Spaces:
Running
Running
File size: 3,939 Bytes
99bbd30 |
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 |
import os
import numpy as np
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
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,
embeddings: bool = False,
) -> np.ndarray:
r"""Predict emotions or extract embeddings from raw audio signal."""
# run through processor to normalize signal
# always returns a batch, so we just get the first entry
# then we put it on the device
y = processor(x, sampling_rate=sampling_rate)
y = y["input_values"][0]
y = y.reshape(1, -1)
y = torch.from_numpy(y).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__":
from pathlib import Path
import librosa
from tqdm import tqdm
import sys
test_lst = sys.argv[1]
output_path = sys.argv[2]
device = "cpu"
model_name = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = EmotionModel.from_pretrained(model_name).to(device)
ecos = 0
nums = 0
not_found = 0
with open(test_lst, "r") as fr:
lines = fr.readlines()
path = output_path
for idx, line in enumerate(lines):
gen_wav = path + "gen/" + str(idx).zfill(8) + ".wav"
target = path + "tgt/" + str(idx).zfill(8) + ".wav"
if Path(gen_wav).exists() and Path(target).exists():
try:
wav = librosa.load(gen_wav, sr=16000)[0]
except Exception as e:
print(f"Error in {gen_wav}, {e}")
not_found += 1
continue
try:
target = librosa.load(target, sr=16000)[0]
except Exception as e:
not_found += 1
print(f"Error in {target}, {e}")
continue
with torch.inference_mode():
gen_emo_embs = process_func(wav, 16000, embeddings=True)
target_emo_embs = process_func(target, 16000, embeddings=True)
emo_cos = np.sum(gen_emo_embs * target_emo_embs) / (
np.linalg.norm(gen_emo_embs) * np.linalg.norm(target_emo_embs)
)
emo_acc = emo_cos * 100
else:
# raise FileNotFoundError(wav, target)
not_found += 1
continue
_cos = emo_acc
ecos += _cos
nums += 1
print(f"EMO_SIM: {ecos / nums:.3f}")
|