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30da57ddab
-移除 .env.example 中的后端专用 AI 配置 - 在 GPT 模型中添加新字段：style、extras 和 _format - 修改转录器的 on_finish 方法调用 - 更新 GPT 提示模板，增加时间标记要求
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import mlx_whisper
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from pathlib import Path
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import os
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import platform
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from huggingface_hub import snapshot_download
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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.logger import get_logger
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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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logger = get_logger(__name__)
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class MLXWhisperTranscriber(Transcriber):
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def __init__(
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self,
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model_size: str = "base"
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):
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# 检查平台
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if platform.system() != "Darwin":
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raise RuntimeError("MLX Whisper 仅支持 Apple 平台")
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# 检查环境变量
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if os.environ.get("TRANSCRIBER_TYPE") != "mlx-whisper":
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raise RuntimeError("必须设置环境变量 TRANSCRIBER_TYPE=mlx-whisper 才能使用 MLX Whisper")
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self.model_size = model_size
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self.model_name = f"mlx-community/whisper-{model_size}"
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self.model_path = None
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# 设置模型路径
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model_dir = get_model_dir("mlx-whisper")
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self.model_path = os.path.join(model_dir, self.model_name)
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# 检查并下载模型
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if not Path(self.model_path).exists():
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logger.info(f"模型 {self.model_name} 不存在，开始下载...")
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snapshot_download(
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self.model_name,
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local_dir=self.model_path,
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local_dir_use_symlinks=False,
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)
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logger.info("模型下载完成")
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logger.info(f"初始化 MLX Whisper 转录器，模型：{self.model_name}")
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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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# 使用 MLX Whisper 进行转录
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result = mlx_whisper.transcribe(
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file_path,
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path_or_hf_repo=f"{self.model_name}"
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)
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# 转换为标准格式
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segments = []
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full_text = ""
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for segment in result["segments"]:
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text = segment["text"].strip()
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full_text += text + " "
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segments.append(TranscriptSegment(
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start=segment["start"],
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end=segment["end"],
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text=text
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))
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transcript_result = TranscriptResult(
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language=result.get("language", "unknown"),
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full_text=full_text.strip(),
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segments=segments,
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raw=result
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)
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# self.on_finish(file_path, transcript_result)
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return transcript_result
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except Exception as e:
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logger.error(f"MLX Whisper 转写失败：{e}")
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raise e
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def on_finish(self, video_path: str, result: TranscriptResult) -> None:
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logger.info("MLX Whisper 转写完成")
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transcription_finished.send({
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"file_path": video_path,
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}) |