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wav2vec2-large-xls-r-300m-Urdu

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9889
  • Wer: 0.5607
  • Cer: 0.2370

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_0 --config ur --split test

Inference With LM

from datasets import load_dataset, Audio
from transformers import pipeline
model = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu"
data = load_dataset("mozilla-foundation/common_voice_8_0",
                     "ur",
                     split="test", 
                     streaming=True, 
                     use_auth_token=True)

sample_iter = iter(data.cast_column("path", 
                    Audio(sampling_rate=16_000)))
sample = next(sample_iter)

asr = pipeline("automatic-speech-recognition", model=model)
prediction = asr(sample["path"]["array"], 
                  chunk_length_s=5, 
                  stride_length_s=1)
prediction
# => {'text': 'اب یہ ونگین لمحاتانکھار دلمیں میںفوث کریلیا اجائ'}

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 200

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.6398 30.77 400 3.3517 1.0 1.0
2.9225 61.54 800 2.5123 1.0 0.8310
1.2568 92.31 1200 0.9699 0.6273 0.2575
0.8974 123.08 1600 0.9715 0.5888 0.2457
0.7151 153.85 2000 0.9984 0.5588 0.2353
0.6416 184.62 2400 0.9889 0.5607 0.2370

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0

Eval results on Common Voice 8 "test" (WER):

Without LM With LM (run ./eval.py)
52.03 39.89
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Dataset used to train kingabzpro/wav2vec2-large-xls-r-300m-Urdu

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Evaluation results