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from packaging import version
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
import fairseq
from s3prl.upstream.interfaces import UpstreamBase
SAMPLE_RATE = 16000
EXAMPLE_SEC = 5
class UpstreamExpert(UpstreamBase):
def __init__(self, ckpt, **kwargs):
super().__init__(**kwargs)
assert version.parse(fairseq.__version__) > version.parse(
"0.10.2"
), "Please install the fairseq master branch."
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[ckpt]
)
self.model = model[0]
self.task = task
if len(self.hooks) == 0:
module_name = "self.model.encoder.layers"
for module_id in range(len(eval(module_name))):
self.add_hook(
f"{module_name}[{module_id}]",
lambda input, output: input[0].transpose(0, 1),
)
self.add_hook("self.model.encoder", lambda input, output: output[0])
def forward(self, wavs):
if self.task.cfg.normalize:
wavs = [F.layer_norm(wav, wav.shape) for wav in wavs]
device = wavs[0].device
wav_lengths = torch.LongTensor([len(wav) for wav in wavs]).to(device)
wav_padding_mask = ~torch.lt(
torch.arange(max(wav_lengths)).unsqueeze(0).to(device),
wav_lengths.unsqueeze(1),
)
padded_wav = pad_sequence(wavs, batch_first=True)
features, feat_padding_mask = self.model.extract_features(
padded_wav,
padding_mask=wav_padding_mask,
mask=None,
)
return {
"default": features,
} |