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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.0/preprocess_hubert_f0.py
import os
import librosa
import torch
import numpy as np
from fairseq import checkpoint_utils
from tqdm import tqdm
import torch
def load_hubert_model(hps):
# Load model
ckpt_path = hps.hubert_file
print("Load Hubert Model...")
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[ckpt_path],
suffix="",
)
model = models[0]
model.eval()
if torch.cuda.is_available():
model = model.cuda()
return model
def get_hubert_content(hmodel, wav_16k_tensor):
feats = wav_16k_tensor
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav_16k_tensor.device),
"padding_mask": padding_mask.to(wav_16k_tensor.device),
"output_layer": 9, # layer 9
}
with torch.no_grad():
logits = hmodel.extract_features(**inputs)
feats = hmodel.final_proj(logits[0]).squeeze(0)
return feats
def content_vector_encoder(model, audio_path, default_sampling_rate=16000):
"""
# content vector default sr: 16000
"""
wav16k, sr = librosa.load(audio_path, sr=default_sampling_rate)
device = next(model.parameters()).device
wav16k = torch.from_numpy(wav16k).to(device)
# (1, 256, frame_len)
content_feature = get_hubert_content(model, wav_16k_tensor=wav16k)
return content_feature.cpu().detach().numpy()
def repeat_expand_2d(content, target_len):
"""
content : [hubert_dim(256), src_len]
target: [hubert_dim(256), target_len]
"""
src_len = content.shape[-1]
target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(
content.device
)
temp = torch.arange(src_len + 1) * target_len / src_len
current_pos = 0
for i in range(target_len):
if i < temp[current_pos + 1]:
target[:, i] = content[:, current_pos]
else:
current_pos += 1
target[:, i] = content[:, current_pos]
return target
def get_mapped_features(raw_content_features, mapping_features):
"""
Content Vector: frameshift = 20ms, hop_size = 480 in 24k
Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms)
"""
source_hop = 480
target_hop = 256
factor = np.gcd(source_hop, target_hop)
source_hop //= factor
target_hop //= factor
print(
"Mapping source's {} frames => target's {} frames".format(
target_hop, source_hop
)
)
results = []
for index, mapping_feat in enumerate(tqdm(mapping_features)):
# mappping_feat: (mels_frame_len, n_mels)
target_len = len(mapping_feat)
# (source_len, 256)
raw_feats = raw_content_features[index][0].cpu().numpy().T
source_len, width = raw_feats.shape
# const ~= target_len * target_hop
const = source_len * source_hop // target_hop * target_hop
# (source_len * source_hop, dim)
up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0)
# (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
down_sampling_feats = np.average(
up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
)
err = abs(target_len - len(down_sampling_feats))
if err > 3:
print("index:", index)
print("mels:", mapping_feat.shape)
print("raw content vector:", raw_feats.shape)
print("up_sampling:", up_sampling_feats.shape)
print("down_sampling_feats:", down_sampling_feats.shape)
exit()
if len(down_sampling_feats) < target_len:
# (1, dim) -> (err, dim)
end = down_sampling_feats[-1][None, :].repeat(err, axis=0)
down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0)
# (target_len, dim)
feats = down_sampling_feats[:target_len]
results.append(feats)
return results
def extract_hubert_features_of_dataset(datasets, model, out_dir):
for utt in tqdm(datasets):
uid = utt["Uid"]
audio_path = utt["Path"]
content_vector_feature = content_vector_encoder(model, audio_path) # (T, 256)
save_path = os.path.join(out_dir, uid + ".npy")
np.save(save_path, content_vector_feature)
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