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import time
import librosa
import torch
import torch.nn.functional as F
import soundfile as sf
#import logging
#logging.getLogger("numba").setLevel(logging.WARNING)
from transformers import (
Wav2Vec2FeatureExtractor,
HubertModel,
)
import utils
import torch.nn as nn
cnhubert_base_path = None
class CNHubert(nn.Module):
def __init__(self):
super().__init__()
self.model = HubertModel.from_pretrained(cnhubert_base_path)
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
cnhubert_base_path
)
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_model():
model = CNHubert()
model.eval()
return model
# 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
def get_content(hmodel, wav_16k_tensor):
with torch.no_grad():
feats = hmodel(wav_16k_tensor)
return feats.transpose(1, 2)
if __name__ == "__main__":
model = get_model()
src_path = "/Users/Shared/ει³ι’2.wav"
wav_16k_tensor = utils.load_wav_to_torch_and_resample(src_path, 16000)
model = model
wav_16k_tensor = wav_16k_tensor
feats = get_content(model, wav_16k_tensor)
print(feats.shape)
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