File size: 56,813 Bytes
0eb3766 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 |
from email.mime import audio
import json
import os
from pandas import read_json
from regex import B, D
import tqdm
from typing import List, Dict, Any
import nltk
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from dataclasses import dataclass
from abc import ABC, abstractmethod
from rouge_score import rouge_scorer
import math
import time
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def read_json(file_path: str) -> Dict[str, Any]:
with open(file_path, "r") as f:
data = json.load(f)
return data
def exact_match_accuracy(predictions: List[str], references: List[str]) -> float:
correct = 0
for pred, ref in zip(predictions, references):
if isinstance(ref, str):
ref = [ref]
if isinstance(ref, int):
ref = [ref]
is_match_this_turn = False
for r in ref:
if pred.strip() == r.strip():
is_match_this_turn = True
if is_match_this_turn:
correct += 1
return correct / len(predictions) if predictions else 0.0
def blur_match_accuracy(predictions: List[str], references: List[str]) -> float:
correct = 0
for pred, ref in zip(predictions, references):
# if isinstance(ref, int):
# if == ref:
if str(ref) in str(pred).strip().lower():
correct += 1
return correct / len(predictions) if predictions else 0.0
def calculate_f1(predictions: List[str], references: List[str]) -> float:
def compute_f1(pred: str, ref: str) -> float:
pred_tokens = pred.strip().split()
ref_tokens = ref.strip().split()
common_tokens = set(pred_tokens) & set(ref_tokens)
num_common = len(common_tokens)
if num_common == 0:
return 0.0
precision = num_common / len(pred_tokens)
recall = num_common / len(ref_tokens)
return 2 * precision * recall / (precision + recall)
total_f1 = 0.0
for pred, ref in zip(predictions, references):
if isinstance(ref, str):
ref = [ref]
max_f1 = 0.0
for r in ref:
max_f1 = max(compute_f1(pred, r), max_f1)
total_f1 += max_f1
return total_f1 / len(predictions) if predictions else 0.0
def rouge_evaluation(predictions: List[str], references: List[str]) -> Dict[str, float]:
scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
rouge1_scores, rouge2_scores, rougel_scores = [], [], []
for pred, ref in zip(predictions, references):
if isinstance(ref, str):
ref = [ref]
rouge1, rouge2, rougeL = 0, 0, 0
for r in ref:
scores = scorer.score(r, pred)
rouge1 = max(scores['rouge1'].fmeasure, rouge1)
rouge2 = max(scores['rouge2'].fmeasure, rouge2)
rougeL = max(scores['rougeL'].fmeasure, rougeL)
rouge1_scores.append(rouge1)
rouge2_scores.append(rouge2)
rougel_scores.append(rougeL)
return {
'rouge1': sum(rouge1_scores) / len(rouge1_scores),
'rouge2': sum(rouge2_scores) / len(rouge2_scores),
'rougeL': sum(rougel_scores) / len(rougel_scores),
}
def bleu_evaluation(predictions: List[str], references: List[str]) -> Dict[str, float]:
smoothie = SmoothingFunction().method4
bleu1_scores, bleu2_scores, bleu3_scores, bleu4_scores = [], [], [], []
for pred, ref in zip(predictions, references):
hypothesis = nltk.word_tokenize(pred)
if isinstance(ref, str):
ref = [ref]
bleu1, bleu2, bleu3, bleu4 = 0, 0, 0, 0
for r in ref:
reference = [nltk.word_tokenize(r)]
bleu1 = max(sentence_bleu(reference, hypothesis, weights=(1, 0, 0, 0), smoothing_function=smoothie), bleu1)
bleu2 = max(sentence_bleu(reference, hypothesis, weights=(0.5, 0.5, 0, 0), smoothing_function=smoothie), bleu2)
bleu3 = max(sentence_bleu(reference, hypothesis, weights=(1/3, 1/3, 1/3, 0), smoothing_function=smoothie), bleu3)
bleu4 = max(sentence_bleu(reference, hypothesis, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=smoothie), bleu4)
bleu1_scores.append(bleu1)
bleu2_scores.append(bleu2)
bleu3_scores.append(bleu3)
bleu4_scores.append(bleu4)
return {
'bleu1': sum(bleu1_scores) / len(bleu1_scores) if bleu1_scores else 0.0,
'bleu2': sum(bleu2_scores) / len(bleu2_scores) if bleu2_scores else 0.0,
'bleu3': sum(bleu3_scores) / len(bleu3_scores) if bleu3_scores else 0.0,
'bleu4': sum(bleu4_scores) / len(bleu4_scores) if bleu4_scores else 0.0,
}
def mean_absolute_error(predictions: List[float], references: List[float]) -> float:
if not predictions:
return 0.0
error_sum = 0.0
for p, r in zip(predictions, references):
error_sum += abs(p - r)
return error_sum / len(predictions)
def mean_squared_error(predictions: List[float], references: List[float]) -> float:
if not predictions:
return 0.0
error_sum = 0.0
for p, r in zip(predictions, references):
error_sum += (p - r) ** 2
return error_sum / len(predictions)
def root_mean_squared_error(predictions: List[float], references: List[float]) -> float:
return math.sqrt(mean_squared_error(predictions, references))
def post_process_output(output: str) -> str:
cnt = 0
for d in output:
if d['gt'] in d['response'].strip().lower():
cnt += 1
acc = round(cnt / len(output), 4)
print(f"Accuracy: {acc}")
return acc
def evaluation_accuracy(predictions: List[str]) -> Dict[str, float]:
correct = 0
for pred in predictions:
if pred == '1':
correct += 1
return correct / len(predictions) if predictions else 0.0
class AudioComprehensionModel:
def __init__(self, model_name: str):
self.model_name = model_name
self.load_model()
def load_model(self):
