mirror of
https://github.com/HiMeditator/auto-caption.git
synced 2026-02-04 04:14:42 +08:00
eba2c5ca45
- 重构字幕引擎，将音频采集改为在新线程上进行 - 重构 audio2text 中的类，调整运行逻辑 - 更新 main 函数，添加对 Sosv 模型的支持 - 修改 AudioStream 类，默认使用 16000Hz 采样率
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
import json
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import threading
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import time
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from datetime import datetime
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from vosk import Model, KaldiRecognizer, SetLogLevel
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from utils import shared_data
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from utils import stdout_cmd, stdout_obj, google_translate, ollama_translate
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class VoskRecognizer:
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"""
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使用 Vosk 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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model_path: Vosk 识别模型路径
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target: 翻译目标语言
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trans_model: 翻译模型名称
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ollama_name: Ollama 模型名称
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"""
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def __init__(self, model_path: str, target: str | None, trans_model: str, ollama_name: str):
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SetLogLevel(-1)
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if model_path.startswith('"'):
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model_path = model_path[1:]
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if model_path.endswith('"'):
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model_path = model_path[:-1]
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self.model_path = model_path
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self.target = target
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if trans_model == 'google':
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self.trans_func = google_translate
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else:
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self.trans_func = ollama_translate
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self.ollama_name = ollama_name
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self.time_str = ''
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self.cur_id = 0
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self.prev_content = ''
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self.model = Model(self.model_path)
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self.recognizer = KaldiRecognizer(self.model, 16000)
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def start(self):
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"""启动 Vosk 引擎"""
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stdout_cmd('info', 'Vosk recognizer started.')
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def send_audio_frame(self, data: bytes):
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"""
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发送音频帧给 Vosk 引擎，引擎将自动识别并将识别结果输出到标准输出中
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Args:
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data: 音频帧数据，采样率必须为 16000Hz
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"""
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caption = {}
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caption['command'] = 'caption'
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caption['translation'] = ''
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if self.recognizer.AcceptWaveform(data):
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content = json.loads(self.recognizer.Result()).get('text', '')
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caption['index'] = self.cur_id
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caption['text'] = content
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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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self.prev_content = ''
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if content == '': return
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self.cur_id += 1
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if self.target:
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self.trans_time = time.time()
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th = threading.Thread(
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target=self.trans_func,
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args=(self.ollama_name, self.target, caption['text'], self.time_str)
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)
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th.start()
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else:
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content = json.loads(self.recognizer.PartialResult()).get('partial', '')
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if content == '' or content == self.prev_content:
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return
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if self.prev_content == '':
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self.time_str = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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caption['index'] = self.cur_id
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caption['text'] = content
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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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self.prev_content = content
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stdout_obj(caption)
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def translate(self):
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"""持续读取共享数据中的音频帧，并进行语音识别，将识别结果输出到标准输出中"""
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global shared_data
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while shared_data.status == 'running':
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chunk = shared_data.chunk_queue.get()
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self.send_audio_frame(chunk)
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def stop(self):
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"""停止 Vosk 引擎"""
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stdout_cmd('info', 'Vosk recognizer closed.') |