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import numpy as np
from typing import Dict
import torch
import pyctcdecode
from transformers import (
Wav2Vec2Processor,
Wav2Vec2ProcessorWithLM,
Wav2Vec2ForCTC,
)
class PreTrainedPipeline():
def __init__(self, model_path: str, language_model_fp: str):
self.language_model_fp = language_model_fp
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = Wav2Vec2ForCTC.from_pretrained(model_path)
self.model.to(self.device)
processor = Wav2Vec2Processor.from_pretrained(model_path)
self.sampling_rate = processor.feature_extractor.sampling_rate
vocab = processor.tokenizer.get_vocab()
sorted_vocab_dict = [(char, ix) for char, ix in sorted(vocab.items(), key=lambda item: item[1])]
self.decoder = pyctcdecode.build_ctcdecoder(
labels=[x[0] for x in sorted_vocab_dict],
kenlm_model_path=self.language_model_fp,
)
self.processor_with_lm = Wav2Vec2ProcessorWithLM(
feature_extractor=processor.feature_extractor,
tokenizer=processor.tokenizer,
decoder=self.decoder
)
def __call__(self, inputs: np.array) -> 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.
"""
input_values = self.processor_with_lm(
inputs, return_tensors="pt",
sampling_rate=self.sampling_rate
)['input_values']
input_values = input_values.to(self.device)
with torch.no_grad():
# input_values should be a 2D tensor by now. 1st dim represents audio channels.
model_outs = self.model(input_values)
logits = model_outs.logits.cpu().detach().numpy()
text_predicted = self.processor_with_lm.batch_decode(logits)['text']
return {
"text": text_predicted
}
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