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--- |
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language: |
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- ur |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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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_8_0 |
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metrics: |
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- wer |
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base_model: facebook/wav2vec2-xls-r-300m |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-Urdu |
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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 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: ur |
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metrics: |
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- type: wer |
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value: 39.89 |
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name: Test WER |
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- type: cer |
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value: 16.7 |
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name: Test CER |
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--- |
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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-xls-r-300m-Urdu |
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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: 0.9889 |
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- Wer: 0.5607 |
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- Cer: 0.2370 |
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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 kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_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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from datasets import load_dataset, Audio |
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from transformers import pipeline |
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model = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu" |
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data = load_dataset("mozilla-foundation/common_voice_8_0", |
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"ur", |
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split="test", |
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streaming=True, |
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use_auth_token=True) |
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sample_iter = iter(data.cast_column("path", |
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Audio(sampling_rate=16_000))) |
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sample = next(sample_iter) |
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asr = pipeline("automatic-speech-recognition", model=model) |
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prediction = asr(sample["path"]["array"], |
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chunk_length_s=5, |
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stride_length_s=1) |
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prediction |
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# => {'text': 'اب یہ ونگین لمحاتانکھار دلمیں میںفوث کریلیا اجائ'} |
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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: 0.0001 |
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- train_batch_size: 32 |
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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: 64 |
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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: 200 |
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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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| 3.6398 | 30.77 | 400 | 3.3517 | 1.0 | 1.0 | |
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| 2.9225 | 61.54 | 800 | 2.5123 | 1.0 | 0.8310 | |
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| 1.2568 | 92.31 | 1200 | 0.9699 | 0.6273 | 0.2575 | |
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| 0.8974 | 123.08 | 1600 | 0.9715 | 0.5888 | 0.2457 | |
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| 0.7151 | 153.85 | 2000 | 0.9984 | 0.5588 | 0.2353 | |
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| 0.6416 | 184.62 | 2400 | 0.9889 | 0.5607 | 0.2370 | |
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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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### 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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| 52.03 | 39.89 | |
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