Create ffmpeg_handler.py

ffmpeg_handler.py

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+from typing import Dict, Any, List
+from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
+import torch
+from transformers.pipelines.audio_utils import ffmpeg_read
+#import io
+
+
+#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+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, device=)
+        
+
+        
+        # Move model to device
+#        self.model.to(device)
+        
+    def __call__(self, data: Any) -> List[Dict[str, str]]:
+        print('==========NEW PROCESS=========')
+
+        inputs = data.pop("inputs", data)
+        audio_nparray = ffmpeg_read(inputs, 16000)
+        audio_tensor= torch.from_numpy(audio_nparray)
+
+        
+        transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-tiny")
+        transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
+        result = transcribe(audio_tensor)
+        
+
+        return result