Initial commit

transcription-api/backends/backend.py

@@ -0,0 +1,68 @@
+
+from typing import Any, Mapping, TypedDict
+import numpy as np
+from faster_whisper.audio import decode_audio  # type: ignore
+
+SUPPORTED_MODELS = ["tiny", "tiny.en", "small", "small.en", "base", "base.en", "medium", "medium.en", "large-v2"]
+
+WordData = TypedDict(
+    "WordData", {"word": str, "start": float | str, "end": float | str, "score": float}
+)
+
+Segment = TypedDict(
+    "Segment",
+    {
+        "id": str,
+        "text": str,
+        "start": float | str,
+        "end": float | str,
+        "score": float,
+        "words": list[WordData],
+    },
+)
+
+Transcription = TypedDict(
+    "Transcription",
+    {   
+        "text": str,
+        "language": str,
+        "duration": float,
+        "segments": list[Segment],
+    },
+)
+
+class Backend:
+    name: str = "faster-whisper"
+    model_size: str
+
+    def __post_init__(self):
+        if self.model_size not in self.supported_model_sizes():
+            raise ValueError(f"model must be one of {self.supported_model_sizes()}")
+
+    def supported_backends(self):
+        """
+        This is of not much use as of the moment, If we ever support multiple
+        backends this can be utilized.
+        """
+        return ["faster-whisper"]
+
+    def model_path(self) -> str:
+        """
+        Returns the local path to the model, error-out if unavailable
+        """
+        raise NotImplementedError()
+
+    def supported_model_sizes(self) -> list[str]:
+        return SUPPORTED_MODELS
+    
+    def download_model(self):
+        raise NotImplementedError()
+
+    def load(self):
+        raise NotImplementedError()
+
+    def transcribe(self, input: np.ndarray) -> list[Segment]:
+        """
+        This should return word level transcription data.
+        """
+        raise NotImplementedError()