mirror of
https://github.com/HiMeditator/auto-caption.git
synced 2026-02-04 04:14:42 +08:00
cd9f3a847d
- 重构了 Gummy 和 Vosk 字幕引擎的代码，提高了可扩展性和可读性 - 合并 Gummy 和 Vosk 引擎为单个可执行文件 - 实现了字幕引擎和主程序之间的 WebSocket 通信，避免了孤儿进程问题
100 lines
3.1 KiB
Python
100 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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from utils import stdout_cmd, stdout_obj
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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.index = 0
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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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self.usage = 0
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self.cur_id = -1
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self.time_str = ''
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stdout_cmd('info', 'Gummy translator started.')
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def on_close(self) -> None:
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stdout_cmd('info', 'Gummy translator closed.')
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stdout_cmd('usage', str(self.usage))
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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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if self.cur_id != transcription_result.sentence_id:
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self.time_str = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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self.cur_id = transcription_result.sentence_id
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self.index += 1
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caption['command'] = 'caption'
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caption['index'] = self.index
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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['text'] = transcription_result.text
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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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if 'text' in caption:
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stdout_obj(caption)
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class GummyRecognizer:
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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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api_key: 阿里云百炼平台 API KEY
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"""
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def __init__(self, rate: int, source: str, target: str | None, api_key: str | None):
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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，擎将自动识别并将识别结果输出到标准输出中"""
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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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