handle.py

from typing import  Dict
import librosa
import numpy as np
import torch
import pyewts
import noisereduce as nr
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from num2tib.core import convert
from num2tib.core import convert2text
import re
converter = pyewts.pyewts()
def replace_numbers_with_convert(sentence, wylie=True):
    pattern = r'\d+(\.\d+)?'
    def replace(match):
        return convert(match.group(), wylie)
    result = re.sub(pattern, replace, sentence)
    
    return result

def cleanup_text(inputs):
    for src, dst in replacements:
        inputs = inputs.replace(src, dst)
    return inputs

speaker_embeddings = {
    "Lhasa(female)": "female_2.npy",

}

replacements = [
    ('_', '_'),
    ('*', 'v'),
    ('`', ';'),
    ('~', ','),
    ('+', ','),
    ('\\', ';'),
    ('|', ';'),
    ('╚',''),
    ('╗','')
]





class EndpointHandler():
    def __init__(self, path=""):
        # load the model
        self.processor = SpeechT5Processor.from_pretrained("TenzinGayche/TTS_run3_ep20_174k_b")
        self.model = SpeechT5ForTextToSpeech.from_pretrained("TenzinGayche/TTS_run3_ep20_174k_b")
        self.model.to('cuda')
        self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")


    def __call__(self, data: Dict[str]) -> Dict[str, str]:
        """
        Args:
            data (:obj:):
                includes the deserialized audio file as bytes
        Return:
            A :obj:`dict`:. base64 encoded image
        """
        # process input

        if len(text.strip()) == 0:
            return (16000, np.zeros(0).astype(np.int16))
        text = converter.toWylie(text)
        text=cleanup_text(text)
        text=replace_numbers_with_convert(text)
        inputs = self.processor(text=text, return_tensors="pt")
        # limit input length
        input_ids = inputs["input_ids"]
        input_ids = input_ids[..., :self.model.config.max_text_positions]
        speaker_embedding = np.load(speaker_embeddings['Lhasa(female)'])
        speaker_embedding = torch.tensor(speaker_embedding)
        speech = self.model.generate_speech(input_ids.to('cuda'), speaker_embedding.to('cuda'), vocoder=vocoder.to('cuda'))
        speech = nr.reduce_noise(y=speech.to('cpu'), sr=16000)
        return (16000, speech)