63fcfb7ba2
Step 3 (귀): add WhisperSTT + whisper_worker, a warm out-of-venv worker mirroring the MeloTTS shape (whisper312 venv, small/int8 on CPU). transcribe() closes the voice round trip (MeloTTS wav -> whisper text); utterances() turns an injected audio_source into Utterances (Discord voice feed pending). Wired into factory as WSAI_STT=whisper. Also address the arbiter's TTS follow-ups: - melo worker error handling: capture stderr (drained in a bounded background task so the pipe can't fill), surface the real failure cause, and defend against an empty/invalid ready line instead of dying on JSONDecodeError. - pipeline pre-warm: load slow backends (warmup()) at startup so the first utterance is answered warm; a warmup failure is logged, not fatal. Verified: real TTS->STT round trip recovers the sentence near-perfectly; warm transcribe ~1.2s (CPU). 12 tests pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
"""Persistent faster-whisper STT worker.
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faster-whisper (ctranslate2) lives in its own Python (whisper312); loading the
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model takes seconds, so we load it ONCE here and then serve transcription
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requests over stdin/stdout. This process is launched with the whisper312
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interpreter by wsai.backends.whisper.WhisperSTT.
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Like the MeloTTS worker, model/backend chatter could corrupt the JSON protocol,
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so on startup we split the streams: a private duplicate of the original stdout
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carries the protocol, and fd 1 is redirected to fd 2 so any library print lands
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on stderr instead (where the parent drains it for diagnostics).
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Protocol (one JSON object per line, on the protocol channel):
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<- {"wav": "/abs/path.wav", "language": "ko"}
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-> {"ok": true, "text": "...", "language": "ko", "ms": 123}
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-> {"ok": false, "error": "..."}
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On startup, once the model is ready, it emits exactly one line:
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-> {"ready": true, "ms": <load-ms>, "device": "cpu", "model": "small"}
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"""
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import json
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import os
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import sys
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import time
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# Split protocol from library noise BEFORE importing anything heavy.
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_proto = os.fdopen(os.dup(1), "w", buffering=1) # private copy of real stdout
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os.dup2(2, 1) # fd1 -> stderr, so stray library prints don't hit the protocol
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def _emit(obj: dict) -> None:
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_proto.write(json.dumps(obj, ensure_ascii=False) + "\n")
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_proto.flush()
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def _log(*a):
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print(*a, file=sys.stderr, flush=True)
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def main() -> None:
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model_name = os.environ.get("WSAI_WHISPER_MODEL", "small")
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device = os.environ.get("WSAI_WHISPER_DEVICE", "cpu") # "cpu" | "cuda"
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# int8 on CPU keeps a small model fast; float16 is the usual CUDA choice.
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compute = os.environ.get(
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"WSAI_WHISPER_COMPUTE", "int8" if device == "cpu" else "float16"
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)
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default_lang = os.environ.get("WSAI_WHISPER_LANGUAGE", "ko") or None
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t0 = time.monotonic()
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from faster_whisper import WhisperModel # heavy import; only in whisper venv
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model = WhisperModel(model_name, device=device, compute_type=compute)
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load_ms = int((time.monotonic() - t0) * 1000)
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_emit({"ready": True, "ms": load_ms, "device": device, "model": model_name})
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_log(f"[whisper_worker] {model_name} ready in {load_ms} ms on {device}/{compute}")
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for line in sys.stdin:
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line = line.strip()
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if not line:
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continue
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try:
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req = json.loads(line)
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wav = req["wav"]
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language = req.get("language", default_lang)
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s = time.monotonic()
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segments, info = model.transcribe(
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wav,
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language=language,
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beam_size=int(req.get("beam_size", 5)),
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vad_filter=bool(req.get("vad_filter", True)),
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)
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text = "".join(seg.text for seg in segments).strip()
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ms = int((time.monotonic() - s) * 1000)
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_emit({"ok": True, "text": text, "language": info.language, "ms": ms})
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except Exception as exc: # keep the worker alive across bad requests
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_emit({"ok": False, "error": f"{type(exc).__name__}: {exc}"})
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_log(f"[whisper_worker] error: {exc}")
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if __name__ == "__main__":
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main()
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