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import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
from faster_whisper import WhisperModel
from deep_translator import GoogleTranslator
def transribe_inference(model="large-v3-turbo",compute_type="float16",input_audio="",language="Auto"):
model_size = model
# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type=compute_type)
ref_audio = input_audio
segments, info = model.transcribe(
ref_audio,
language=None if language == "Auto" else language,
beam_size=5,
task="transcribe",
condition_on_previous_text=False,
without_timestamps=True,
chunk_length=40,
vad_filter=True
)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
# Concatenate all segment texts into one line
output_text = " ".join([segment.text.strip() for segment in segments])
print("Transcribe Text :",output_text)
return output_text
def translate_inference(text=str,target="th"):
translated = GoogleTranslator(source='auto', target=target).translate(text=text)
print("Translated Text:",translated)
return translated
#input_ref_audio = "ref/ref_gen_7_jp_male,คิโซนโนเอบุไซโตะโอโคเอะตะ โคมุนิเคชั่นโอจิตสึเกน..wav"
#tranlsate = False
#if tranlsate:
# translate_inference(text=transribe_inference(input_audio=input_ref_audio))
#else:
# transribe_inference(input_audio=input_ref_audio)
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