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--- |
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language: |
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- ar |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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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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metrics: |
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- wer |
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- cer |
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base_model: facebook/wav2vec2-xls-r-300m |
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model-index: |
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- name: wav2vec2-xls-r-300m-arabic |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: Common Voice ar |
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type: mozilla-foundation/common_voice_7_0 |
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args: ar |
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metrics: |
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- type: wer |
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value: 38.83 |
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name: Test WER With LM |
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- type: cer |
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value: 15.33 |
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name: Test CER With LM |
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- task: |
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type: automatic-speech-recognition |
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name: 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: ar |
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metrics: |
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- type: wer |
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value: 89.8 |
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name: Test WER |
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- task: |
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type: automatic-speech-recognition |
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name: 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: ar |
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metrics: |
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- type: wer |
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value: 87.46 |
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name: Test WER |
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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-large-xlsr-300-arabic |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4514 |
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- Wer: 0.4256 |
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- Cer: 0.1528 |
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#### Evaluation Commands |
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1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` |
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```bash |
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python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-300-arabic --dataset mozilla-foundation/common_voice_7_0 --config ur --split test |
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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 = "kingabzpro/wav2vec2-large-xlsr-300-arabic" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ar", 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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### 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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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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: 1000 |
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- num_epochs: 10 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 5.4375 | 1.8 | 500 | 3.3330 | 1.0 | 1.0 | |
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| 2.2187 | 3.6 | 1000 | 0.7790 | 0.6501 | 0.2338 | |
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| 0.9471 | 5.4 | 1500 | 0.5353 | 0.5015 | 0.1822 | |
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| 0.7416 | 7.19 | 2000 | 0.4889 | 0.4490 | 0.1640 | |
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| 0.6358 | 8.99 | 2500 | 0.4514 | 0.4256 | 0.1528 | |
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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 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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