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metadata
language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
model-index:
  - name: XLS-R-1B - Hindi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 15.899
          - name: Test CER
            type: cer
            value: 5.83

XLS-R-1B - Hindi

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6921
  • Wer: 0.3547

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: 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: 1500
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.0674 2.07 400 1.3411 0.8835
1.324 4.15 800 0.9311 0.7142
1.2023 6.22 1200 0.8060 0.6170
1.1573 8.29 1600 0.7415 0.4972
1.1117 10.36 2000 0.7248 0.4588
1.0672 12.44 2400 0.6729 0.4350
1.0336 14.51 2800 0.7117 0.4346
1.0025 16.58 3200 0.7019 0.4272
0.9578 18.65 3600 0.6792 0.4118
0.9272 20.73 4000 0.6863 0.4156
0.9321 22.8 4400 0.6535 0.3972
0.8802 24.87 4800 0.6766 0.3906
0.844 26.94 5200 0.6782 0.3949
0.8387 29.02 5600 0.6916 0.3921
0.8042 31.09 6000 0.6806 0.3797
0.793 33.16 6400 0.7120 0.3831
0.7567 35.23 6800 0.6862 0.3808
0.7463 37.31 7200 0.6893 0.3709
0.7053 39.38 7600 0.7096 0.3701
0.6906 41.45 8000 0.6921 0.3676
0.6891 43.52 8400 0.7167 0.3663
0.658 45.6 8800 0.6833 0.3580
0.6576 47.67 9200 0.6914 0.3569
0.6358 49.74 9600 0.6922 0.3551

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_8_0 with split test
python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi-with-lm --dataset mozilla-foundation/common_voice_8_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-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_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 8 "test" (WER):

Without LM With LM (run ./eval.py)
26.209 15.899