---
base_model: openai/whisper-base
library_name: peft
model-index:
- name: lowhipa-base-cv
  results: []
datasets:
- mozilla-foundation/common_voice_11_0
pipeline_tag: automatic-speech-recognition
---

# lowhipa-base-cv

This Whisper-for-IPA (WhIPA) model adapter is a PEFT LoRA fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on a subset of the CommonVoice11 dataset (1k samples each from Greek, Finnish, Hungarian, Japanese, Maltese, Polish, Tamil) with G2P-based IPA transcriptions.

## Model description

For deployment and description, please refer to https://github.com/jshrdt/whipa.

```
from transformers import WhisperForConditionalGeneration, WhisperTokenizer, WhisperProcessor
from peft import PeftModel

tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-base", task="transcribe")
tokenizer.add_special_tokens({"additional_special_tokens": ["<|ip|>"] + tokenizer.all_special_tokens})

base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
base_model.generation_config.lang_to_id["<|ip|>"] = tokenizer.convert_tokens_to_ids(["<|ip|>"])[0]
base_model.resize_token_embeddings(len(tokenizer))

whipa_model = PeftModel.from_pretrained(base_model, "jshrdt/lowhipa-base-cv")

whipa_model.generation_config.language = "<|ip|>"
whipa_model.generation_config.task = "transcribe"

whipa_processor = WhisperProcessor.from_pretrained("openai/whisper-base", task="transcribe")

```

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters


### Training results



### Framework versions

- PEFT 0.15.1