bert_vits2/get_emo.py

import librosa
import numpy as np
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)

from contants 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


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


def get_emo(audio, emotion_model, processor):
    wav, sr = librosa.load(audio, 16000)
    device = config.system.device
    return process_func(
        np.expand_dims(wav, 0).astype(np.float),
        sr,
        emotion_model,
        processor,
        device,
        embeddings=True,
    ).squeeze(0)