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TranscriberModels.py
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TranscriberModels.py
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import openai
import whisper
import os
import torch
def get_model(use_api):
if use_api:
return APIWhisperTranscriber()
else:
return WhisperTranscriber()
class WhisperTranscriber:
def __init__(self):
self.audio_model = whisper.load_model(os.path.join(os.getcwd(), 'tiny.en.pt'))
print(f"[INFO] Whisper using GPU: " + str(torch.cuda.is_available()))
def get_transcription(self, wav_file_path):
try:
result = self.audio_model.transcribe(wav_file_path, fp16=torch.cuda.is_available())
except Exception as e:
print(e)
return ''
return result['text'].strip()
class APIWhisperTranscriber:
def get_transcription(self, wav_file_path):
try:
with open(wav_file_path, "rb") as audio_file:
result = openai.Audio.transcribe("whisper-1", audio_file)
except Exception as e:
print(e)
return ''
return result['text'].strip()