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--- |
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language: |
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- pa |
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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_8_0 |
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metrics: |
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- wer |
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- cer |
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base_model: Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 |
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model-index: |
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- name: wav2vec2-punjabi-V8-Abid |
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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 pa-IN |
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type: mozilla-foundation/common_voice_8_0 |
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args: pa-IN |
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metrics: |
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- type: wer |
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value: 36.02 |
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name: Test WER With LM |
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- type: cer |
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value: 12.81 |
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name: Test CER With LM |
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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-53-punjabi |
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This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2101 |
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- Wer: 0.4939 |
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- Cer: 0.2238 |
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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-xlsr-53-punjabi --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --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-53-punjabi" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "pa-IN", 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: 16 |
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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: 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: 200 |
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- num_epochs: 30 |
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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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| 11.0563 | 3.7 | 100 | 1.9492 | 0.7123 | 0.3872 | |
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| 1.6715 | 7.41 | 200 | 1.3142 | 0.6433 | 0.3086 | |
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| 0.9117 | 11.11 | 300 | 1.2733 | 0.5657 | 0.2627 | |
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| 0.666 | 14.81 | 400 | 1.2730 | 0.5598 | 0.2534 | |
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| 0.4225 | 18.52 | 500 | 1.2548 | 0.5300 | 0.2399 | |
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| 0.3209 | 22.22 | 600 | 1.2166 | 0.5229 | 0.2372 | |
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| 0.2678 | 25.93 | 700 | 1.1795 | 0.5041 | 0.2276 | |
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| 0.2088 | 29.63 | 800 | 1.2101 | 0.4939 | 0.2238 | |
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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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