if 'qwen-audio-chat' in self.model_name.lower():
self.model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map='cuda', trust_remote_code=True).eval()
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, trust_remote_code=True)
self.tokenizer.padding_side = 'left'
self.tokenizer.pad_token_id = self.tokenizer.eod_id
elif 'qwen2' in self.model_name.lower():
self.processor = AutoProcessor.from_pretrained(self.model_name)
print(self.processor.chat_template)
self.model = Qwen2AudioForConditionalGeneration.from_pretrained(self.model_name, device_map="auto").eval()
elif 'new_model_name' in self.model_name.lower():
# support to load self-build models here
pass
else:
raise ValueError(f"Unsupported model name: {self.model_name}")
def generate(self, prompt: str, max_new_tokens=256, audio_path: str=None) -> str:
if "qwen-audio-chat" in self.model_name.lower():
query = self.tokenizer.from_list_format([
{'audio': audio_path}, # Either a local path or an url
{'text': prompt} # The query,
])
response, history = self.model.chat(self.tokenizer, query=query, history=None)
return response
elif "qwen2" in self.model_name.lower():
conversation = [
{'role': 'system', 'content': 'You are a helpful assistant.'},
{"role": "user", "content": [
{"type": "audio", "audio": audio_path},
{"type": "text", "text": prompt},
]},
]
text = self.processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = []
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(
librosa.load(
ele['audio'],
sr=self.processor.feature_extractor.sampling_rate)[0]
)
# print(text)
inputs = self.processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to("cuda")
inputs = inputs.to("cuda")
# print(inputs)
# exit(0)
generate_ids = self.model.generate(**inputs, max_length=300)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = self.processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
return response
elif "new" in self.model_name.lower():
# support to generate response based on self-build models here
pass
else:
raise ValueError(f"Unsupported model name: {self.model_name}")
@dataclass
class Instance:
input: Dict[str, Any]
output: Dict[str, Any]
id: str
class BaseTask(ABC):
def __init__(self, task_data: Dict[str, Any], model: AudioComprehensionModel, audio_dir: str = None, output_dir: str = None, task_name: str = None):
self.task_data = read_json(task_data)
self.model = model
self.audio_dir = audio_dir # should include the audios files
self.data = self._parse_data(self.task_data)
self.choice_candidate = self._get_choice_candidate(self.task_data)
self.task_name = os.path.dirname(task_data).split("/")[-1] if task_name is None else task_name
self.output_dir = output_dir
os.makedirs(self.output_dir, exist_ok=True) if self.output_dir else None
self.references = []
self.predictions = []
def save_predictions(self, audio_paths):
results = []
for gt, response, audio_path in zip(self.references, self.predictions, audio_paths):
results.append({
'gt': gt,
'response': response,
'audio_path': audio_path,
})
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = os.path.join(self.output_dir, f'{self.task_name }_{time_prefix}.json') if self.output_dir else f'{self.task_name }_{time_prefix}.json'
json.dump(results, open(results_file, 'w'))
@abstractmethod
def _get_choice_candidate(self):
pass
@abstractmethod
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
pass
@abstractmethod
def evaluate(self) -> Dict[str, float]:
pass
@abstractmethod
def run_inference(self):
pass
class EvaluationTask(BaseTask):
"""
Used to determine whether the results generated by the model are correct
"""
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return task_data
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
return ["None"]
def save_predictions(self, audio_paths):
results = []
for gt, response, audio_path in zip(self.references, self.predictions, audio_paths):
results.append({
'gt': gt[0],
'response': gt[1],
'audio_path': audio_path,
'llm_prediction': response,
})
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = os.path.join(self.output_dir, f'{self.task_name }_{time_prefix}.json') if self.output_dir else f'{self.task_name }_{time_prefix}.json'
json.dump(results, open(results_file, 'w'))
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
prompt = " will provide you with a Ground-truth label and a Prediction label. The label can either be a single string or a list of multiple labels. I need you to compare these two labels on a semantic level.\nSpecifically, I want you to evaluate whether the Prediction label semantically matches, is partially aligned, includes, or describes the Ground-truth label (or the semantic meaning represented by the list of labels). If any of these conditions are satisfied, consider it a match.\n\nHere are some examples of successful matches:\n\nGround-truth label: \"rain\"\nPrediction label: \"The sound in the audio is rain falling\"\n(This is considered a match.)\nGround-truth label: [\"decrease\", \"volume\", \"none\"]\nPrediction label: \"The intent in the audio is to adjust the volume\"(This is also considered a match.)\nIf the labels successfully match, assign a score of 1. If they do not match, assign a score of 0.**Imporant!!!, only output the score (0 or 1), no explanation.** \n\nGround-truth label:{}\nPrediction label:{}"
