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
Model save
Browse files
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: openai/whisper-small.en
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: 2026-02-25_02-15-51
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# 2026-02-25_02-15-51
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This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2900
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- Wer: 11.7695
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 10000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:------:|:----:|:---------------:|:-------:|
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| 1.1276 | 0.1123 | 500 | 0.3356 | 13.7752 |
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| 0.2792 | 0.2247 | 1000 | 0.3113 | 12.9679 |
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| 0.2666 | 0.3370 | 1500 | 0.3035 | 12.6108 |
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| 0.2616 | 0.4493 | 2000 | 0.2999 | 12.2032 |
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| 0.2563 | 0.5617 | 2500 | 0.2986 | 13.2479 |
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| 0.2501 | 0.6740 | 3000 | 0.2929 | 12.4547 |
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| 0.2561 | 0.7863 | 3500 | 0.2910 | 11.9032 |
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| 0.2497 | 0.8987 | 4000 | 0.2898 | 12.0265 |
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| 0.2336 | 1.0110 | 4500 | 0.2877 | 11.4051 |
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| 0.1795 | 1.1233 | 5000 | 0.2915 | 11.8667 |
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| 0.1837 | 1.2357 | 5500 | 0.2909 | 11.7313 |
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| 0.1763 | 1.3480 | 6000 | 0.2900 | 11.7695 |
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### Framework versions
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- Transformers 4.51.3
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- Pytorch 2.6.0+cu124
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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generation_config.json
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{
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"alignment_heads": [
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8
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[
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5
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[
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8,
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7
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[
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0
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[
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[
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10
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10,
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10,
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10,
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[
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10,
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3
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[
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10,
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6
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[
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10,
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11
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[
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"begin_suppress_tokens": [
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220,
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],
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"bos_token_id": 50257,
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"decoder_start_token_id": 50257,
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"eos_token_id": 50256,
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"forced_decoder_ids": [
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],
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"is_multilingual": false,
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"max_initial_timestamp_index": 50,
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"max_length": 448,
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"no_timestamps_token_id": 50362,
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"pad_token_id": 50256,
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"prev_sot_token_id": 50360,
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"suppress_tokens": [],
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"transformers_version": "4.51.3"
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}
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model.safetensors
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size 966992008
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runs/Feb25_02-15-52_v330-007.ls6.tacc.utexas.edu/events.out.tfevents.1772007364.v330-007.ls6.tacc.utexas.edu.63755.0
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size 12897
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