How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="arampacha/wav2vec2-xls-r-1b-ka")
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained("arampacha/wav2vec2-xls-r-1b-ka")
model = AutoModelForCTC.from_pretrained("arampacha/wav2vec2-xls-r-1b-ka")
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wav2vec2-xls-r-1b-ka

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/KA/NOIZY_STUDENT_2/ - KA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1022
  • Wer: 0.1527
  • Cer: 0.0221

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: 7e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.2839 6.45 400 0.2229 0.3609 0.0557
0.9775 12.9 800 0.1271 0.2202 0.0317
0.9045 19.35 1200 0.1268 0.2030 0.0294
0.8652 25.8 1600 0.1211 0.1940 0.0287
0.8505 32.26 2000 0.1192 0.1912 0.0276
0.8168 38.7 2400 0.1086 0.1763 0.0260
0.7737 45.16 2800 0.1098 0.1753 0.0256
0.744 51.61 3200 0.1054 0.1646 0.0239
0.7114 58.06 3600 0.1034 0.1573 0.0228
0.6773 64.51 4000 0.1022 0.1527 0.0221

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.0
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Dataset used to train arampacha/wav2vec2-xls-r-1b-ka

Evaluation results

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