 0.3898 | 0.4322 | |
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| 1.2799 | 17.44 | 6000 | 0.3806 | 0.3572 | |
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| 1.2771 | 18.89 | 6500 | 0.3828 | 0.3427 | |
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| 1.2451 | 20.35 | 7000 | 0.3702 | 0.3359 | |
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| 1.2182 | 21.8 | 7500 | 0.3685 | 0.3270 | |
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| 1.2152 | 23.26 | 8000 | 0.3650 | 0.3308 | |
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| 1.1837 | 24.71 | 8500 | 0.3568 | 0.3187 | |
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| 1.1721 | 26.16 | 9000 | 0.3659 | 0.3249 | |
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| 1.1764 | 27.61 | 9500 | 0.3547 | 0.3145 | |
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| 1.1606 | 29.07 | 10000 | 0.3514 | 0.3104 | |
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| 1.1431 | 30.52 | 10500 | 0.3469 | 0.3062 | |
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| 1.1047 | 31.97 | 11000 | 0.3313 | 0.2979 | |
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| 1.1315 | 33.43 | 11500 | 0.3298 | 0.2992 | |
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| 1.1022 | 34.88 | 12000 | 0.3296 | 0.2973 | |
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| 1.0935 | 36.34 | 12500 | 0.3278 | 0.2926 | |
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| 1.0676 | 37.79 | 13000 | 0.3208 | 0.2868 | |
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| 1.0571 | 39.24 | 13500 | 0.3322 | 0.2885 | |
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| 1.0536 | 40.7 | 14000 | 0.3245 | 0.2831 | |
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| 1.0525 | 42.15 | 14500 | 0.3285 | 0.2826 | |
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| 1.0464 | 43.6 | 15000 | 0.3223 | 0.2796 | |
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| 1.0415 | 45.06 | 15500 | 0.3166 | 0.2774 | |
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| 1.0356 | 46.51 | 16000 | 0.3177 | 0.2746 | |
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| 1.04 | 47.96 | 16500 | 0.3150 | 0.2735 | |
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| 1.0209 | 49.42 | 17000 | 0.3175 | 0.2731 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.10.3 |
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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 = "hf-test/xls-r-300m-sv" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "sv-SE", 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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# => "jag lämnade grovjobbet åt honom" |
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``` |
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### Eval results on Common Voice 7 "test" (WER): |
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| Without LM | With LM (run `./eval.py`) | |
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|---|---| |
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| 27.30 | 18.85 | |
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