36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import base64
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import edge_tts
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from faster_whisper import WhisperModel
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from . import config
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class SpeechService:
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def __init__(self) -> None:
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print("⏳ 正在加载本地语音识别模型 (首次启动可能需要下载)...")
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self._whisper_model = WhisperModel(
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config.WHISPER_MODEL_NAME,
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device=config.WHISPER_DEVICE,
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compute_type=config.WHISPER_COMPUTE_TYPE,
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)
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print("✅ 本地语音模型加载完毕！")
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def transcribe(self, audio_path: str) -> str:
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segments, _ = self._whisper_model.transcribe(
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audio_path,
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beam_size=config.WHISPER_BEAM_SIZE,
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language=config.WHISPER_LANGUAGE,
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)
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return "".join(segment.text for segment in segments)
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async def synthesize_audio_data_url(self, text: str) -> str:
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communicate = edge_tts.Communicate(text, config.TTS_VOICE)
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audio_data = b""
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async for chunk in communicate.stream():
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if chunk["type"] == "audio":
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audio_data += chunk["data"]
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audio_b64 = base64.b64encode(audio_data).decode("utf-8")
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return f"data:audio/mp3;base64,{audio_b64}"
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