datasets:

• mozilla-foundation/common_voice_15_0
• mozilla-foundation/common_voice_13_0 language:
• hi metrics:
• cer
• wer library_name: transformers pipeline_tag: automatic-speech-recognition model-index:
  • name: whisper-small-hi-cv results:

    • task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 15 type: mozilla-foundation/common_voice_15_0 args: hi metrics:

      • name: Test WER type: wer value: 13.9913
      • name: Test CER type: cer value: 5.8844

    • task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 args: hi metrics:

      • name: Test WER type: wer value: 23.3824
      • name: Test CER type: cer value: 10.5288

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

Model Details

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

• Loss: 0.3691
• Wer: 0.3285
• Cer: 0.0875

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: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 100

Training results

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3

SPACE

Automatic Speech Recognization in hindi