Spaces:
Running
Running
File size: 2,316 Bytes
960cd20 |
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 |
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)
|