readme: ---
  language:
  - bg
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Bulgarian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: bg
      metrics:
      - name: Test WER
        type: wer
        value: 21.195
      - name: Test CER
        type: cer
        value: 4.786
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: bg
      metrics:
      - name: Test WER
        type: wer
        value: 32.667
      - name: Test CER
        type: cer
        value: 12.452
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: bg
      metrics:
      - name: Test WER
        type: wer
        value: 31.03
  ---
  
  <!-- 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 - Bulgarian
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2473
  - Wer: 0.3002
  
  ## 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: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.1589        | 3.48  | 400  | 3.0830          | 1.0    |
  | 2.8921        | 6.96  | 800  | 2.6605          | 0.9982 |
  | 1.3049        | 10.43 | 1200 | 0.5069          | 0.5707 |
  | 1.1349        | 13.91 | 1600 | 0.4159          | 0.5041 |
  | 1.0686        | 17.39 | 2000 | 0.3815          | 0.4746 |
  | 0.999         | 20.87 | 2400 | 0.3541          | 0.4343 |
  | 0.945         | 24.35 | 2800 | 0.3266          | 0.4132 |
  | 0.9058        | 27.83 | 3200 | 0.2969          | 0.3771 |
  | 0.8672        | 31.3  | 3600 | 0.2802          | 0.3553 |
  | 0.8313        | 34.78 | 4000 | 0.2662          | 0.3380 |
  | 0.8068        | 38.26 | 4400 | 0.2528          | 0.3181 |
  | 0.7796        | 41.74 | 4800 | 0.2537          | 0.3073 |
  | 0.7621        | 45.22 | 5200 | 0.2503          | 0.3036 |
  | 0.7611        | 48.7  | 5600 | 0.2477          | 0.2991 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - 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-bg --dataset mozilla-foundation/common_voice_8_0 --config bg --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```
  
  ### 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-bg"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "bg", 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
  # => "и надутият му ката блоонкурем взе да се събира"
  ```
  
  ### Eval results on Common Voice 8 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 30.07 | 21.195 |
  