| import librosa | |
| import torch | |
| from .init import processor, model | |
| LIMIT = 90 # limit 90 seconds | |
| class A2T: | |
| def __init__(self, mic): | |
| self.mic = mic | |
| def __preproccess(self, audio, frame_rate): | |
| try: | |
| audio = audio / 32678.0 | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.T) | |
| if frame_rate != 16_000: | |
| audio = librosa.resample(audio, orig_sr=frame_rate, target_sr=16000) | |
| audio = audio[:16_000*LIMIT] | |
| audio = torch.tensor(audio) | |
| return audio | |
| except Exception as e: | |
| print("Error", e) | |
| return None | |
| def predict(self): | |
| if this.mic is not None: | |
| audio = self.mic | |
| frame_rate = audio.frame_rate | |
| else: | |
| return "please provide audio" | |
| try: | |
| forced_decoder_ids = processor.get_decoder_prompt_ids(language="english", task="transcribe") | |
| audio = self.__preproccess(audio=audio, frame_rate=frame_rate) | |
| inputs = processor(audio=audio, sampling_rate=16000, return_tensors="pt") | |
| predicted_ids = model.generate(**inputs, max_length=400, forced_decoder_ids=forced_decoder_ids) | |
| transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| return transcription[0] | |
| except Exception as e: | |
| print("Error", e) | |
| return "Oops some kinda error" | |