112 lines
4.8 KiB
Python
112 lines
4.8 KiB
Python
"""④ tts — 나레이션 음성 + 실측 타임라인.
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**오디오가 타이밍을 결정한다.** 영상 비트를 먼저 못 박고 음성을 우겨넣으면 말이 잘리거나 빈다.
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문장별로 만들어 실제 길이를 재고, 그 큐에 씬·카메라를 맞춘다. 순서를 뒤집지 않는다.
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파일도 서브프로세스도 없다. TTS 응답을 받아 메모리에서 트림·합성·인코딩한다.
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"""
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import asyncio
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import httpx
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import numpy as np
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from openai import AsyncOpenAI
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from models.tts import NarrationAudio, NarrationCue, NarrationTimeline, TypecastVoice
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from settings import settings
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from utils.audio import SAMPLE_RATE, decode_mp3, encode_mp3, trim_silence
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from utils.prompt import load_prompt
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TYPECAST_API = "https://api.typecast.ai/v1/text-to-speech"
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OPENAI_TTS_MODEL = "gpt-4o-mini-tts"
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TYPECAST_MODEL = "ssfm-v30"
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# "천천히"라고 지시하면 TTS가 과하게 늘어진다. 톤은 지시문으로, 속도는 파라미터로 통제한다.
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TTS_INSTRUCTIONS = load_prompt("tts_instructions")
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DEFAULT_SPEED = 1.28
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DEFAULT_PAUSES = (0.35, 0.35, 0.30)
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DEFAULT_LEAD_IN = 0.35
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DEFAULT_TAIL = 1.7
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async def speak_openai(text: str, voice: str, speed: float, instructions: str) -> bytes:
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response = await AsyncOpenAI(api_key=settings.chatgpt_api_key).audio.speech.create(
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model=OPENAI_TTS_MODEL,
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voice=voice,
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input=text,
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instructions=instructions,
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speed=speed,
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response_format="mp3",
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)
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return await response.aread()
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async def speak_typecast(text: str, typecast: TypecastVoice) -> bytes:
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if not settings.typecast_api_key:
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raise RuntimeError("TYPECAST_API_KEY 없음 — typecast 엔진을 쓰려면 필요하다")
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async with httpx.AsyncClient(timeout=90) as client:
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response = await client.post(
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TYPECAST_API,
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headers={"X-API-KEY": settings.typecast_api_key},
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json={
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"text": text,
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"model": TYPECAST_MODEL,
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"voice_id": typecast.voice_id,
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"language": "kor",
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"prompt": {"emotion_type": "preset",
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"emotion_preset": typecast.emotion,
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"emotion_intensity": typecast.emotion_intensity},
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"output": {"audio_format": "mp3", "volume": 100,
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"audio_tempo": typecast.tempo},
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})
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response.raise_for_status()
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return response.content
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def merge_clips(clips: list[np.ndarray], starts: list[float], total: float) -> np.ndarray:
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"""큐 시작 시각에 얹어 한 트랙으로 만든다. 큐는 겹치지 않으므로 덮어쓰면 된다."""
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track = np.zeros(round(total * SAMPLE_RATE), dtype=np.int16)
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for clip, start in zip(clips, starts, strict=True):
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offset = round(start * SAMPLE_RATE)
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track[offset:offset + len(clip)] = clip[:max(0, len(track) - offset)]
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return track
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async def synthesize(sentences: list[str], voice: str = "nova", *,
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speed: float = DEFAULT_SPEED,
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instructions: str = TTS_INSTRUCTIONS,
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typecast: TypecastVoice | None = None,
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pauses: tuple[float, ...] = DEFAULT_PAUSES,
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lead_in: float = DEFAULT_LEAD_IN,
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tail: float = DEFAULT_TAIL) -> NarrationAudio:
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"""typecast를 주면 그 엔진으로 간다 — voice_id 없이 typecast를 고를 수 없다."""
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if not sentences:
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raise ValueError("나레이션 문장이 없다")
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if typecast:
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spoken = await asyncio.gather(*(speak_typecast(text, typecast) for text in sentences))
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else:
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spoken = await asyncio.gather(
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*(speak_openai(text, voice, speed, instructions) for text in sentences))
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# TTS가 문장 끝에 붙이는 최대 1.2초 공백이 그대로 영상 길이가 되면 훅 직후에 침묵이 생긴다
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clips = [trim_silence(decode_mp3(mp3)) for mp3 in spoken]
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cues, starts = [], []
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cursor = lead_in
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for index, (text, clip) in enumerate(zip(sentences, clips, strict=True)):
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duration = len(clip) / SAMPLE_RATE
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pause = pauses[index] if index < len(pauses) else pauses[-1]
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starts.append(cursor)
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cues.append(NarrationCue(index=index, text=text, start=round(cursor, 3),
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duration=round(duration, 3), end=round(cursor + duration, 3),
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pause_after=pause))
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cursor += duration + pause
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total = round(cues[-1].end + tail, 2)
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return NarrationAudio(
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timeline=NarrationTimeline(
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engine="typecast" if typecast else "openai",
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voice=typecast.voice_id if typecast else voice,
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lead_in=lead_in, tail=tail, total=total, cues=cues),
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mp3=encode_mp3(merge_clips(clips, starts, total)),
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)
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