mirror of
https://github.com/shirayu/whispering.git
synced 2024-05-20 01:08:14 +00:00
80 lines
1.8 KiB
Python
80 lines
1.8 KiB
Python
#!/usr/bin/env python3
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import sys
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from typing import List, Optional
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import numpy as np
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import torch
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from pydantic import BaseModel, root_validator
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class WhisperConfig(BaseModel):
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model_name: str
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device: str
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language: str
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fp16: bool = True
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@root_validator
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def validate_model_name(cls, values):
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if values["model_name"].endswith(".en") and values["language"] not in {
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"en",
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"English",
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}:
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raise ValueError("English only model")
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return values
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class Context(BaseModel, arbitrary_types_allowed=True):
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timestamp: float = 0.0
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buffer_tokens: List[torch.Tensor] = []
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buffer_mel: Optional[torch.Tensor] = None
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vad: bool = True
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temperatures: List[float]
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allow_padding: bool = False
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patience: Optional[float] = None
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compression_ratio_threshold: Optional[float] = 2.4
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logprob_threshold: Optional[float] = -1.0
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no_captions_threshold: Optional[float] = 0.6
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best_of: int = 5
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beam_size: Optional[int] = None
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no_speech_threshold: Optional[float] = 0.6
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logprob_threshold: Optional[float] = -1.0
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compression_ratio_threshold: Optional[float] = 2.4
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buffer_threshold: Optional[float] = 0.5
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vad_threshold: float = 0.5
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class ParsedChunk(BaseModel):
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start: float
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end: float
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text: str
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tokens: List[int]
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temperature: float
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avg_logprob: float
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compression_ratio: float
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no_speech_prob: float
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class SpeechSegment(BaseModel, arbitrary_types_allowed=True):
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start_block_idx: int
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end_block_idx: int
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audio: np.ndarray
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class StdoutWriter:
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def open(self, *args, **kwargs):
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return self
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def __enter__(self, *args, **kwargs):
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return self
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def __exit__(self):
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pass
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def flush(self):
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sys.stdout.flush()
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def write(self, text):
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sys.stdout.write(text)
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