Spaces:
Running
Running
File size: 3,252 Bytes
02259d3 |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
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,
Wav2Vec2Model,
)
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/原音频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)
|