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---
library_name: peft
base_model: openai/whisper-large-v3
---
# Model Card for Model ID
As of our knowledge SOTA in swiss german with wer=14.269151618793657 and normalized_wer=12.800897299473698.
## Model Details
Trained on RTX 3070 for 30 hours using SwissDial all Dialects with following guide: https://github.com/Vaibhavs10/fast-whisper-finetuning/blob/main/Whisper_w_PEFT.ipynb
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Flurin17
- **Language(s) (NLP):** swiss-german
- **License:** IDK ask openai
- **Finetuned from model [optional]:** openai/whisper-large-v3
### Model Sources [optional]
## Uses
from peft import PeftModel, PeftConfig
from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer
peft_model_id = "Flurin17/whisper-large-v3-peft-swiss-german" # Use the same model ID as before.
peft_config = PeftConfig.from_pretrained(peft_model_id)
model = WhisperForConditionalGeneration.from_pretrained(
peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto"
)
model = PeftModel.from_pretrained(model, peft_model_id)
model.config.use_cache = True
from transformers import WhisperFeatureExtractor
feature_extractor = WhisperFeatureExtractor.from_pretrained(peft_model_id)
from transformers import WhisperTokenizer
tokenizer = WhisperTokenizer.from_pretrained(peft_model_id, language=language, task=task)
from transformers import AutomaticSpeechRecognitionPipeline
import torch
pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
with torch.cuda.amp.autocast():
result = pipe(r"L:\Coding\random\audio.mp3", generate_kwargs={"language": "german"})
print(result["text"])
### Framework versions
- PEFT 0.7.1 |