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import torch
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
from transformers import (
Wav2Vec2FeatureExtractor,
HubertModel,
HubertConfig,
AutoConfig
)
import torch.nn as nn
class CNHubert(nn.Module):
def __init__(self, hubert_config_dict: dict[str, any], extractor_config_dict: dict[str, any]):
super().__init__()
self.model = HubertModel(HubertConfig.from_dict(hubert_config_dict))
self.feature_extractor = Wav2Vec2FeatureExtractor.from_dict(extractor_config_dict)
def forward(self, x):
input_values = self.feature_extractor(
x, return_tensors="pt", sampling_rate=16000
).input_values.to(x.device)
feats = self.model(input_values)["last_hidden_state"]
return feats
# class CNHubertLarge(nn.Module):
# def __init__(self):
# super().__init__()
# self.model = HubertModel.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large")
# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-hubert-large")
# def forward(self, x):
# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
# feats = self.model(input_values)["last_hidden_state"]
# return feats
#
# class CVec(nn.Module):
# def __init__(self):
# super().__init__()
# self.model = HubertModel.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base")
# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/vc-webui-big/hubert_base")
# def forward(self, x):
# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
# feats = self.model(input_values)["last_hidden_state"]
# return feats
#
# class cnw2v2base(nn.Module):
# def __init__(self):
# super().__init__()
# self.model = Wav2Vec2Model.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base")
# self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("/data/docker/liujing04/gpt-vits/chinese-wav2vec2-base")
# def forward(self, x):
# input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
# feats = self.model(input_values)["last_hidden_state"]
# return feats
# def get_large_model():
# model = CNHubertLarge()
# model.eval()
# return model
#
# def get_model_cvec():
# model = CVec()
# model.eval()
# return model
#
# def get_model_cnw2v2base():
# model = cnw2v2base()
# model.eval()
# return model
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