readme: ---
  language:
  - ha
  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: XLS-R-300M - Hausa
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: ha
      metrics:
      - type: wer
        value: 36.295
        name: Test WER
      - name: Test CER
        type: cer
        value: 11.073
  ---
  
  <!-- 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. -->
  
  # XLS-R-300M - Hausa
  
  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: 0.6094
  - Wer: 0.5234
  
  ## 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: 13
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine_with_restarts
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.9599        | 6.56  | 400  | 2.8650          | 1.0    |
  | 2.7357        | 13.11 | 800  | 2.7377          | 0.9951 |
  | 1.3012        | 19.67 | 1200 | 0.6686          | 0.7111 |
  | 1.0454        | 26.23 | 1600 | 0.5686          | 0.6137 |
  | 0.9069        | 32.79 | 2000 | 0.5576          | 0.5815 |
  | 0.82          | 39.34 | 2400 | 0.5502          | 0.5591 |
  | 0.7413        | 45.9  | 2800 | 0.5970          | 0.5586 |
  | 0.6872        | 52.46 | 3200 | 0.5817          | 0.5428 |
  | 0.634         | 59.02 | 3600 | 0.5636          | 0.5314 |
  | 0.6022        | 65.57 | 4000 | 0.5780          | 0.5229 |
  | 0.5705        | 72.13 | 4400 | 0.6036          | 0.5323 |
  | 0.5408        | 78.69 | 4800 | 0.6119          | 0.5336 |
  | 0.5225        | 85.25 | 5200 | 0.6105          | 0.5270 |
  | 0.5265        | 91.8  | 5600 | 0.6034          | 0.5231 |
  | 0.5154        | 98.36 | 6000 | 0.6094          | 0.5234 |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - 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-ha-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ha --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-ha-cv8"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ha", 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
  # => "kakin hade ya ke da kyautar"
  ```
  
  ### Eval results on Common Voice 8 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 47.821 | 36.295 |