easydel.inference.vwhisper.server

easydel.inference.vwhisper.server#

class easydel.inference.vwhisper.server.ResponseFormat(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: str, Enum

json = 'json'#
srt = 'srt'#
text = 'text'#
verbose_json = 'verbose_json'#
vtt = 'vtt'#
class easydel.inference.vwhisper.server.TranscriptionResponse(*, text: str, segments: Optional[List[Dict[str, Any]]] = None)[source]#

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

segments: Optional[List[Dict[str, Any]]]#
text: str#
class easydel.inference.vwhisper.server.WhisperModel(model_name=None, dtype=<class 'jax.numpy.bfloat16'>)[source]#

Bases: object

Singleton wrapper for the Whisper model to avoid reloading.

easydel.inference.vwhisper.server.create_whisper_app(model_name: str = 'openai/whisper-large-v3-turbo', dtype=<class 'jax.numpy.bfloat16'>)[source]#

Create a FastAPI app for Whisper transcription.

easydel.inference.vwhisper.server.run_server(model_name: str = 'openai/whisper-large-v3-turbo', host: str = '0.0.0.0', port: int = 8000, dtype=<class 'jax.numpy.bfloat16'>)[source]#

Run the Whisper FastAPI server.

Parameters

  • model_name – Name of the Whisper model to use (from HuggingFace)

  • host – Host to bind the server

  • port – Port to bind the server

  • dtype – Data type for the model (default: bfloat16)

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