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wav2vec2-xls-r-1b-hi-cv7

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5878
  • Wer: 0.3419

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: 7.5e-05
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 2000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.9859 2.72 400 1.1663 0.7948
1.2969 5.44 800 0.7725 0.6562
1.1954 8.16 1200 0.5940 0.4904
1.164 10.88 1600 0.5338 0.4316
1.1464 13.6 2000 0.5432 0.4226
1.1553 16.33 2400 0.5471 0.4260
1.0985 19.05 2800 0.5290 0.4076
1.0421 21.77 3200 0.5672 0.4181
0.9831 24.49 3600 0.5741 0.4141
0.9827 27.21 4000 0.5754 0.4179
0.9669 29.93 4400 0.5310 0.3889
0.9496 32.65 4800 0.5649 0.4062
0.9112 35.37 5200 0.5738 0.3926
0.8838 38.1 5600 0.5232 0.3768
0.8666 40.81 6000 0.5510 0.3852
0.8366 43.54 6400 0.5436 0.3837
0.7957 46.26 6800 0.5337 0.3775
0.7834 48.98 7200 0.5611 0.3844
0.7685 51.7 7600 0.5710 0.4008
0.7431 54.42 8000 0.5636 0.3726
0.7353 57.14 8400 0.5937 0.3836
0.7001 59.86 8800 0.5815 0.3858
0.6799 62.58 9200 0.5862 0.3696
0.6459 65.31 9600 0.6181 0.3762
0.6121 68.03 10000 0.5637 0.3590
0.5942 70.75 10400 0.6374 0.3882
0.5769 73.47 10800 0.6015 0.3640
0.5689 76.19 11200 0.5669 0.3508
0.5461 78.91 11600 0.5967 0.3621
0.5286 81.63 12000 0.5840 0.3605
0.5057 84.35 12400 0.5848 0.3489
0.482 87.07 12800 0.5860 0.3488
0.4655 89.79 13200 0.5780 0.3453
0.4523 92.52 13600 0.6150 0.3532
0.4422 95.24 14000 0.5930 0.3452
0.4436 97.96 14400 0.5867 0.3428

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_7_0 with split test
python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi --dataset mozilla-foundation/common_voice_7_0 --config hi --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-xls-r-1b-hi"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "hi", 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
# => "तुम्हारे पास तीन महीने बचे हैं"

Eval results on Common Voice 7 "test" (WER):

Without LM With LM (run ./eval.py)
28.942 18.504
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Dataset used to train anuragshas/wav2vec2-xls-r-1b-hi

Evaluation results