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剪辑逻辑进度80%；
待优化点： 1. 优化脚本-解说质量 2. 优化webui体验
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linyq
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@ -353,7 +353,7 @@ class VideoClipParams(BaseModel):
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bgm_file: Optional[str] = Field(default="", description="背景音乐文件")
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bgm_volume: Optional[float] = Field(default=0.2, description="背景音乐音量")
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subtitle_enabled: Optional[bool] = Field(default=False, description="是否启用字幕")
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subtitle_enabled: Optional[bool] = Field(default=True, description="是否启用字幕")
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subtitle_position: Optional[str] = Field(default="bottom", description="字幕位置") # top, bottom, center
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font_name: Optional[str] = Field(default="STHeitiMedium.ttc", description="字体名称")
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text_fore_color: Optional[str] = Field(default="#FFFFFF", description="文字前景色")
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@ -365,4 +365,3 @@ class VideoClipParams(BaseModel):
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custom_position: float = Field(default=70.0, description="自定义位置")
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n_threads: Optional[int] = 8 # 线程数，有助于提升视频处理速度
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# paragraph_number: Optional[int] = 1 # 段落数量
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@ -1,6 +1,7 @@
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import json
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import os.path
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import re
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import traceback
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from typing import Optional
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from faster_whisper import WhisperModel
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@ -11,35 +12,53 @@ import google.generativeai as genai
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from app.config import config
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from app.utils import utils
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model_size = config.whisper.get("model_size", "large-v3")
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model_size = config.whisper.get("model_size", "faster-whisper-large-v2")
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device = config.whisper.get("device", "cpu")
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compute_type = config.whisper.get("compute_type", "int8")
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model = None
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def create(audio_file, subtitle_file: str = ""):
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"""
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为给定的音频文件创建字幕文件。
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参数:
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- audio_file: 音频文件的路径。
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- subtitle_file: 字幕文件的输出路径（可选）。如果未提供，将根据音频文件的路径生成字幕文件。
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返回:
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无返回值，但会在指定路径生成字幕文件。
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"""
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global model
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if not model:
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model_path = f"{utils.root_dir()}/models/whisper-{model_size}"
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model_path = f"{utils.root_dir()}/app/models/faster-whisper-large-v2"
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model_bin_file = f"{model_path}/model.bin"
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if not os.path.isdir(model_path) or not os.path.isfile(model_bin_file):
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model_path = model_size
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logger.error(
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"请先下载 whisper 模型\n\n"
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"********************************************\n"
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"下载地址：https://huggingface.co/guillaumekln/faster-whisper-large-v2\n"
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"存放路径：app/models \n"
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"********************************************\n"
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)
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return None
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logger.info(
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f"loading model: {model_path}, device: {device}, compute_type: {compute_type}"
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f"加载模型: {model_path}, 设备: {device}, 计算类型: {compute_type}"
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)
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try:
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model = WhisperModel(
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model_size_or_path=model_path, device=device, compute_type=compute_type
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model_size_or_path=model_path, device=device, compute_type=compute_type, local_files_only=True
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)
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except Exception as e:
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logger.error(
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f"failed to load model: {e} \n\n"
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f"加载模型失败: {e} \n\n"
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f"********************************************\n"
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f"this may be caused by network issue. \n"
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f"please download the model manually and put it in the 'models' folder. \n"
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f"see [README.md FAQ](https://github.com/harry0703/NarratoAI) for more details.\n"
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f"这可能是由网络问题引起的. \n"
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f"请手动下载模型并将其放入 'app/models' 文件夹中。 \n"
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f"see [README.md FAQ](https://github.com/linyqh/NarratoAI) for more details.\n"
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f"********************************************\n\n"
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f"{traceback.format_exc()}"
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)
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return None
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@ -56,7 +75,7 @@ def create(audio_file, subtitle_file: str = ""):
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)
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logger.info(
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f"detected language: '{info.language}', probability: {info.language_probability:.2f}"
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f"检测到的语言: '{info.language}', probability: {info.language_probability:.2f}"
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)
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start = timer()
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@ -139,6 +158,15 @@ def create(audio_file, subtitle_file: str = ""):
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def file_to_subtitles(filename):
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"""
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将字幕文件转换为字幕列表。
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参数:
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filename (str): 字幕文件的路径。
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返回:
