68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
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from typing import Any, Mapping, TypedDict
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import numpy as np
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from faster_whisper.audio import decode_audio # type: ignore
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SUPPORTED_MODELS = ["tiny", "tiny.en", "small", "small.en", "base", "base.en", "medium", "medium.en", "large-v2", "large-v3"]
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WordData = TypedDict(
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"WordData", {"word": str, "start": float | str, "end": float | str, "score": float}
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)
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Segment = TypedDict(
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"Segment",
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{
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"id": str,
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"text": str,
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"start": float | str,
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"end": float | str,
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"score": float,
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"words": list[WordData],
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},
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)
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Transcription = TypedDict(
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"Transcription",
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{
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"text": str,
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"language": str,
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"duration": float,
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"segments": list[Segment],
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},
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)
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class Backend:
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name: str = "faster-whisper"
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model_size: str
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def __post_init__(self):
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if self.model_size not in self.supported_model_sizes():
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raise ValueError(f"model must be one of {self.supported_model_sizes()}")
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def supported_backends(self):
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"""
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This is of not much use as of the moment, If we ever support multiple
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backends this can be utilized.
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"""
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return ["faster-whisper"]
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def model_path(self) -> str:
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"""
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Returns the local path to the model, error-out if unavailable
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"""
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raise NotImplementedError()
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def supported_model_sizes(self) -> list[str]:
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return SUPPORTED_MODELS
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def download_model(self):
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raise NotImplementedError()
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def load(self):
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raise NotImplementedError()
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def transcribe(self, input: np.ndarray) -> list[Segment]:
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"""
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This should return word level transcription data.
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"""
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raise NotImplementedError() |