File size: 9,945 Bytes
6f024ab |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 |
#!/usr/bin/env python3
# Copyright 2025 Xiaomi Corp. (authors: Han Zhu
# Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Calculate UTMOS score with automatic Mean Opinion Score (MOS) prediction system
adapted from https://huggingface.co/spaces/sarulab-speech/UTMOS-demo
# Download model checkpoints
wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt -P model/huggingface/utmos/utmos.pt
wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt -P model/huggingface/utmos/wav2vec_small.pt
"""
import argparse
import logging
import os
import fairseq
import librosa
import numpy as np
import pytorch_lightning as pl
import soundfile as sf
import torch
import torch.nn as nn
from tqdm import tqdm
logging.basicConfig(level=logging.INFO)
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--wav-path", type=str, help="path of the evaluated speech directory"
)
parser.add_argument(
"--utmos-model-path",
type=str,
default="model/huggingface/utmos/utmos.pt",
help="path of the UTMOS model",
)
parser.add_argument(
"--ssl-model-path",
type=str,
default="model/huggingface/utmos/wav2vec_small.pt",
help="path of the wav2vec SSL model",
)
return parser
class UTMOSScore:
"""Predicting score for each audio clip."""
def __init__(self, utmos_model_path, ssl_model_path):
self.sample_rate = 16000
self.device = (
torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
)
self.model = (
BaselineLightningModule.load_from_checkpoint(
utmos_model_path, ssl_model_path=ssl_model_path
)
.eval()
.to(self.device)
)
def score(self, wavs: torch.Tensor) -> torch.Tensor:
"""
Args:
wavs: waveforms to be evaluated. When len(wavs) == 1 or 2,
the model processes the input as a single audio clip. The model
performs batch processing when len(wavs) == 3.
"""
if len(wavs.shape) == 1:
out_wavs = wavs.unsqueeze(0).unsqueeze(0)
elif len(wavs.shape) == 2:
out_wavs = wavs.unsqueeze(0)
elif len(wavs.shape) == 3:
out_wavs = wavs
else:
raise ValueError("Dimension of input tensor needs to be <= 3.")
bs = out_wavs.shape[0]
batch = {
"wav": out_wavs,
"domains": torch.zeros(bs, dtype=torch.int).to(self.device),
"judge_id": torch.ones(bs, dtype=torch.int).to(self.device) * 288,
}
with torch.no_grad():
output = self.model(batch)
return output.mean(dim=1).squeeze(1).cpu().detach() * 2 + 3
def score_dir(self, dir, dtype="float32"):
def _load_speech_task(fname, sample_rate):
wav_data, sr = sf.read(fname, dtype=dtype)
if sr != sample_rate:
wav_data = librosa.resample(
wav_data, orig_sr=sr, target_sr=self.sample_rate
)
wav_data = torch.from_numpy(wav_data)
return wav_data
score_lst = []
for fname in tqdm(os.listdir(dir)):
speech = _load_speech_task(os.path.join(dir, fname), self.sample_rate)
speech = speech.to(self.device)
with torch.no_grad():
score = self.score(speech)
score_lst.append(score.item())
return np.mean(score_lst)
def load_ssl_model(ckpt_path="wav2vec_small.pt"):
SSL_OUT_DIM = 768
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[ckpt_path]
)
ssl_model = model[0]
ssl_model.remove_pretraining_modules()
return SSL_model(ssl_model, SSL_OUT_DIM)
class BaselineLightningModule(pl.LightningModule):
def __init__(self, ssl_model_path):
super().__init__()
self.construct_model(ssl_model_path)
self.save_hyperparameters()
def construct_model(self, ssl_model_path):
self.feature_extractors = nn.ModuleList(
[
load_ssl_model(ckpt_path=ssl_model_path),
DomainEmbedding(3, 128),
]
)
output_dim = sum(
[
feature_extractor.get_output_dim()
