README.md

---
library_name: transformers
pipeline_tag: automatic-speech-recognition
license: apache-2.0
tags:
- automatic-speech-recognition
- whisper
- english
- fine-tuned
- speech-to-text
---

# Model Card for dk2325/whisper-tiny-finetuned

Whisper Tiny English fine-tuned for improved general English ASR performance.

## Model Details

### Model Description

This model is a fine-tuned ASR checkpoint based on Whisper Tiny English, trained to improve transcription quality on English speech while remaining lightweight and fast.

- Developed by: DK2325
- Funded by: Self-funded personal project
- Shared by: DK2325
- Model type: Seq2Seq speech-to-text transformer (Whisper)
- Language(s): English
- License: Apache-2.0
- Finetuned from model: openai/whisper-tiny.en

### Model Sources

- Repository: https://github.com/DK2325/ASR_Finetuning_openai-whisper-tiny.en
- Paper: https://arxiv.org/abs/2212.04356
- Demo: Not available

## Uses

### Direct Use

Use this model for automatic speech recognition of English audio such as:
- Read speech
- Lectures
- Voice notes
- General transcription tasks

### Downstream Use

Can be integrated into:
- Subtitle generation tools
- ASR APIs
- Search/indexing pipelines for spoken content

### Out-of-Scope Use

Not intended for:
- Non-English transcription
- High-noise multi-speaker audio without preprocessing
- Safety-critical, legal, or medical decision workflows without human review

## Bias, Risks, and Limitations

- Performance varies across accents, recording quality, microphone type, and domain.
- Errors may occur on proper nouns, rare words, and technical terms.
- Model outputs should be reviewed by humans in high-stakes scenarios.

### Recommendations

Users (both direct and downstream) should be aware of model limitations.
Evaluate on your own target dataset before production deployment.

## How to Get Started with the Model

Use with Hugging Face Transformers automatic speech recognition pipeline.
```python
    from transformers import pipeline

    asr = pipeline(
        "automatic-speech-recognition",
        model="dk2325/whisper-tiny-finetuned",
        device=-1
    )

    result = asr(
        "path/to/audio.wav",
        generate_kwargs={"language": "en", "task": "transcribe"}
    )

    print(result["text"])
```
## Training Details

### Training Data

Fine-tuned on English speech data prepared through project manifests (LibriSpeech-style pipeline).

### Training Procedure

#### Preprocessing

- Audio processed with Whisper feature extractor
- Text tokenized with Whisper tokenizer/processor
- Seq2Seq training with standard ASR collation

#### Training Hyperparameters

- Training regime: fp16 mixed precision
- Learning rate: 1e-5
- Optimizer: AdamW
- Gradient accumulation: used for low-VRAM setup

#### Speeds, Sizes, Times

Training was performed on a local low-resource setup (4GB VRAM class GPU).
Precise training-time profiling was not fully standardized.

## Evaluation

### Testing Data, Factors and Metrics

#### Testing Data

Project validation split (English ASR setup).

#### Factors

- Baseline model vs fine-tuned model

#### Metrics

- Word Error Rate (WER)

### Results

- Base WER: 0.2806
- Fine-tuned WER: 0.0586

#### Summary

Fine-tuning substantially reduced WER compared to the base Whisper Tiny English checkpoint in project validation.

## Model Examination

No formal interpretability study was performed.

## Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator:
https://mlco2.github.io/impact#compute

- Hardware Type: Local consumer GPU (4GB VRAM class)
- Hours used: Not precisely tracked
- Cloud Provider: N/A
- Compute Region: N/A
- Carbon Emitted: Not measured

## Technical Specifications

### Model Architecture and Objective

Whisper Tiny English encoder-decoder transformer fine-tuned for English speech-to-text transcription.

### Compute Infrastructure

Local machine training setup.

#### Hardware

Consumer GPU with 4GB VRAM class constraints.

#### Software

Python, PyTorch, Hugging Face Transformers, Datasets, Evaluate.

## Citation

### BibTeX
```bibtex
@misc{dk2325_whisper_tiny_finetuned_2026,
  title={Whisper Tiny English Fine-Tuned ASR},
  author={DK2325},
  year={2026},
  howpublished={\url{https://huggingface.co/dk2325/whisper-tiny-finetuned}}
}
```
### APA

DK2325. (2026). Whisper Tiny English Fine-Tuned ASR. Hugging Face. https://huggingface.co/dk2325/whisper-tiny-finetuned

## Glossary

- ASR: Automatic Speech Recognition
- WER: Word Error Rate (lower is better)

## More Information

This model is part of an end-to-end fine-tuning and deployment project focused on practical ASR improvements under limited hardware constraints.

## Model Card Authors

DK2325

## Model Card Contact

Hugging Face profile: https://huggingface.co/dk2325