| 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): |
| |
| 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), |
| } |
|
|
