readme: ---
  language:
  - hi
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-1b-hi-cv7
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_7_0
        name: Common Voice 7
        args: hi
      metrics:
      - type: wer
        value: 18.504
        name: Test WER
      - name: Test CER
        type: cer
        value: 6.655
  ---
  
  <!-- 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-xls-r-1b-hi-cv7
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5878
  - Wer: 0.3419
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 2000
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.9859        | 2.72  | 400   | 1.1663          | 0.7948 |
  | 1.2969        | 5.44  | 800   | 0.7725          | 0.6562 |
  | 1.1954        | 8.16  | 1200  | 0.5940          | 0.4904 |
  | 1.164         | 10.88 | 1600  | 0.5338          | 0.4316 |
  | 1.1464        | 13.6  | 2000  | 0.5432          | 0.4226 |
  | 1.1553        | 16.33 | 2400  | 0.5471          | 0.4260 |
  | 1.0985        | 19.05 | 2800  | 0.5290          | 0.4076 |
  | 1.0421        | 21.77 | 3200  | 0.5672          | 0.4181 |
  | 0.9831        | 24.49 | 3600  | 0.5741          | 0.4141 |
  | 0.9827        | 27.21 | 4000  | 0.5754          | 0.4179 |
  | 0.9669        | 29.93 | 4400  | 0.5310          | 0.3889 |
  | 0.9496        | 32.65 | 4800  | 0.5649          | 0.4062 |
  | 0.9112        | 35.37 | 5200  | 0.5738          | 0.3926 |
  | 0.8838        | 38.1  | 5600  | 0.5232          | 0.3768 |
  | 0.8666        | 40.81 | 6000  | 0.5510          | 0.3852 |
  | 0.8366        | 43.54 | 6400  | 0.5436          | 0.3837 |
  | 0.7957        | 46.26 | 6800  | 0.5337          | 0.3775 |
  | 0.7834        | 48.98 | 7200  | 0.5611          | 0.3844 |
  | 0.7685        | 51.7  | 7600  | 0.5710          | 0.4008 |
  | 0.7431        | 54.42 | 8000  | 0.5636          | 0.3726 |
  | 0.7353        | 57.14 | 8400  | 0.5937          | 0.3836 |
  | 0.7001        | 59.86 | 8800  | 0.5815          | 0.3858 |
  | 0.6799        | 62.58 | 9200  | 0.5862          | 0.3696 |
  | 0.6459        | 65.31 | 9600  | 0.6181          | 0.3762 |
  | 0.6121        | 68.03 | 10000 | 0.5637          | 0.3590 |
  | 0.5942        | 70.75 | 10400 | 0.6374          | 0.3882 |
  | 0.5769        | 73.47 | 10800 | 0.6015          | 0.3640 |
  | 0.5689        | 76.19 | 11200 | 0.5669          | 0.3508 |
  | 0.5461        | 78.91 | 11600 | 0.5967          | 0.3621 |
  | 0.5286        | 81.63 | 12000 | 0.5840          | 0.3605 |
  | 0.5057        | 84.35 | 12400 | 0.5848          | 0.3489 |
  | 0.482         | 87.07 | 12800 | 0.5860          | 0.3488 |
  | 0.4655        | 89.79 | 13200 | 0.5780          | 0.3453 |
  | 0.4523        | 92.52 | 13600 | 0.6150          | 0.3532 |
  | 0.4422        | 95.24 | 14000 | 0.5930          | 0.3452 |
  | 0.4436        | 97.96 | 14400 | 0.5867          | 0.3428 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi --dataset mozilla-foundation/common_voice_7_0 --config hi --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-xls-r-1b-hi"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "hi", 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_audi