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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"
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