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import importlib.resources
import json
import torch
from pathlib import Path
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from question_retriever import get_question
from tools.data_helpers import get_file_path
__resources_path = Path(str(importlib.resources.files("data")))
def test_whisper() -> None:
task_id = "1f975693-876d-457b-a649-393859e79bf3"
question = json.loads(get_question(task_id=task_id))
audio_file = get_file_path(file_name=question["file_name"])
# cuda_available = torch.cuda.is_available()
cuda_available = False
device = "cuda:0" if cuda_available else "cpu"
torch_dtype = torch.float16 if cuda_available else torch.float32
model_id = "openai/whisper-large-v3-turbo"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
sample = audio_file
generate_kwargs = {
"return_timestamps": True,
}
result = pipe(sample, generate_kwargs=generate_kwargs)
print(result["text"])
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