File size: 4,470 Bytes
1b9cb8c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import argparse
import os
from pathlib import Path

import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)

import utils
from config import config


class RegressionHead(nn.Module):
    r"""Classification head."""

    def __init__(self, config):
        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)

        return x


class EmotionModel(Wav2Vec2PreTrainedModel):
    r"""Speech emotion classifier."""

    def __init__(self, config):
        super().__init__(config)

        self.config = config
        self.wav2vec2 = Wav2Vec2Model(config)
        self.classifier = RegressionHead(config)
        self.init_weights()

    def forward(
        self,
        input_values,
    ):
        outputs = self.wav2vec2(input_values)
        hidden_states = outputs[0]
        hidden_states = torch.mean(hidden_states, dim=1)
        logits = self.classifier(hidden_states)

        return hidden_states, logits


class AudioDataset(Dataset):
    def __init__(self, list_of_wav_files, sr, processor):
        self.list_of_wav_files = list_of_wav_files
        self.processor = processor
        self.sr = sr

    def __len__(self):
        return len(self.list_of_wav_files)

    def __getitem__(self, idx):
        wav_file = self.list_of_wav_files[idx]
        audio_data, _ = librosa.load(wav_file, sr=self.sr)
        processed_data = self.processor(audio_data, sampling_rate=self.sr)[
            "input_values"
        ][0]
        return torch.from_numpy(processed_data)


def process_func(
    x: np.ndarray,
    sampling_rate: int,
    model: EmotionModel,
    processor: Wav2Vec2Processor,
    device: str,
    embeddings: bool = False,
) -> np.ndarray:
    r"""Predict emotions or extract embeddings from raw audio signal."""
    model = model.to(device)
    y = processor(x, sampling_rate=sampling_rate)
    y = y["input_values"][0]
    y = torch.from_numpy(y).unsqueeze(0).to(device)

    # run through model
    with torch.no_grad():
        y = model(y)[0 if embeddings else 1]

    # convert to numpy
    y = y.detach().cpu().numpy()

    return y


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-c", "--config", type=str, default=config.bert_gen_config.config_path
    )
    parser.add_argument(
        "--num_processes", type=int, default=config.bert_gen_config.num_processes
    )
    args, _ = parser.parse_known_args()
    config_path = args.config
    hps = utils.get_hparams_from_file(config_path)

    device = config.bert_gen_config.device

    model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    if not Path(model_name).joinpath("pytorch_model.bin").exists():
        utils.download_emo_models(config.mirror, REPO_ID, model_name)

    processor = Wav2Vec2Processor.from_pretrained(model_name)
    model = EmotionModel.from_pretrained(model_name).to(device)

    lines = []
    with open(hps.data.training_files, encoding="utf-8") as f:
        lines.extend(f.readlines())

    with open(hps.data.validation_files, encoding="utf-8") as f:
        lines.extend(f.readlines())

    wavnames = [line.split("|")[0] for line in lines]
    dataset = AudioDataset(wavnames, 16000, processor)
    data_loader = DataLoader(
        dataset,
        batch_size=1,
        shuffle=False,
        num_workers=min(args.num_processes, os.cpu_count() - 1),
    )

    with torch.no_grad():
        for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
            wavname = wavnames[i]
            emo_path = wavname.replace(".wav", ".emo.npy")
            if os.path.exists(emo_path):
                continue
            emb = model(data.to(device))[0].detach().cpu().numpy()
            np.save(emo_path, emb)

    print("Emo vec 生成完毕!")