control-vc/fairseq_feature_reader.py at main · MelissaChen15/control-vc · GitHub

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
"""
Feature reader for pre-trained hubert model

Reference:
 https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/gslm/speech2unit
"""

import gc
import os
import random
import shutil
import numpy as np

import torch
import tqdm

import fairseq
import soundfile as sf
import torch.nn.functional as F


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):
        (
            model,
            cfg,
            task,
        ) = fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]
        )
        self.model = model[0].eval().cuda()
        self.task = task
        self.layer = layer
        self.max_chunk = max_chunk

    def read_audio(self, path, ref_len=None):
        wav, sr = sf.read(path)
        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):
        x = self.read_audio(file_path, ref_len)
        with torch.no_grad():
            x = torch.from_numpy(x).float().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)


def get_feature_reader(feature_type):
    if feature_type == "hubert":
        return HubertFeatureReader
    else:
        raise NotImplementedError(f"{feature_type} is not supported.")


def get_feature_iterator(
    feature_type, checkpoint_path, layer, manifest_path, sample_pct
):
    feature_reader_cls = get_feature_reader(feature_type)
    with open(manifest_path, "r") as fp:
        lines = fp.read().split("\n")
        root = lines.pop(0).strip()
        file_path_list = [
            os.path.join(root, line.split("\t")[0])
            for line in lines
            if len(line) > 0
        ]
        if sample_pct < 1.0:
            file_path_list = random.sample(
                file_path_list, int(sample_pct * len(file_path_list))
            )
        num_files = len(file_path_list)
        reader = feature_reader_cls(
            checkpoint_path=checkpoint_path, layer=layer
        )

        def iterate():
            for file_path in file_path_list:
                feats = reader.get_feats(file_path)
                yield feats.cpu().numpy()

    return iterate, num_files


def get_features(
    feature_type, checkpoint_path, layer, manifest_path, sample_pct, flatten
):
    generator, num_files = get_feature_iterator(
        feature_type=feature_type,
        checkpoint_path=checkpoint_path,
        layer=layer,
        manifest_path=manifest_path,
        sample_pct=sample_pct,
    )
    iterator = generator()

    features_list = []
    for features in tqdm.tqdm(iterator, total=num_files):
        features_list.append(features)

    # Explicit clean up
    del iterator
    del generator
    gc.collect()
    torch.cuda.empty_cache()

    if flatten:
        return np.concatenate(features_list)

    return features_list


def get_and_dump_features(
    feature_type,
    checkpoint_path,
    layer,
    manifest_path,
    sample_pct,
    flatten,
    out_features_path,
):
    # Feature extraction
    features_batch = get_features(
        feature_type=feature_type,
        checkpoint_path=checkpoint_path,
        layer=layer,
        manifest_path=manifest_path,
        sample_pct=sample_pct,
        flatten=flatten,
    )

    # Save features
    out_dir_path = os.path.dirname(out_features_path)
    os.makedirs(out_dir_path, exist_ok=True)
    shutil.copyfile(
        manifest_path,
        os.path.join(out_dir_path, os.path.basename(manifest_path)),
    )
    np.save(out_features_path, features_batch)

    return features_batch