---
language:
- sw
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sw
datasets:
- mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: Akashpb13/Swahili_xlsr
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 8
      type: mozilla-foundation/common_voice_8_0
      args: sw
    metrics:
    - type: wer
      value: 0.11763625454589981
      name: Test WER
    - type: cer
      value: 0.02884228669922436
      name: Test CER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Robust Speech Event - Dev Data
      type: speech-recognition-community-v2/dev_data
      args: kmr
    metrics:
    - type: wer
      value: 0.11763625454589981
      name: Test WER
    - type: cer
      value: 0.02884228669922436
      name: Test CER
---

# Akashpb13/Swahili_xlsr

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_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
- Loss: 0.159032
- Wer: 0.187934
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.

## Intended uses & limitations
More information needed
## Training and evaluation data
Training data - 
Common voice Hausa train.tsv and dev.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

## Training procedure
For creating the training dataset, all possible datasets were appended and 90-10 split was used. 

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 0.000096
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 80
- mixed_precision_training: Native AMP


### Training results

| Step | Training Loss | Validation Loss | Wer      |
|------|---------------|-----------------|----------|
| 500  | 4.810000      | 2.168847        | 0.995747 |
| 1000 | 0.564200      | 0.209411        | 0.303485 |
| 1500 | 0.217700      | 0.153959        | 0.239534 |
| 2000 | 0.150700      | 0.139901        | 0.216327 |
| 2500 | 0.119400      | 0.137543        | 0.208828 |
| 3000 | 0.099500      | 0.140921        | 0.203045 |
| 3500 | 0.087100      | 0.138835        | 0.199649 |
| 4000 | 0.074600      | 0.141297        | 0.195844 |
| 4500 | 0.066600      | 0.148560        | 0.194127 |
| 5000 | 0.060400      | 0.151214        | 0.194388 |
| 5500 | 0.054400      | 0.156072        | 0.192187 |
| 6000 | 0.051100      | 0.154726        | 0.190322 |
| 6500 | 0.048200      | 0.159847        | 0.189538 |
| 7000 | 0.046400      | 0.158727        | 0.188307 |
| 7500 | 0.046500      | 0.159032        | 0.187934 |


### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3

#### Evaluation Commands

1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`

```bash
python eval.py --model_id Akashpb13/Swahili_xlsr --dataset mozilla-foundation/common_voice_8_0 --config sw --split test
```