| import whisper | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| from googletrans import Translator | |
| import torch | |
| def load_models(): | |
| lang_detector = whisper.load_model("small") | |
| tamil_processor = WhisperProcessor.from_pretrained("Lingalingeswaran/whisper-small-ta") | |
| tamil_model = WhisperForConditionalGeneration.from_pretrained("Lingalingeswaran/whisper-small-ta") | |
| sinhala_processor = WhisperProcessor.from_pretrained("Lingalingeswaran/whisper-small-sinhala") | |
| sinhala_model = WhisperForConditionalGeneration.from_pretrained("Lingalingeswaran/whisper-small-sinhala") | |
| english_model = whisper.load_model("small") | |
| return lang_detector, tamil_processor, tamil_model, sinhala_processor, sinhala_model, english_model | |
| def detect_language(audio_file, lang_detector): | |
| audio = whisper.load_audio(audio_file) | |
| audio = whisper.pad_or_trim(audio) | |
| mel = whisper.log_mel_spectrogram(audio).to(lang_detector.device) | |
| _, probs = lang_detector.detect_language(mel) | |
| return max(probs, key=probs.get) | |
| def transcribe_audio(audio_file, detected_lang, tamil_processor, tamil_model, sinhala_processor, sinhala_model, english_model): | |
| if detected_lang == "ta": | |
| processor, model = tamil_processor, tamil_model | |
| elif detected_lang == "si": | |
| processor, model = sinhala_processor, sinhala_model | |
| else: | |
| model = english_model | |
| return model.transcribe(audio_file)["text"] | |
| audio = whisper.load_audio(audio_file) | |
| inputs = processor(audio, return_tensors="pt", sampling_rate=16000) | |
| with torch.no_grad(): | |
| predicted_ids = model.generate(**inputs) | |
| return processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] | |
| def translate_to_english(text): | |
| return Translator().translate(text, dest="en").text | |
| def full_pipeline(audio_file): | |
| lang_detector, tamil_processor, tamil_model, sinhala_processor, sinhala_model, english_model = load_models() | |
| detected_lang = detect_language(audio_file, lang_detector) | |
| transcription = transcribe_audio(audio_file, detected_lang, tamil_processor, tamil_model, sinhala_processor, sinhala_model, english_model) | |
| return translate_to_english(transcription) | |