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from typing import Dict |
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from transformers.pipelines.audio_utils import ffmpeg_read |
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import whisper |
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import torch |
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SAMPLE_RATE = 16000 |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.model = whisper.load_model("medium") |
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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 deserialized audio file as bytes |
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Return: |
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A :obj:`dict`:. base64 encoded image |
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""" |
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inputs = data.pop("inputs", data) |
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audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE) |
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audio_tensor= torch.from_numpy(audio_nparray) |
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result = self.model.transcribe(audio_nparray) |
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return {"text": result["text"]} |
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