mirror of
https://github.com/JefferyHcool/BiliNote.git
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43b88c85fa
- 新增 events 模块，包括 handlers 和 signals 子模块 - 在 handlers 中实现 cleanup_temp_files 函数，用于清理转写临时文件 - 在 signals 中定义 transcription_finished 信号 - 修改 main.py，添加 startup_event 函数以注册事件处理器- 更新 WhisperTranscriber 类，增加 on_finish 方法并发送转写完成信号 - 在 base.py 中添加 TranscriberBase 类的 on_finish 方法占位符
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
from faster_whisper import WhisperModel
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from app.decorators.timeit import timeit
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from app.models.transcriber_model import TranscriptSegment, TranscriptResult
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from app.transcriber.base import Transcriber
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from app.utils.env_checker import is_cuda_available, is_torch_installed
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from app.utils.path_helper import get_model_dir
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from events import transcription_finished
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'''
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Size of the model to use (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en, distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2, distil-large-v3, large-v3-turbo, or turbo
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'''
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class WhisperTranscriber(Transcriber):
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# TODO:修改为可配置
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def __init__(
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self,
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model_size: str = "base",
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device: str = 'cpu',
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compute_type: str = None,
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cpu_threads: int = 1,
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):
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if device == 'cpu' or device is None:
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self.device = 'cpu'
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else:
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self.device = "cuda" if self.is_cuda() else "cpu"
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if device == 'cuda' and self.device == 'cpu':
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print('没有 cuda 使用 cpu进行计算')
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self.compute_type = compute_type or ("float16" if self.device == "cuda" else "int8")
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model_path = get_model_dir("whisper")
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self.model = WhisperModel(
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model_size,
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device=self.device,
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# compute_type="int8", # 或 "float16"
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cpu_threads=cpu_threads,
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download_root=model_path
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)
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@staticmethod
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def is_torch_installed() -> bool:
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try:
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import torch
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return True
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except ImportError:
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return False
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@staticmethod
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def is_cuda() -> bool:
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try:
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if is_cuda_available():
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print("✅ CUDA 可用，使用 GPU")
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return True
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elif is_torch_installed():
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print("⚠️ 只装了 torch，但没有 CUDA，用 CPU")
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return False
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else:
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print("❌ 还没有安装 torch，请先安装")
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return False
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except ImportError:
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return False
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@timeit
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def transcript(self, file_path: str) -> TranscriptResult:
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try:
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segments_raw, info = self.model.transcribe(file_path)
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segments = []
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full_text = ""
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for seg in segments_raw:
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text = seg.text.strip()
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full_text += text + " "
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segments.append(TranscriptSegment(
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start=seg.start,
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end=seg.end,
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text=text
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))
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result= TranscriptResult(
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language=info.language,
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full_text=full_text.strip(),
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segments=segments,
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raw=info
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)
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self.on_finish(file_path, result)
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return result
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except Exception as e:
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print(f"转写失败：{e}")
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def on_finish(self,video_path:str,result: TranscriptResult)->None:
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print("转写完成")
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transcription_finished.send({
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"file_path": video_path,
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})
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