metadata
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 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