Instructions to use slplab/whisper-large_v2_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slplab/whisper-large_v2_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="slplab/whisper-large_v2_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("slplab/whisper-large_v2_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("slplab/whisper-large_v2_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 674 Bytes
139c456 e769edb 139c456 e769edb 139c456 e769edb 139c456 9ee43a8 139c456 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | from typing import Dict, Any, List
from transformers import pipeline
import torch
#### USE of PIPELINE
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
self.pipe = pipeline(task='automatic-speech-recognition', model=path, device=device)
def __call__(self, data: Any) -> List[Dict[str, str]]:
inputs = data.pop("inputs", data)
transcribe = self.pipe
transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
result = transcribe(inputs)
return result |