File size: 2,301 Bytes
bd95217 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | 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),
}
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