Spaces:
Runtime error
Runtime error
File size: 4,589 Bytes
0d80816 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
# 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://huggingface.co/m-a-p/MERT-v1-330M
import torch
from tqdm import tqdm
import numpy as np
from transformers import Wav2Vec2FeatureExtractor
from transformers import AutoModel
import torchaudio
import torchaudio.transforms as T
from sklearn.preprocessing import StandardScaler
def mert_encoder(model, processor, audio_path, hps):
"""
# mert default sr: 24000
"""
with torch.no_grad():
resample_rate = processor.sampling_rate
device = next(model.parameters()).device
input_audio, sampling_rate = torchaudio.load(audio_path)
input_audio = input_audio.squeeze()
if sampling_rate != resample_rate:
resampler = T.Resample(sampling_rate, resample_rate)
input_audio = resampler(input_audio)
inputs = processor(
input_audio, sampling_rate=resample_rate, return_tensors="pt"
).to(
device
) # {input_values: tensor, attention_mask: tensor}
outputs = model(**inputs, output_hidden_states=True) # list: len is 25
# [25 layer, Time steps, 1024 feature_dim]
# all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
# mert_features.append(all_layer_hidden_states)
feature = outputs.hidden_states[
hps.mert_feature_layer
].squeeze() # [1, frame len, 1024] -> [frame len, 1024]
return feature.cpu().detach().numpy()
def mert_features_normalization(raw_mert_features):
normalized_mert_features = list()
mert_features = np.array(raw_mert_features)
scaler = StandardScaler().fit(mert_features)
for raw_mert_feature in raw_mert_feature:
normalized_mert_feature = scaler.transform(raw_mert_feature)
normalized_mert_features.append(normalized_mert_feature)
return normalized_mert_features
def get_mapped_mert_features(raw_mert_features, mapping_features, fast_mapping=True):
source_hop = 320
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
)
)
mert_features = []
for index, mapping_feat in enumerate(tqdm(mapping_features)):
# mapping_feat: (mels_frame_len, n_mels)
target_len = mapping_feat.shape[0]
# (frame_len, 1024)
raw_feats = raw_mert_features[index].cpu().numpy()
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 mert vector:", raw_feats.shape)
print("up_sampling:", up_sampling_feats.shape)
print("const:", const)
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]
mert_features.append(feats)
return mert_features
def load_mert_model(hps):
print("Loading MERT Model: ", hps.mert_model)
# Load model
model_name = hps.mert_model
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
if torch.cuda.is_available():
model = model.cuda()
# model = model.eval()
preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(
model_name, trust_remote_code=True
)
return model, preprocessor
# loading the corresponding preprocessor config
# def load_preprocessor (model_name="m-a-p/MERT-v1-330M"):
# print('load_preprocessor...')
# preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(model_name,trust_remote_code=True)
# return preprocessor
|