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from typing import Dict, List, Any |
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import torch |
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from transformers.pipelines.audio_utils import ffmpeg_read |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, pipeline |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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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-turbo" |
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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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chunk_length_s=30, |
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batch_size=16, |
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return_timestamps=True, |
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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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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing : |
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- "label": A string representing what the label/class is. There can be multiple labels. |
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- "score": A score between 0 and 1 describing how confident the model is for this label/class. |
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""" |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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if parameters is not None: |
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result = self.pipeline(inputs, return_timestamps=True, **parameters) |
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else: |
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result = self.pipeline(inputs, return_timestamps=True, generate_kwargs={"task": "translate"}) |
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return result |
