mirror of
https://github.com/HiMeditator/auto-caption.git
synced 2026-02-04 04:14:42 +08:00
8e575a9ba3
- 字幕记录表格可以按时间降序排列 - 将 caption-engine 重命名为 engine - 更新了相关文件和文件夹的路径 - 修改了 README 和 TODO 文档中的相关内容 - 更新了 Electron 构建配置
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
from dashscope.audio.asr import (
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TranslationRecognizerCallback,
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TranscriptionResult,
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TranslationResult,
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TranslationRecognizerRealtime
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)
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import dashscope
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from datetime import datetime
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import json
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import sys
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class Callback(TranslationRecognizerCallback):
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"""
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语音大模型流式传输回调对象
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"""
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def __init__(self):
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super().__init__()
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self.usage = 0
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self.cur_id = -1
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self.time_str = ''
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def on_open(self) -> None:
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# print("on_open")
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pass
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def on_close(self) -> None:
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# print("on_close")
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pass
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def on_event(
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self,
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request_id,
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transcription_result: TranscriptionResult,
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translation_result: TranslationResult,
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usage
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) -> None:
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caption = {}
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if transcription_result is not None:
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caption['index'] = transcription_result.sentence_id
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caption['text'] = transcription_result.text
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if caption['index'] != self.cur_id:
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self.cur_id = caption['index']
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cur_time = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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caption['time_s'] = cur_time
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self.time_str = cur_time
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else:
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caption['time_s'] = self.time_str
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caption['time_t'] = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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caption['translation'] = ""
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if translation_result is not None:
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lang = translation_result.get_language_list()[0]
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caption['translation'] = translation_result.get_translation(lang).text
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if usage:
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self.usage += usage['duration']
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# print(caption)
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self.send_to_node(caption)
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def send_to_node(self, data):
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"""
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将数据发送到 Node.js 进程
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"""
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try:
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json_data = json.dumps(data) + '\n'
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sys.stdout.write(json_data)
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sys.stdout.flush()
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except Exception as e:
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print(f"Error sending data to Node.js: {e}", file=sys.stderr)
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class GummyTranslator:
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"""
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使用 Gummy 引擎流式处理的音频数据，并在标准输出中输出与 Auto Caption 软件可读取的 JSON 字符串数据
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初始化参数：
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rate: 音频采样率
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source: 源语言代码字符串（zh, en, ja 等）
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target: 目标语言代码字符串（zh, en, ja 等）
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"""
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def __init__(self, rate, source, target, api_key):
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if api_key:
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dashscope.api_key = api_key
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self.translator = TranslationRecognizerRealtime(
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model = "gummy-realtime-v1",
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format = "pcm",
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sample_rate = rate,
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transcription_enabled = True,
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translation_enabled = (target is not None),
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source_language = source,
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translation_target_languages = [target],
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callback = Callback()
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)
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def start(self):
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"""启动 Gummy 引擎"""
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self.translator.start()
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def send_audio_frame(self, data):
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"""发送音频帧"""
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self.translator.send_audio_frame(data)
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def stop(self):
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"""停止 Gummy 引擎"""
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self.translator.stop()
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