readme: ---
  language:
  - as
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-as
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_7_0
        name: Common Voice 7
        args: as
      metrics:
      - type: wer
        value: 56.995
        name: Test WER
      - name: Test CER
        type: cer
        value: 20.39
  ---
  
  <!-- 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-as
  
  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: 1.9068
  - Wer: 0.6679
  
  ## 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: 0.0003
  - 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_ratio: 0.12
  - num_epochs: 240
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 5.7027        | 21.05  | 400  | 3.4157          | 1.0    |
  | 1.1638        | 42.1   | 800  | 1.3498          | 0.7461 |
  | 0.2266        | 63.15  | 1200 | 1.6147          | 0.7273 |
  | 0.1473        | 84.21  | 1600 | 1.6649          | 0.7108 |
  | 0.1043        | 105.26 | 2000 | 1.7691          | 0.7090 |
  | 0.0779        | 126.31 | 2400 | 1.8300          | 0.7009 |
  | 0.0613        | 147.36 | 2800 | 1.8681          | 0.6916 |
  | 0.0471        | 168.41 | 3200 | 1.8567          | 0.6875 |
  | 0.0343        | 189.46 | 3600 | 1.9054          | 0.6840 |
  | 0.0265        | 210.51 | 4000 | 1.9020          | 0.6786 |
  | 0.0219        | 231.56 | 4400 | 1.9068          | 0.6679 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-as --dataset mozilla-foundation/common_voice_7_0 --config as --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-as"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "as", 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
  # => "জাহাজত তো তিশকুৰলৈ যাব কিন্তু জহাজিটো আহিপনে"
  ```
  
  ### Eval results on Common Voice 7 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 67 | 56.995 |
  
  