Update handler.py
#1
by
spellingdragon
- opened
- handler.py +5 -3
handler.py
CHANGED
@@ -8,16 +8,17 @@ class EndpointHandler():
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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model_id = "openai/whisper-large-v3"
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-
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
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)
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-
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processor = AutoProcessor.from_pretrained(model_id)
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self.pipeline = pipeline(
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"automatic-speech-recognition",
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-
model=
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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max_new_tokens=128,
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@@ -27,6 +28,7 @@ class EndpointHandler():
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torch_dtype=torch_dtype,
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device=device,
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)
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def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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model_id = "openai/whisper-large-v3"
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+
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
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)
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model.to(device)
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processor = AutoProcessor.from_pretrained(model_id)
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self.pipeline = pipeline(
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"automatic-speech-recognition",
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+
model=model,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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max_new_tokens=128,
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torch_dtype=torch_dtype,
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device=device,
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)
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+
self.model = model
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def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
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