---
library_name: peft
base_model: openai/whisper-large-v2
datasets:
- mozilla-foundation/common_voice_16_0
language:
- ja
metrics:
- wer
---

# Model Card for Model ID

Japanese transcription, testing in progress to see results, main personal use cases are japanese comedy 

usage 9GB vram with this Lora

## Model Details

### Model Description

openai-whisper-large-v2-LORA-ja


- **Developed by:** FZNX
- **Model type:** PEFT LORA 
- **Language(s) (NLP):** Fine tune Japanese on whisper common 16
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Whisper Large V2


## How to Get Started with the Model

import torch
from transformers import (
    AutomaticSpeechRecognitionPipeline,
    WhisperForConditionalGeneration,
    WhisperTokenizer,
    WhisperProcessor,
)
from peft import PeftModel, PeftConfig

peft_model_id = "fznx92/openai-whisper-large-v2-ja-transcribe-colab"
sample = "insert mp3 file location here"

language = "japanese"
task = "transcribe"

peft_config = PeftConfig.from_pretrained(peft_model_id)
model = WhisperForConditionalGeneration.from_pretrained(
    peft_config.base_model_name_or_path,
)
model = PeftModel.from_pretrained(model, peft_model_id)
model.to("cuda").half()

processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)

pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, batch_size=8, torch_dtype=torch.float16, device="cuda:0")

def transcribe(audio, return_timestamps=False):
    text = pipe(audio, chunk_length_s=30, return_timestamps=return_timestamps, generate_kwargs={"language": language, "task": task})["text"]
    return text

transcript = transcribe(sample)
print(transcript)

### Training Data

Common Voice 16 dataset

### Training Procedure 

via Google Colab T5 @ 6 hours 

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->


### Framework versions

- PEFT 0.7.1