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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import whisper
tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker")
model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-base-spellchecker")
def correct(inputs):
input_ids = tokenizer.encode(inputs,return_tensors='pt')
sample_output = model.generate(
input_ids,
do_sample=True,
max_length=50,
top_p=0.99,
num_return_sequences=1
)
res = tokenizer.decode(sample_output[0], skip_special_tokens=True)
return res
whisper_model = whisper.load_model("base")
def transcribe(audio_file):
# Load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio_file)
audio = whisper.pad_or_trim(audio)
# Convert audio data to PyTorch tensor and float data type
mel = torch.from_numpy(audio).float()
# Make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(mel).to(model.device)
# Detect the spoken language
_, probs = whisper_model.detect_language(mel)
# Decode the audio
options = whisper.DecodingOptions(fp16=False)
result = whisper.decode(whisper_model, mel, options)
result_text = result.text
print('result_text:'+result_text)
return correct(result_text)
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