File size: 2,579 Bytes
901e06a | 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 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import fairseq
import torchaudio
import soundfile as sf
import torch.nn.functional as F
from fairseq.data.audio.audio_utils import convert_waveform
class HubertFeatureReader:
"""
Wrapper class to run inference on HuBERT model.
Helps extract features for a given audio file.
"""
def __init__(self, checkpoint_path, layer, max_chunk=1600000, use_cuda=True):
(
model,
cfg,
task,
) = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])
self.model = model[0].eval()
self.task = task
self.layer = layer
self.max_chunk = max_chunk
self.use_cuda = use_cuda
if self.use_cuda:
self.model.cuda()
def read_audio(self, path, ref_len=None, channel_id=None):
wav, sr = torchaudio.load(path)
if channel_id is not None:
assert (
wav.ndim == 2
), f"Expected stereo input when channel_id is given ({path})"
assert channel_id in [1, 2], "channel_id is expected to be in [1, 2]"
wav = wav[:, channel_id - 1]
wav, sr = convert_waveform(wav, sr, to_sample_rate=self.task.cfg.sample_rate)
wav = wav.squeeze(0).numpy()
if wav.ndim == 2:
wav = wav.mean(-1)
assert wav.ndim == 1, wav.ndim
assert sr == self.task.cfg.sample_rate, sr
if ref_len is not None and abs(ref_len - len(wav)) > 160:
print(f"ref {ref_len} != read {len(wav)} ({path})")
return wav
def get_feats(self, file_path, ref_len=None, channel_id=None):
x = self.read_audio(file_path, ref_len, channel_id)
with torch.no_grad():
x = torch.from_numpy(x).float()
if self.use_cuda:
x = x.cuda()
if self.task.cfg.normalize:
x = F.layer_norm(x, x.shape)
x = x.view(1, -1)
feat = []
for start in range(0, x.size(1), self.max_chunk):
x_chunk = x[:, start : start + self.max_chunk]
feat_chunk, _ = self.model.extract_features(
source=x_chunk,
padding_mask=None,
mask=False,
output_layer=self.layer,
)
feat.append(feat_chunk)
return torch.cat(feat, 1).squeeze(0)
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