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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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- tunis-ai/arabic_speech_corpus |
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- THCHS-30 |
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model-index: |
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- name: lowhipa-base-comb |
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results: [] |
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pipeline_tag: automatic-speech-recognition |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lowhipa-base-comb |
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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: |
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- CommonVoice11 dataset (1k samples each from Greek, Finnish, Hungarian, Japanese, Maltese, Polish, Tamil) with G2P-based IPA transcriptions |
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- Mandarin THCHS-30 database (https://arxiv.org/pdf/1512.01882) with IPA transcriptions by Taubert (2023, https://zenodo.org/records/7528596) (1k samples) |
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- Arabic Speech Corpus (https://en.arabicspeechcorpus.com) with custom IPA transcriptions transliterated from the provided Buckwalter transcriptions (1k samples) (https://doi.org/10.5281/zenodo.17111977) |
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## Model description |
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For deployment and description, please refer to https://github.com/jshrdt/whipa. |
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``` |
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from transformers import WhisperForConditionalGeneration, WhisperTokenizer, WhisperProcessor |
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from peft import PeftModel |
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tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-base", task="transcribe") |
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tokenizer.add_special_tokens({"additional_special_tokens": ["<|ip|>"] + tokenizer.all_special_tokens}) |
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base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") |
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base_model.generation_config.lang_to_id["<|ip|>"] = tokenizer.convert_tokens_to_ids(["<|ip|>"])[0] |
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base_model.resize_token_embeddings(len(tokenizer)) |
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whipa_model = PeftModel.from_pretrained(base_model, "jshrdt/lowhipa-base-comb") |
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whipa_model.generation_config.language = "<|ip|>" |
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whipa_model.generation_config.task = "transcribe" |
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whipa_processor = WhisperProcessor.from_pretrained("openai/whisper-base", task="transcribe") |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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### Training results |
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| Training Loss | Epoch | Validation Loss | |
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|:-------------:|:-------:|:---------------:| |
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| 1.5428 | 2.0323 | 1.2981462478637695 | |
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| 0.7498 | 4.0645 | 0.8457677960395813 | |
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| 0.5968 | 6.0968 | 0.759925901889801 | |
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| 0.5156 | 8.1290 | 0.7213243246078491 | |
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| 0.4603 | 10.1613 | 0.7064764499664307 | |
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### Framework versions |
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- PEFT 0.15.1 |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