gt = inst["gt"]
response = inst["response"]
prompt = prompt.format(gt, response)
try:
response = self.model.generate(prompt)
# print(response)
except Exception as e:
response = "None"
continue
self.predictions.append(response)
self.references.append([inst["gt"], inst["response"]])
audio_paths.append(inst["audio_path"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = evaluation_accuracy(self.predictions)
return {"accuracy": acc}
class AccentSexClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
return ['female', 'male']
def save_predictions(self, audio_paths):
results = []
for gt, response, audio_path in zip(self.references, self.predictions, audio_paths):
results.append({
'gt': gt,
'response': response,
'audio_path': audio_path,
})
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = os.path.join(self.output_dir, f'{self.task_name }_{time_prefix}.json') if self.output_dir else f'{self.task_name }_{time_prefix}.json'
json.dump(results, open(results_file, 'w'))
def run_inference(self):
self.predictions = []
self.references = []
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question by directly choose a choice from choice candidates. Questions: {question}, Candidate choices: {self.choice_candidate}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except:
print("error audio {}".format(inst.input["audio_file"]))
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class AcousticSceneClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
print(f"Choice candidates: {self.choice_candidate}")
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the input music and then determine the category of the acoustic scene. The candidate scene category are {self.choice_candidate}. Please output **only one category** from the provided candidate categories, and **DO NOT** output any other words.\nQuestions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class AnimalSoundDetection(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data) -> List[str]:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
print(f"Choice candidates: {self.choice_candidate}")
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question by directly choose a choice from choice candidates, without other words. Questions: {question}, Candidate choices: {self.choice_candidate}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class AudioCaptions(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
return ["None"]
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
bleu = bleu_evaluation(self.predictions, self.references)
return {"bleu1": bleu['bleu1']}
class AudioCaptionsClotho(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
return ["None"]
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = bleu_evaluation(self.predictions, self.references)
return {"accuracy": acc}
class AudioQA(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data) -> List[str]:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question by directly choose a choice from choice candidates. Questions: {question}, Candidate choices: {self.choice_candidate}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class BirdSoundDetection(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: List[Instance]) -> List[str]:
return ["Yes", "No"]
def save_predictions(self, audio_paths):
results = []
for gt, response, audio_path in zip(self.references, self.predictions, audio_paths):
results.append({
'gt': gt,
'response': response,
'audio_path': audio_path,
})
time_prefix = time.strftime('%y%m%d%H%M%S', time.localtime())
results_file = os.path.join(self.output_dir, f'{self.task_name }_{time_prefix}.json') if self.output_dir else f'{self.task_name }_{time_prefix}.json'
json.dump(results, open(results_file, 'w'))
def run_inference(self):
self.predictions = []
self.references = []
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question by directly choose a choice from choice candidates. Questions: {question}, Candidate choices: {self.choice_candidate}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append("Yes" if inst.output["text"] == 1 else "No")
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class EnvironmentSoundRecognition(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data) -> List[str]:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question by directly choose a choice from choice candidates. Questions: {question}, Candidate choices: {self.choice_candidate}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print(f"error {e}")
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = blur_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class IntentClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
intent_label = data['intent_label']
return intent_label
def run_inference(self):
audio_paths = []
candidate_actions = ','.join([k for k in self.choice_candidate['action'].keys() if not k[0].isdigit()])
candidate_objects = ','.join([k for k in self.choice_candidate['object'].keys() if not k[0].isdigit()])
