Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hindi
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use CKSINGH/whisper-small-hi-finalBatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CKSINGH/whisper-small-hi-finalBatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CKSINGH/whisper-small-hi-finalBatch")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CKSINGH/whisper-small-hi-finalBatch") model = AutoModelForSpeechSeq2Seq.from_pretrained("CKSINGH/whisper-small-hi-finalBatch") - Notebooks
- Google Colab
- Kaggle
Whisper small Hi - CKS 2411 finalbatch
This model is a fine-tuned version of CKSINGH/whisper-small-hi-graminVoice on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4932
- Wer: 34.6890
- Cer: 23.4230
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: 1.75e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.1262 | 8.77 | 500 | 0.4595 | 29.7847 | 17.7683 |
| 0.006 | 17.54 | 1000 | 0.4932 | 34.6890 | 23.4230 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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