readme: ---
  language:
  - mr
  license: apache-2.0
  tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-mr
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: mr
      metrics:
      - type: wer
        value: 32.811
        name: Test WER
      - name: Test CER
        type: cer
        value: 7.692
  ---
  
  <!-- 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-xls-r-300m-mr
  
  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.
  It achieves the following results on the evaluation set:
  - Loss: 0.5479
  - Wer: 0.5740
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - 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: 1000
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 3.7378        | 18.18  | 400  | 3.5047          | 1.0    |
  | 3.1707        | 36.36  | 800  | 2.6166          | 0.9912 |
  | 1.4942        | 54.55  | 1200 | 0.5778          | 0.6927 |
  | 1.2058        | 72.73  | 1600 | 0.5168          | 0.6362 |
  | 1.0558        | 90.91  | 2000 | 0.5105          | 0.6069 |
  | 0.9488        | 109.09 | 2400 | 0.5151          | 0.6089 |
  | 0.8588        | 127.27 | 2800 | 0.5157          | 0.5989 |
  | 0.7991        | 145.45 | 3200 | 0.5179          | 0.5740 |
  | 0.7545        | 163.64 | 3600 | 0.5348          | 0.5740 |
  | 0.7144        | 181.82 | 4000 | 0.5518          | 0.5724 |
  | 0.7041        | 200.0  | 4400 | 0.5479          | 0.5740 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.1
  - Tokenizers 0.11.0
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-mr --dataset mozilla-foundation/common_voice_8_0 --config mr --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 = "anuragshas/wav2vec2-large-xls-r-300m-mr"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mr", 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
  # => "या पानास लेखाचे स्वरूप यायला हावे"
  ```
  
  ### Eval results on Common Voice 8 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 49.177 | 32.811 |
  