File size: 2,322 Bytes
4629510 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
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'),
('`', ';'),
('~', ','),
('+', ','),
('\\', ';'),
('|', ';'),
('β',''),
('β','')
]
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)
|