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README.md
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---
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language:
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- ha
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- mozilla-foundation/common_voice_8_0
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- generated_from_trainer
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- ha
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- robust-speech-event
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- model_for_talk
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datasets:
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- mozilla-foundation/common_voice_8_0
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model-index:
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- name: Akashpb13/Hausa_xlsr
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 8
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type: mozilla-foundation/common_voice_8_0
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args: ha
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metrics:
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- name: Test WER
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type: wer
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value: 0.20614541257934219
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- name: Test CER
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type: cer
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value: 0.04358048053214061
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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args: ha
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metrics:
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- name: Test WER
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type: wer
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value: 0.20614541257934219
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- name: Test CER
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type: cer
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value: 0.04358048053214061
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---
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# Akashpb13/Hausa_xlsr
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
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It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
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- Loss: 0.275118
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- Wer: 0.329955
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## Model description
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"facebook/wav2vec2-xls-r-300m" was finetuned.
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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Training data -
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Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv
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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
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## Training procedure
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For creating the training dataset, all possible datasets were appended and 90-10 split was used.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.000096
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 13
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- gradient_accumulation_steps: 2
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 50
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- mixed_precision_training: Native AMP
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### Training results
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| Step | Training Loss | Validation Loss | Wer |
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|------|---------------|-----------------|----------|
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| 500 | 5.175900 | 2.750914 | 1.000000 |
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| 1000 | 1.028700 | 0.338649 | 0.497999 |
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| 1500 | 0.332200 | 0.246896 | 0.402241 |
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| 2000 | 0.227300 | 0.239640 | 0.395839 |
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| 2500 | 0.175000 | 0.239577 | 0.373966 |
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| 3000 | 0.140400 | 0.243272 | 0.356095 |
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| 3500 | 0.119200 | 0.263761 | 0.365164 |
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| 4000 | 0.099300 | 0.265954 | 0.353428 |
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| 4500 | 0.084400 | 0.276367 | 0.349693 |
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| 5000 | 0.073700 | 0.282631 | 0.343825 |
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| 5500 | 0.068000 | 0.282344 | 0.341158 |
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| 6000 | 0.064500 | 0.281591 | 0.342491 |
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### Framework versions
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- Transformers 4.16.0.dev0
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- Pytorch 1.10.0+cu102
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- Datasets 1.18.3
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- Tokenizers 0.10.3
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#### Evaluation Commands
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1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
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```bash
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python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ha --split test
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```
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