| 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 |