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from typing import Dict, Any, List
#from transformers import WhisperForCTC, WhisperTokenizer
from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
import torch
#from functools import partial
#import torchaudio
import soundfile as sf
import io
# Check for GPU
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
self.tokenizer = WhisperTokenizer.from_pretrained(path, language="korean", task='transcribe')
self.model = WhisperForConditionalGeneration.from_pretrained(path)
#self.tokenizer = WhisperTokenizer.from_pretrained(path)
self.processor = WhisperProcessor.from_pretrained(path, language="korean", task='transcribe')
#self.processor = AutoProcessor.from_pretrained(path)
#self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
self.feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
# Move model to device
# self.model.to(device)
def __call__(self, data: Any) -> List[Dict[str, str]]:
print('HELLO')
#print(f"{data}")
#inputs = data.pop("inputs", data)
#print(f'1. inputs: {inputs}')
inputs, _ = sf.read(io.BytesIO(data['inputs']))
#inputs, _ = sf.read(data['inputs'])
print(f'2. inputs: {inputs}')
input_features = self.feature_extractor(inputs, sampling_rate=16000).input_features[0]
print(f'3. input_features: {input_features}')
input_features_tensor = torch.tensor(input_features).unsqueeze(0)
input_ids = self.model.generate(input_features_tensor)
print(f'4. input_ids: {input_ids}')
transcription = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0]
#inputs, _ = torchaudio.load(inputs, normalize=True)
#input_features = self.processor.feature_extractor(inputs, sampling_rate=16000).input_features[0]
#input_ids = self.processor.tokenizer(input_features, return_tensors="pt").input_ids
#generated_ids = self.model.generate(input_ids)
# #transcription = self.pipe(inputs, generate_kwargs = {"task":"transcribe", "language":"<|ko|>"})
# #transcription = self.pipe(inputs)
# #print(input)
# inputs = self.processor(inputs, retun_tensors="pt")
# #input_features = {key: value.to(device) for key, value in input_features.items()}
# input_features = inputs.input_features
# generated_ids = self.model.generate(input_features)
# #generated_ids = self.model.generate(inputs=input_features)
# #self.model.generate = partial(self.model.generate, language="korean", task="transcribe")
# #generated_ids = self.model.generate(inputs = input_features)
#transcription = self.processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
#transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return transcription
#original __call__
# def __call__(self, data: Any) -> List[Dict[str, str]]:
# inputs = data.pop("inputs", data)
# # Preprocess the input audio
# input_features = self.tokenizer(inputs, return_tensors="pt", padding="longest")
# input_features = {key: value.to(device) for key, value in input_features.items()}
# # Perform automatic speech recognition
# with torch.no_grad():
# logits = self.model(**input_features).logits
# predicted_ids = torch.argmax(logits, dim=-1)
# transcription = self.tokenizer.batch_decode(predicted_ids)[0]
# response = [{"task": "transcribe", "language": "korean", "transcription": transcription}]
# return response |