Rename handler to handler.py

handler

@@ -1,88 +0,0 @@
-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=""):
-        # Load the model and tokenizer
-#        #self.model = WhisperForCTC.from_pretrained(path)
-        self.tokenizer = WhisperTokenizer.from_pretrained(path)        
-        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(path)
-
-        
-        # 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

handler.py

@@ -0,0 +1,20 @@
+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