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import numpy as np
from typing import Dict
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from pyctcdecode import Alphabet, BeamSearchDecoderCTC
class PreTrainedModel():
def __init__(self, path):
"""
Initialize model
"""
processor = Wav2Vec2Processor.from_pretrained(path)
model = Wav2Vec2ForCTC.from_pretrained(path)
vocab_list = list(processor.tokenizer.get_vocab().keys())
# convert ctc blank character representation
vocab_list[0] = ""
# replace special characters
vocab_list[1] = "⁇"
vocab_list[2] = "⁇"
vocab_list[3] = "⁇"
# convert space character representation
vocab_list[4] = " "
alphabet = Alphabet.build_alphabet(vocab_list, ctc_token_idx=0)
self.decoder = BeamSearchDecoderCTC(alphabet)
def __call__(self, inputs)-> Dict[str, str]:
"""
Args:
inputs (:obj:`np.array`):
The raw waveform of audio received. By default at 16KHz.
Return:
A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing
the detected text from the input audio.
"""
return {
"text": self.decoder.decode(logits)
}
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