---
pipeline_tag: automatic-speech-recognition
language: mri
license: apache-2.0
tags:
  - trimmed
  - whisper
library_name: transformers
base_model: openai/whisper-large-v3-turbo
base_model_relation: quantized
---

# whisper-large-v3-turbo-mri-32768

This model is a vocabulary-pruned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo)
optimised for **Maori** via the
[trimming](https://huggingface.co/blog/introduction-to-trimming) method.

Only the **decoder** token-embedding table and **lm_head** (proj_out) were pruned.
The encoder is **fully unchanged**, so audio feature extraction is identical to
the original model.

## Vocabulary reduction

| Component | Original | Pruned |
|-----------|----------|--------|
| vocab_size | 50257 | 31159 |
| embed_tokens | 66.4M | 41.9M |
| proj_out | 0.0M | 0.0M |
| **Total** | **808.9M** | **784.4M** |

## Usage

```python
from transformers import WhisperForConditionalGeneration, AutoTokenizer, pipeline

model = WhisperForConditionalGeneration.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768")
tokenizer = AutoTokenizer.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768")

asr = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer)
result = asr("audio.wav", generate_kwargs={"language": "mri"})
print(result["text"])
```

## ⚠️ Limitations

Tokens not used in the selected language(s) were removed. The model may produce
incorrect output for other languages.