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import sys
sys.path.append('..')
sys.path.append('.')
from aac_metrics import evaluate
from inference import AudioBartInference
from tqdm import tqdm
import os
import pandas as pd
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
metric_list = ["bleu_1", "bleu_4", "rouge_l", "meteor", "spider_fl"]
if __name__ == "__main__":
dataset = "AudioCaps"
# dataset = "clotho"
ckpt_path = "/data/jyk/aac_results/bart_base/audiocaps_35e5_2000/checkpoints/epoch_8"
# ckpt_path = "/data/jyk/aac_results/masking/linear_scalinEg/checkpoints/epoch_14"
max_encodec_length = 1022
infer_module = AudioBartInference(ckpt_path, max_encodec_length)
from_encodec = True
csv_path = f"/workspace/audiobart/csv/{dataset}/test.csv"
base_path = f"/data/jyk/aac_dataset/{dataset}/encodec_16"
clap_name = "clap_audio_fused"
df = pd.read_csv(csv_path)
generation_config = {
"_from_model_config": True,
"bos_token_id": 0,
"decoder_start_token_id": 2,
"early_stopping": True,
"eos_token_id": 2,
"forced_bos_token_id": 0,
"forced_eos_token_id": 2,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"pad_token_id": 1,
"max_length": 50
}
print(f"> Making Predictions for model {ckpt_path}...")
predictions = []
references = []
for idx in tqdm(range(len(df)), dynamic_ncols=True, colour="BLUE"):
if not from_encodec:
wav_path = df.loc[idx]['file_name']
else:
wav_path = df.loc[idx]['file_path']
wav_path = os.path.join(base_path,wav_path)
if not os.path.exists(wav_path):
pass
if not from_encodec:
prediction = infer_module.infer(wav_path)
else:
prediction = infer_module.infer_from_encodec(wav_path, clap_name, generation_config)
predictions.append(prediction[0])
reference = [df.loc[idx]['caption_1'],df.loc[idx]['caption_2'],df.loc[idx]['caption_3'],df.loc[idx]['caption_4'],df.loc[idx]['caption_5'] ]
references.append(reference)
print("> Evaluating predictions...")
result = evaluate(predictions, references, metrics=metric_list)
result = {k: round(v.item(),4) for k, v in result[0].items()}
keys = list(result.keys())
for key in keys:
if "fluerr" in key:
del result[key]
print(result) |