for feature_extractor in self.feature_extractors
]
)
output_layers = [
LDConditioner(judge_dim=128, num_judges=3000, input_dim=output_dim)
]
output_dim = output_layers[-1].get_output_dim()
output_layers.append(
Projection(
hidden_dim=2048,
activation=torch.nn.ReLU(),
range_clipping=False,
input_dim=output_dim,
)
)
self.output_layers = nn.ModuleList(output_layers)
def forward(self, inputs):
outputs = {}
for feature_extractor in self.feature_extractors:
outputs.update(feature_extractor(inputs))
x = outputs
for output_layer in self.output_layers:
x = output_layer(x, inputs)
return x
class SSL_model(nn.Module):
def __init__(self, ssl_model, ssl_out_dim) -> None:
super(SSL_model, self).__init__()
self.ssl_model, self.ssl_out_dim = ssl_model, ssl_out_dim
def forward(self, batch):
wav = batch["wav"]
wav = wav.squeeze(1) # [batches, wav_len]
res = self.ssl_model(wav, mask=False, features_only=True)
x = res["x"]
return {"ssl-feature": x}
def get_output_dim(self):
return self.ssl_out_dim
class DomainEmbedding(nn.Module):
def __init__(self, n_domains, domain_dim) -> None:
super().__init__()
self.embedding = nn.Embedding(n_domains, domain_dim)
self.output_dim = domain_dim
def forward(self, batch):
return {"domain-feature": self.embedding(batch["domains"])}
def get_output_dim(self):
return self.output_dim
class LDConditioner(nn.Module):
"""
Conditions ssl output by listener embedding
"""
def __init__(self, input_dim, judge_dim, num_judges=None):
super().__init__()
self.input_dim = input_dim
self.judge_dim = judge_dim
self.num_judges = num_judges
assert num_judges is not None
self.judge_embedding = nn.Embedding(num_judges, self.judge_dim)
# concat [self.output_layer, phoneme features]
self.decoder_rnn = nn.LSTM(
input_size=self.input_dim + self.judge_dim,
hidden_size=512,
num_layers=1,
batch_first=True,
bidirectional=True,
) # linear?
self.out_dim = self.decoder_rnn.hidden_size * 2
def get_output_dim(self):
return self.out_dim
def forward(self, x, batch):
judge_ids = batch["judge_id"]
if "phoneme-feature" in x.keys():
concatenated_feature = torch.cat(
(
x["ssl-feature"],
x["phoneme-feature"]
.unsqueeze(1)
.expand(-1, x["ssl-feature"].size(1), -1),
),
dim=2,
)
else:
concatenated_feature = x["ssl-feature"]
if "domain-feature" in x.keys():
concatenated_feature = torch.cat(
(
concatenated_feature,
x["domain-feature"]
.unsqueeze(1)
.expand(-1, concatenated_feature.size(1), -1),
),
dim=2,
)
if judge_ids is not None:
concatenated_feature = torch.cat(
(
concatenated_feature,
self.judge_embedding(judge_ids)
.unsqueeze(1)
.expand(-1, concatenated_feature.size(1), -1),
),
dim=2,
)
decoder_output, (h, c) = self.decoder_rnn(concatenated_feature)
return decoder_output
class Projection(nn.Module):
def __init__(self, input_dim, hidden_dim, activation, range_clipping=False):
super(Projection, self).__init__()
self.range_clipping = range_clipping
output_dim = 1
if range_clipping:
self.proj = nn.Tanh()
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
activation,
nn.Dropout(0.3),
nn.Linear(hidden_dim, output_dim),
)
self.output_dim = output_dim
def forward(self, x, batch):
output = self.net(x)
# range clipping
if self.range_clipping:
return self.proj(output) * 2.0 + 3
else:
return output
def get_output_dim(self):
return self.output_dim
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
UTMOS = UTMOSScore(
utmos_model_path=args.utmos_model_path,
ssl_model_path=args.ssl_model_path,
)
score = UTMOS.score_dir(args.wav_path)
logging.info(f"UTMOS score: {score:.2f}")
|