metadata
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.
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
- Training Dataset: Trelis/llm-lingo
- Train Loss: 2.5791
- Training Time: 19 seconds
Inference
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.
Fine-tuned using Trelis Studio