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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 sys
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:
device = (
"cuda:0"
if torch.cuda.is_available()
else (
"mps"
if sys.platform == "darwin" and torch.backends.mps.is_available()
else "cpu"
)
)
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(path):
wav, sr = librosa.load(path, 16000)
device = config.bert_gen_config.device
print("successfully generate the emo vec")
return process_func(
np.expand_dims(wav, 0).astype(np.float),
sr,
model,
processor,
device,
embeddings=True,
).squeeze(0)
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=16)
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!") |