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import os
import fairseq
import pytorch_lightning as pl
import requests
import torch
import torch.nn as nn
from tqdm import tqdm
UTMOS_CKPT_URL = "https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt"
WAV2VEC_URL = "https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt"
"""
UTMOS score, automatic Mean Opinion Score (MOS) prediction system,
adapted from https://huggingface.co/spaces/sarulab-speech/UTMOS-demo
"""
class UTMOSScore:
"""Predicting score for each audio clip."""
def __init__(self, device, ckpt_path="epoch=3-step=7459.ckpt"):
self.device = device
filepath = os.path.join(os.path.dirname(__file__), ckpt_path)
if not os.path.exists(filepath):
download_file(UTMOS_CKPT_URL, filepath)
self.model = BaselineLightningModule.load_from_checkpoint(filepath).eval().to(device)
def score(self, wavs: torch.Tensor) -> torch.Tensor:
"""
Args:
wavs: audio waveform 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 download_file(url, filename):
"""
Downloads a file from the given URL
Args:
url (str): The URL of the file to download.
filename (str): The name to save the file as.
"""
print(f"Downloading file {filename}...")
response = requests.get(url, stream=True)
response.raise_for_status()
total_size_in_bytes = int(response.headers.get("content-length", 0))
progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
with open(filename, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
progress_bar.update(len(chunk))
f.write(chunk)
progress_bar.close()
def load_ssl_model(ckpt_path="wav2vec_small.pt"):
filepath = os.path.join(os.path.dirname(__file__), ckpt_path)
if not os.path.exists(filepath):
download_file(WAV2VEC_URL, filepath)
SSL_OUT_DIM = 768
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([filepath])
ssl_model = model[0]
ssl_model.remove_pretraining_modules()
return SSL_model(ssl_model, SSL_OUT_DIM)
class BaselineLightningModule(pl.LightningModule):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.construct_model()
self.save_hyperparameters()
def construct_model(self):
self.feature_extractors = nn.ModuleList(
[load_ssl_model(ckpt_path="wav2vec_small.pt"), 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, audio_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 != 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 != 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
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