File size: 2,686 Bytes
d8bfe4a | 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 | import torch
import argparse
from tqdm import tqdm
from pathlib import Path
import numpy as np
import os
import torch.nn.functional as F
import soundfile as sf
from torchaudio.transforms import Resample
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from funasr import AutoModel
def load_audio(manifest_path):
import numpy as np
generated_pathes, tgt_pathes, gt_texts= [], [], []
with open(manifest_path, "r") as f:
for ind, line in enumerate(f):
if len(line.strip()) < 2:
continue
generated_path, tgt_path, gt_text = line.strip().split("\t")[:3]
generated_pathes.append(generated_path)
tgt_pathes.append(tgt_path)
gt_texts.append(gt_text)
return generated_pathes, tgt_pathes, gt_texts
def load_tsv(path):
with open(path, "r") as rf:
lines = rf.readlines()
return lines
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Inference')
parser.add_argument('-t', '--tsv', type=str)
parser.add_argument('-o', '--out_home', type=str)
args = parser.parse_args()
if os.path.exists(os.path.join(args.out_home, "emo.log")):
with open(os.path.join(args.out_home, "emo.log"), "r") as rf:
if len(rf.readlines()) > 100:
exit()
model = AutoModel(model="/workspace/echoloc/modelscope/iic/emotion2vec_plus_large/")
generated_pathes, tgt_pathes, gt_texts = load_audio(args.tsv)
simis = []
with torch.no_grad():
with open(os.path.join(args.out_home, "emo.log"), "w") as f:
for index, (est_path, tgt_path, gt_text) in enumerate(tqdm(zip(generated_pathes, tgt_pathes, gt_texts))):
try:
generated_emb = model.generate(est_path, granularity="utterance", extract_embedding=True, disable_pbar=True)[0]["feats"] # 1024
tgt_emb = model.generate(tgt_path, granularity="utterance", extract_embedding=True, disable_pbar=True)[0]["feats"] # 1024
simi = float(F.cosine_similarity(torch.FloatTensor([generated_emb]), torch.FloatTensor([tgt_emb])).item())
except Exception as e:
simi = -1.0
print(e)
simis.append(simi)
print("%s %s %f"%(est_path, tgt_path, simi), file=f)
print("------------------------------------------", file=f)
simis = np.array(simis)
print("with -1: emo2vec large:", np.mean(simis), file=f)
print("without -1: emo2vec large:", np.mean(simis[simis != -1]), " -1 num:", len(simis[simis==-1]), file=f)
|