XLS-R-300M - Bulgarian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2473
  • Wer: 0.3002

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.1589 3.48 400 3.0830 1.0
2.8921 6.96 800 2.6605 0.9982
1.3049 10.43 1200 0.5069 0.5707
1.1349 13.91 1600 0.4159 0.5041
1.0686 17.39 2000 0.3815 0.4746
0.999 20.87 2400 0.3541 0.4343
0.945 24.35 2800 0.3266 0.4132
0.9058 27.83 3200 0.2969 0.3771
0.8672 31.3 3600 0.2802 0.3553
0.8313 34.78 4000 0.2662 0.3380
0.8068 38.26 4400 0.2528 0.3181
0.7796 41.74 4800 0.2537 0.3073
0.7621 45.22 5200 0.2503 0.3036
0.7611 48.7 5600 0.2477 0.2991

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • 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-bg --dataset mozilla-foundation/common_voice_8_0 --config bg --split test
  1. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 5.0 --stride_length_s 1.0

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-bg"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "bg", 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
# => "и надутият му ката блоонкурем взе да се събира"

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

Without LM With LM (run ./eval.py)
30.07 21.195
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Dataset used to train anuragshas/wav2vec2-large-xls-r-300m-bg

Evaluation results