readme: ---
  language:
  - sv-SE
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-large-xls-r-1b-Swedish
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice sv-SE
        args: sv-SE
      metrics:
      - type: wer
        value: 14.04
        name: Test WER With LM
        args:
        - learning_rate: 7.5e-05
        - train_batch_size: 32
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 4
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 50
        - mixed_precision_training: Native AMP
      - type: cer
        value: 4.86
        name: Test CER  With LM
        args:
        - learning_rate: 7.5e-05
        - train_batch_size: 32
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 4
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 1000
        - num_epochs: 50
        - mixed_precision_training: Native AMP
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sv
      metrics:
      - name: Test WER
        type: wer
        value: 29.69
      - name: Test CER
        type: cer
        value: 12.59
  ---
  
  <!-- 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-1b-Swedish
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  
  **Without LM**
  - Loss: 0.3370
  - Wer: 18.44 
  - Cer: 5.75
  
  **With LM**
  - Loss: 0.3370
  - Wer: 14.04 
  - Cer: 4.86
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset speech-recognition-community-v2/dev_data --config sv --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 = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sv-SE", 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
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 3.1562        | 11.11 | 500  | 0.4830          | 0.3729 | 0.1169 |
  | 0.5655        | 22.22 | 1000 | 0.3553          | 0.2381 | 0.0743 |
  | 0.3376        | 33.33 | 1500 | 0.3359          | 0.2179 | 0.0696 |
  | 0.2419        | 44.44 | 2000 | 0.3232          | 0.1844 | 0.0575 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  