import logging import numpy as np from faster_whisper import WhisperModel from translator import LANGUAGE_DISPLAY log = logging.getLogger("LiveTrans.ASR") LANGUAGE_NAMES = {**LANGUAGE_DISPLAY, "auto": "auto"} class ASREngine: """Speech-to-text using faster-whisper.""" def __init__( self, model_size="medium", device="cuda", device_index=0, compute_type="float16", language="auto", download_root=None, ): self.language = language if language != "auto" else None self._model = WhisperModel( model_size, device=device, device_index=device_index, compute_type=compute_type, download_root=download_root, ) log.info(f"Model loaded: {model_size} on {device} ({compute_type})") def set_language(self, language: str): old = self.language self.language = language if language != "auto" else None log.info(f"ASR language: {old} -> {self.language}") def to_device(self, device: str): # ctranslate2 doesn't support device migration; must reload return False def unload(self): if self._model is not None: try: self._model.model.unload_model() except Exception: pass self._model = None def transcribe(self, audio: np.ndarray) -> dict | None: """Transcribe audio segment. Args: audio: float32 numpy array, 16kHz mono Returns: dict with 'text', 'language', 'language_name' or None if no speech detected. """ segments, info = self._model.transcribe( audio, language=self.language, beam_size=5, vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500), ) text_parts = [] for seg in segments: text_parts.append(seg.text.strip()) full_text = " ".join(text_parts).strip() if not full_text: return None detected_lang = info.language return { "text": full_text, "language": detected_lang, "language_name": LANGUAGE_NAMES.get(detected_lang, detected_lang), }