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- from dataclasses import dataclass, field
- from typing import Optional
- @dataclass
- class WhisperSTTHandlerArguments:
- stt_model_name: str = field(
- default="distil-whisper/distil-large-v3",
- metadata={
- "help": "The pretrained Whisper model to use. Default is 'distil-whisper/distil-large-v3'."
- },
- )
- stt_device: str = field(
- default="cuda",
- metadata={
- "help": "The device type on which the model will run. Default is 'cuda' for GPU acceleration."
- },
- )
- stt_torch_dtype: str = field(
- default="float16",
- metadata={
- "help": "The PyTorch data type for the model and input tensors. One of `float32` (full-precision), `float16` or `bfloat16` (both half-precision)."
- },
- )
- stt_compile_mode: str = field(
- default=None,
- metadata={
- "help": "Compile mode for torch compile. Either 'default', 'reduce-overhead' and 'max-autotune'. Default is None (no compilation)"
- },
- )
- stt_gen_max_new_tokens: int = field(
- default=128,
- metadata={
- "help": "The maximum number of new tokens to generate. Default is 128."
- },
- )
- stt_gen_num_beams: int = field(
- default=1,
- metadata={
- "help": "The number of beams for beam search. Default is 1, implying greedy decoding."
- },
- )
- stt_gen_return_timestamps: bool = field(
- default=False,
- metadata={
- "help": "Whether to return timestamps with transcriptions. Default is False."
- },
- )
- stt_gen_task: str = field(
- default="transcribe",
- metadata={
- "help": "The task to perform, typically 'transcribe' for transcription. Default is 'transcribe'."
- },
- )
- language: Optional[str] = field(
- default='en',
- metadata={
- "help": """The language for the conversation.
- Choose between 'en' (english), 'fr' (french), 'es' (spanish),
- 'zh' (chinese), 'ko' (korean), 'ja' (japanese), 'hi' (hindi) or 'None'.
- If using 'auto', the language is automatically detected and can
- change during the conversation. Default is 'en'."""
- },
- )
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