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list: 包含字幕序号、出现时间、和字幕文本的元组列表。
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"""
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if not filename or not os.path.isfile(filename):
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return []
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@ -313,28 +341,28 @@ def create_with_gemini(audio_file: str, subtitle_file: str = "", api_key: Option
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if __name__ == "__main__":
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task_id = "task456"
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task_id = "test456"
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task_dir = utils.task_dir(task_id)
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subtitle_file = f"{task_dir}/subtitle.srt"
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audio_file = f"{task_dir}/audio.mp3"
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audio_file = f"{task_dir}/audio.wav"
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subtitles = file_to_subtitles(subtitle_file)
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print(subtitles)
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script_file = f"{task_dir}/script.json"
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with open(script_file, "r") as f:
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script_content = f.read()
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s = json.loads(script_content)
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script = s.get("script")
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# script_file = f"{task_dir}/script.json"
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# with open(script_file, "r") as f:
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# script_content = f.read()
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# s = json.loads(script_content)
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# script = s.get("script")
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#
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# correct(subtitle_file, script)
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correct(subtitle_file, script)
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subtitle_file = f"{task_dir}/subtitle-test.srt"
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subtitle_file = f"{task_dir}/subtitle111.srt"
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create(audio_file, subtitle_file)
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# 使用Gemini模型处理音频
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gemini_api_key = config.app.get("gemini_api_key") # 请替换为实际的API密钥
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gemini_subtitle_file = create_with_gemini(audio_file, api_key=gemini_api_key)
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if gemini_subtitle_file:
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print(f"Gemini生成的字幕文件: {gemini_subtitle_file}")
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# # 使用Gemini模型处理音频
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# gemini_api_key = config.app.get("gemini_api_key") # 请替换为实际的API密钥
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# gemini_subtitle_file = create_with_gemini(audio_file, api_key=gemini_api_key)
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#
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# if gemini_subtitle_file:
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# print(f"Gemini生成的字幕文件: {gemini_subtitle_file}")
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@ -383,27 +383,11 @@ def start_subclip(task_id: str, params: VideoClipParams, subclip_path_videos):
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subtitle_path = ""
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if params.subtitle_enabled:
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subtitle_path = path.join(utils.task_dir(task_id), f"subtitle111.srt")
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subtitle_path = path.join(utils.task_dir(task_id), f"subtitle.srt")
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subtitle_provider = config.app.get("subtitle_provider", "").strip().lower()
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logger.info(f"\n\n## 3. 生成字幕、提供程序是: {subtitle_provider}")
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subtitle_fallback = False
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if subtitle_provider == "edge":
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voice.create_subtitle(text=video_script, sub_maker="sub_maker", subtitle_file=subtitle_path)
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# voice.create_subtitle(
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# text=video_script,
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# sub_maker_list=sub_maker_list,
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# list_script=list_script,
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# subtitle_file=subtitle_path
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# )
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# if not os.path.exists(subtitle_path):
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# subtitle_fallback = True
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# logger.warning("找不到字幕文件，回退到whisper")
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#
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# if subtitle_provider == "whisper" or subtitle_fallback:
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# # subtitle.create(audio_file=audio_file, subtitle_file=subtitle_path)
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# subtitle.create_with_gemini(audio_file=audio_file, subtitle_file=subtitle_path, api_key=config.app.get("gemini_api_key", ""))
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# logger.info("\n\n## 更正字幕")
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# subtitle.correct(subtitle_file=subtitle_path, video_script=video_script)
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# 使用 faster-whisper-large-v2 模型生成字幕
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subtitle.create(audio_file=audio_file, subtitle_file=subtitle_path)
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subtitle_lines = subtitle.file_to_subtitles(subtitle_path)
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if not subtitle_lines:
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@ -73,9 +73,10 @@
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deepseek_base_url = "https://api.deepseek.com"
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deepseek_model_name = "deepseek-chat"
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# Subtitle Provider, "edge" or "whisper"
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# Subtitle Provider, "whisper"
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# If empty, the subtitle will not be generated
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subtitle_provider = "edge"
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subtitle_provider = "faster-whisper-large-v2"
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subtitle_enabled = true
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#
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# ImageMagick
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@ -159,7 +160,7 @@
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# model = WhisperModel(model_size, device="cpu", compute_type="int8")
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# recommended model_size: "large-v3"
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model_size="large-v3"
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model_size="faster-whisper-large-v2"
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# if you want to use GPU, set device="cuda"
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device="CPU"
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compute_type="int8"
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