readme: ---
  language:
  - pa-IN
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-punjabi-V8-Abid
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice pa-IN
        args: pa-IN
      metrics:
      - type: wer
        value: 36.02
        name: Test WER With LM
      - type: cer
        value: 12.81
        name: Test CER With LM
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xlsr-53-punjabi
  
  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.
  It achieves the following results on the evaluation set:
  - Loss: 1.2101
  - Wer: 0.4939
  - Cer: 0.2238
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-53-punjabi --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test
  ```
  
  ### Inference With LM
  
  ```python
  import torch
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torchaudio.functional as F
  model_id = "kingabzpro/wav2vec2-large-xlsr-53-punjabi"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "pa-IN", split="test", streaming=True, use_auth_token=True))
  sample = next(sample_iter)
  resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
  model = AutoModelForCTC.from_pretrained(model_id)
  processor = AutoProcessor.from_pretrained(model_id)
  input_values = processor(resampled_audio, return_tensors="pt").input_values
  with torch.no_grad():
      logits = model(input_values).logits
  transcription = processor.batch_decode(logits.numpy()).text
  
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 200
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 11.0563       | 3.7   | 100  | 1.9492          | 0.7123 | 0.3872 |
  | 1.6715        | 7.41  | 200  | 1.3142          | 0.6433 | 0.3086 |
  | 0.9117        | 11.11 | 300  | 1.2733          | 0.5657 | 0.2627 |
  | 0.666         | 14.81 | 400  | 1.2730          | 0.5598 | 0.2534 |
  | 0.4225        | 18.52 | 500  | 1.2548          | 0.5300 | 0.2399 |
  | 0.3209        | 22.22 | 600  | 1.2166          | 0.5229 | 0.2372 |
  | 0.2678        | 25.93 | 700  | 1.1795          | 0.5041 | 0.2276 |
  | 0.2088        | 29.63 | 800  | 1.2101          | 0.4939 | 0.2238 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  