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import sys
import torch
from transformers import (
AutoModelForMaskedLM,
AutoTokenizer,
DebertaV2Model,
DebertaV2Tokenizer,
ClapModel,
ClapProcessor,
)
from config import config
from text.japanese import text2sep_kata
class BertFeature:
def __init__(self, model_path, language="ZH"):
self.model_path = model_path
self.language = language
self.tokenizer = None
self.model = None
self.device = None
self._prepare()
def _get_device(self, device=config.bert_gen_config.device):
if (
sys.platform == "darwin"
and torch.backends.mps.is_available()
and device == "cpu"
):
device = "mps"
if not device:
device = "cuda"
return device
def _prepare(self):
self.device = self._get_device()
if self.language == "EN":
self.tokenizer = DebertaV2Tokenizer.from_pretrained(self.model_path)
self.model = DebertaV2Model.from_pretrained(self.model_path).to(self.device)
else:
self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
self.model = AutoModelForMaskedLM.from_pretrained(self.model_path).to(
self.device
)
self.model.eval()
def get_bert_feature(self, text, word2ph):
if self.language == "JP":
text = "".join(text2sep_kata(text)[0])
with torch.no_grad():
inputs = self.tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(self.device)
res = self.model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
word2phone = word2ph
phone_level_feature = []
for i in range(len(word2phone)):
repeat_feature = res[i].repeat(word2phone[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
class ClapFeature:
def __init__(self, model_path):
self.model_path = model_path
self.processor = None
self.model = None
self.device = None
self._prepare()
def _get_device(self, device=config.bert_gen_config.device):
if (
sys.platform == "darwin"
and torch.backends.mps.is_available()
and device == "cpu"
):
device = "mps"
if not device:
device = "cuda"
return device
def _prepare(self):
self.device = self._get_device()
self.processor = ClapProcessor.from_pretrained(self.model_path)
self.model = ClapModel.from_pretrained(self.model_path).to(self.device)
self.model.eval()
def get_clap_audio_feature(self, audio_data):
with torch.no_grad():
inputs = self.processor(
audios=audio_data, return_tensors="pt", sampling_rate=48000
).to(self.device)
emb = self.model.get_audio_features(**inputs)
return emb.T
def get_clap_text_feature(self, text):
with torch.no_grad():
inputs = self.processor(text=text, return_tensors="pt").to(self.device)
emb = self.model.get_text_features(**inputs)
return emb.T
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