Instructions to use SatwikDutta/2026-02-25_02-15-51 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SatwikDutta/2026-02-25_02-15-51 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SatwikDutta/2026-02-25_02-15-51")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SatwikDutta/2026-02-25_02-15-51") model = AutoModelForSpeechSeq2Seq.from_pretrained("SatwikDutta/2026-02-25_02-15-51") - Notebooks
- Google Colab
- Kaggle
File size: 2,220 Bytes
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library_name: transformers
license: apache-2.0
base_model: openai/whisper-small.en
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 2026-02-25_02-15-51
results: []
---
<!-- 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. -->
# 2026-02-25_02-15-51
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2900
- Wer: 11.7695
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch 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: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.1276 | 0.1123 | 500 | 0.3356 | 13.7752 |
| 0.2792 | 0.2247 | 1000 | 0.3113 | 12.9679 |
| 0.2666 | 0.3370 | 1500 | 0.3035 | 12.6108 |
| 0.2616 | 0.4493 | 2000 | 0.2999 | 12.2032 |
| 0.2563 | 0.5617 | 2500 | 0.2986 | 13.2479 |
| 0.2501 | 0.6740 | 3000 | 0.2929 | 12.4547 |
| 0.2561 | 0.7863 | 3500 | 0.2910 | 11.9032 |
| 0.2497 | 0.8987 | 4000 | 0.2898 | 12.0265 |
| 0.2336 | 1.0110 | 4500 | 0.2877 | 11.4051 |
| 0.1795 | 1.1233 | 5000 | 0.2915 | 11.8667 |
| 0.1837 | 1.2357 | 5500 | 0.2909 | 11.7313 |
| 0.1763 | 1.3480 | 6000 | 0.2900 | 11.7695 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|