README.md

---
language:
- en
metrics:
- wer
base_model:
- openai/whisper-tiny
tags:
- whisper
- stt
- speech-to-text
- speech
- automatic-speech-recognition
- fine-tuned
---
# Whisper Tiny Llm Lingo

Fine-tuned Whisper model based on [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny).

## Training Results

| Metric | Base Model | Fine-tuned |
|--------|------------|------------|
| WER    | 107.85% | 34.30% |

**Improvement:** 73.55% WER reduction (lower is better)

## Training Details

- **Base Model:** [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny)
- **Training Dataset:** [Trelis/llm-lingo](https://huggingface.co/datasets/Trelis/llm-lingo)
- **Train Loss:** 2.5791
- **Training Time:** 19 seconds

## Inference

```python
from transformers import pipeline

asr = pipeline("automatic-speech-recognition", model="Trelis/whisper-tiny-llm-lingo")
result = asr("path/to/audio.wav")
print(result["text"])
```

## Training Logs

Full training logs are available in [training_log.txt](training_log.txt).

---
*Fine-tuned using [Trelis Studio](https://studio.trelis.com)*