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wav2vec2-large-xls-r-300m-ur-cv8

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: 1.1443
  • Wer: 0.5677

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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
3.6269 15.98 400 3.3246 1.0
3.0546 31.98 800 2.8148 0.9963
1.4589 47.98 1200 1.0237 0.6584
1.0911 63.98 1600 0.9524 0.5966
0.8879 79.98 2000 0.9827 0.5822
0.7467 95.98 2400 0.9923 0.5840
0.6427 111.98 2800 0.9988 0.5714
0.5685 127.98 3200 1.0872 0.5807
0.5068 143.98 3600 1.1194 0.5822
0.463 159.98 4000 1.1138 0.5692
0.4212 175.98 4400 1.1232 0.5714
0.4056 191.98 4800 1.1443 0.5677

Framework versions

  • Transformers 4.16.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.1
  • Tokenizers 0.11.0

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-ur-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ur --split test

Inference With LM

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-ur-cv8"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ur", 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
# => "اب نے ٹ پیس ان لیتے ہیں"

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

Without LM With LM (run ./eval.py)
52.146 42.376
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Dataset used to train anuragshas/wav2vec2-large-xls-r-300m-ur-cv8

Evaluation results