candidate_locations = ','.join([k for k in self.choice_candidate['location'].keys() if not k[0].isdigit()])
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then detect the intention. The intention triplet includes three parts: action, object, and location. The candicate actions are {candidate_actions}, candidate objects are {candidate_objects}, and candidate locations are {candidate_locations}. Please answer the questions only use the provided candidate actions, objects, and locations. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(' '.join([self.choice_candidate['action'][inst.output["text"].split()[0]], self.choice_candidate['action'][inst.output["text"].split()[1]], self.choice_candidate['action'][inst.output["text"].split()[2]]]))
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
def post_process_intent_output():
data_path = '/m2v_intern/wushengqiong/model/audio-test/predictions/understanding/IntentClassification_250102204424.json'
intent_label = read_json('/m2v_intern/wushengqiong/model/audio-test/understanding/IntentClassification/annotation.json')['intent_label']
action = intent_label['action']
object = intent_label['object']
location = intent_label['location']
data = read_json(data_path)
results = []
for d in data:
results.append({
'gt': [action[d['gt'].split()[0]], object[d['gt'].split()[1]], location[d['gt'].split()[2]]],
'response': d['response'],
'audio_path': d['audio_path'],
})
json.dump(results, open('/m2v_intern/wushengqiong/model/audio-test/predictions/understanding/IntentClassification_250102204424_1.json', 'w'))
class MusicGenreClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"].replace('\\', '/'))
question = inst.input["prompt"]
prompt = f"Please listen to the input music and then determine the genre of the music. The candidate genres are {self.choice_candidate}. Please output **only one genre** from the provided candidate genres, and **DO NOT** output any other words.\nQuestions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class MusicInstrumentClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
# candidate_instruments = ','.join([k for k in self.choice_candidate.keys() if not k[0].isdigit()])
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the music and then detect the instrument of the music. The candidate instruments are {self.choice_candidate}. Please output **only the most appropriate music instrument** from the provided candidate music instruments, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class MusicInstrumentSourceAnalysis(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the music and then detect the instrucment source of the music. The candidate sources are {self.choice_candidate}. Please output **only the most appropriate music source** from the provided candidate music sources, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"].strip().lower())
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class MusicPitchAnalysis(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"])
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the music and then detect the pitch score of the music. The 0-based MIDI pitch is in the range [0, 127]. Please output **only the most appropriate pitch score in a number** from the provided range, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"].strip().lower())
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class NoteQualitiesAnalysis(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(','.join(item['output']["text"]).strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the music and then detect the note quality of the given music. The candidate annotation is {self.choice_candidate}. Please output **the qualities which are present in this note** from the provided candidate music note quality candidate categories, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(','.join(inst.output["text"]))
audio_paths.append(inst.input["audio_file"].strip().lower())
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class OpenAQA(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then answer the question. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = bleu_evaluation(self.predictions, self.references)
return {"accuracy": acc}
class SoundEventClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the music and then detect the happening event of the given audio. The candidate annotation is {self.choice_candidate}. Please output **only one event** from the provided candidate events,, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"])
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class SpeechCommand(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"].replace('\\', '/'))
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then detect the speech command of the given audio. The candidate annotation is {self.choice_candidate}. Please output **only one command** from the provided candidate commands, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class SpeechEmotionRecognition(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then detect the emotion of the given audio. The candidate annotation is {self.choice_candidate}. Please output **only one emotion** from the provided candidate emotions, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class VocalSoundClassification(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"])
