Create pipe_handler.py

pipe_handler.py

@@ -0,0 +1,69 @@
+from typing import Dict, Any, List
+from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
+import torch
+import soundfile as sf
+import io
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        tokenizer = WhisperTokenizer.from_pretrained('openai/whisper-large', language="korean", task='transcribe')        
+        model = WhisperForConditionalGeneration.from_pretrained(path)
+        #self.tokenizer = WhisperTokenizer.from_pretrained(path)
+        #self.processor = WhisperProcessor.from_pretrained(path, language="korean", task='transcribe')
+        processor = AutoProcessor.from_pretrained(path)
+        #self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
+        feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
+        self.pipe = pipeline(task='automatic-speech-recognition', model=path)
+        
+
+        
+        # Move model to device
+#        self.model.to(device)
+        
+    def __call__(self, data: Any) -> List[Dict[str, str]]:
+        print('==========NEW PROCESS=========')
+        transcription = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-tiny", chunk_length_s=30, device=device)
+        transcription.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
+        result = transcription(data['inputs'])
+        
+        
+        #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)
+        # #(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 result