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import json
import hashlib
import random
PATH = "/work/fast_data_yinghao/MTG/mtg-jamendo-dataset/data"
# Load your dataset
number_to_letter = {
0: "A",
1: "B",
2: "C",
3: "D"
}
def get_emotion(_="test"):
data_samples = []
for split in [_]:
input_file_path = f'{PATH}/splits/split-0/autotagging_moodtheme-{split}.tsv' # Replace with the actual path to your dataset
with open(input_file_path, "r") as f:
for idx, line in enumerate(f):
if idx > 0:
tmp = line.strip().split("\t")
emotions = tmp[5:]
audio_path = tmp[3]
audioid = audio_path.split("/")[-1]
if "low" not in audioid:
audioid = audioid[:-4] + ".low.mp3"
data_sample = {
"instruction": "Please provide the emotion of given audio.",
"input": f"<|SOA|>f'{audio_path}'<|EOA|>",
"output": ", ".join(sorted([emotion.split("---")[-1] for emotion in emotions])),
"uuid": audio_path,
"audioid": audio_path,
"split": [split if split != "validation" else "dev"],
"task_type": {"major": ["global_MIR"], "minor": ["emotion_classification"]},
"domain": "music",
"source": "MTG",
"other": {"tag":"null"}
}
data_samples.append(data_sample)
# if idx > 2:
# break
f.close()
existed_uuid_list = set()
all_emotions = set(emotion for data_sample in data_samples for emotion in data_sample["output"].split(", "))
for data_sample in data_samples:
# change testset instruction to the choice format
data_sample["instruction"] = data_sample["instruction"] + " If you can find multiple emotions, please output in alphabeta order. Use ', ' to split multiple tags."
# change uuid
uuid_string = f"{data_sample['instruction']}#{data_sample['input']}#{data_sample['output']}"
unique_id = hashlib.md5(uuid_string.encode()).hexdigest()[:16] #只取前16位
if unique_id in existed_uuid_list:
sha1_hash = hashlib.sha1(uuid_string.encode()).hexdigest()[:16] # 为了相加的时候位数对应上 # 将 MD5 和 SHA1 结果相加,并计算新的 MD5 作为最终的 UUID
unique_id = hashlib.md5((unique_id + sha1_hash).encode()).hexdigest()[:16]
existed_uuid_list.add(unique_id)
data_sample["uuid"] = f"{unique_id}"
return data_samples
if __name__ == "__main__":
print("start")
for split in ["test", "train", "validation"]:
data_samples = get_emotion(split)
# Save to JSONL format
output_file_path = f'emotion_{split}.jsonl'
with open(output_file_path, 'w') as outfile:
for sample in data_samples:
json.dump(sample, outfile)
outfile.write('\n')
outfile.close()
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