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import torch
from typing import Dict, List, Any
from transformers import (
AutomaticSpeechRecognitionPipeline,
WhisperForConditionalGeneration,
WhisperTokenizer,
WhisperProcessor,
pipeline
)
from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model, PeftConfig
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# peft_model_id = "cathyi/tw-tw-openai-whisper-large-v2-Lora-epoch5-total5epoch"
peft_model_id = path
language = "Chinese"
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)
tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
feature_extractor = processor.feature_extractor
self.forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
self.pipeline = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
# self.pipeline = pipeline(task= "automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
self.pipeline.model.config.forced_decoder_ids = self.pipeline.tokenizer.get_decoder_prompt_ids(language="Chinese", task="transcribe")
self.pipeline.model.generation_config.forced_decoder_ids = self.pipeline.model.config.forced_decoder_ids # just to be sure!
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs", data)
with torch.cuda.amp.autocast():
prediction = self.pipeline(inputs, generate_kwargs={"forced_decoder_ids": self.forced_decoder_ids}, max_new_tokens=255)
# prediction = self.pipeline(inputs, return_timestamps=False)
return prediction |