Instructions to use rakshit10/whisper-small-hi_in-fleurs-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rakshit10/whisper-small-hi_in-fleurs-debug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rakshit10/whisper-small-hi_in-fleurs-debug")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rakshit10/whisper-small-hi_in-fleurs-debug") model = AutoModelForSpeechSeq2Seq.from_pretrained("rakshit10/whisper-small-hi_in-fleurs-debug") - Notebooks
- Google Colab
- Kaggle
whisper-small-hi_in-fleurs-debug
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6015
- Wer: 0.4857
- Cer: 0.1830
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| No log | 2.5 | 5 | 0.6015 | 0.4857 | 0.1830 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for rakshit10/whisper-small-hi_in-fleurs-debug
Base model
openai/whisper-small