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then detect the vocal sound category of the given audio. The candidate annotation is {self.choice_candidate}. Please output **only one vocal sound category** from the provided candidate vocal sounds, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
class VocalTechniqueDetection(BaseTask):
def _parse_data(self, task_data: Dict[str, Any]) -> List[Instance]:
return [Instance(input=d["input"], output=d["output"], id=d["id"])
for d in task_data["data"]]
def _get_choice_candidate(self, data: Dict) -> Dict:
choices = []
for item in data['data']:
choices.append(item['output']["text"].strip().lower())
choices = list(set(choices))
return choices
def run_inference(self):
audio_paths = []
for inst in tqdm.tqdm(self.data):
audio_path = os.path.join(self.audio_dir, inst.input["audio_file"].replace('\\', '/'))
question = inst.input["prompt"]
prompt = f"Please listen to the audio and then detect the vocal technique of the given audio. The candidate annotations are scales, arpeggios, long tones, and excerpts. Please output **only one vocal technique** from the provided candidate vocal techniques, and **DO NOT** output any other words. Questions: {question}\nAnswer:"
try:
response = self.model.generate(prompt, audio_path=audio_path)
except Exception as e:
print("Error audio: {}".format(inst.input["audio_file"]))
response = "None"
continue
self.predictions.append(response)
self.references.append(inst.output["text"].strip().lower())
audio_paths.append(inst.input["audio_file"])
self.save_predictions(audio_paths)
def evaluate(self) -> Dict[str, float]:
acc = exact_match_accuracy(self.predictions, self.references)
return {"accuracy": acc}
def log_performance_csv(model_name, task_name, metric, score, root_path, output_file='prediction.json'):
import csv
file_exists = os.path.isfile(os.path.join(root_path, output_file))
row_data = {
'model': model_name,
'task': task_name,
'metric': metric,
'score': str(score),
}
with open(os.path.join(root_path, output_file), mode='a', newline='', encoding='utf-8') as f:
writer = csv.DictWriter(f, fieldnames=row_data.keys())
if not file_exists:
writer.writeheader()
writer.writerow(row_data)
def log_performance_json(model_name, task_name, metric, score, root_path, output_file='prediction.json'):
import json
log_data = {
'model': model_name,
'task': task_name,
'metric': metric,
'score': str(score),
}
log_file_path = os.path.join(root_path, output_file)
if os.path.exists(log_file_path):
with open(log_file_path, 'r') as f:
existing_data = json.load(f)
else:
existing_data = []
existing_data.append(log_data)
with open(log_file_path, 'w', encoding='utf-8') as f:
json.dump(existing_data, f, indent=4)
def log_performance_detail(model_name, task_name, metrics, root_path, output_file='performance_log.csv'):
import csv
file_path = os.path.join(root_path, output_file)
file_exists = os.path.isfile(file_path)
# Retrieve the main indicator values from the metrics dictionary
metric_value = None
if isinstance(metrics, dict):
# Select metrics based on priority
for key in ['accuracy', 'f1', 'micro_f1', 'bleu4', 'rougeL', 'code_bleu', 'MAE']:
if key in metrics:
metric_value = metrics[key]
break
if metric_value is None and len(metrics) > 0:
# If no priority metric is found, use the first metric
metric_value = list(metrics.values())[0]
else:
metric_value = metrics
# Simplify the file name, keeping only the last part
model_name = model_name.split('/')[-1]
if file_exists:
# Read existing data
rows = []
tasks = set()
with open(file_path, 'r', newline='', encoding='utf-8') as f:
reader = csv.reader(f)
header = next(reader, ['task', model_name]) # If the file is empty, use the default header
if len(header) == 1: # If there is only the task column, add the model column
header.append(model_name)
rows.append(header)
# Read existing data and update
for row in reader:
if row[0] == task_name: # If the same task is found, update the value
row = [task_name, str(metric_value)]
tasks.add(row[0])
rows.append(row)
# If it is a new task, add a new row
if task_name not in tasks:
rows.append([task_name, str(metric_value)])
else:
# Create a new file
rows = [
['task', model_name],
[task_name, str(metric_value)]
]
# Write all data
with open(file_path, 'w', newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerows(rows)
if __name__ == "__main__":
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(description="Run audio understanding tasks")
parser.add_argument('-m', '--model_name', type=str, required=True, help='Name of the audio understanding model to use')
parser.add_argument('-d', '--data_dir', type=str, default='./audio/understanding/', help='Directory containing task data')
parser.add_argument('-o', '--output_dir', type=str, default='./audio/predictions/understanding/', help='Directory to save predictions')
parser.add_argument('-r', '--root_path', type=str, default='./', help='Root path for logging performance')
parser.add_argument('-t', '--task_names', type=str, nargs='+',
help='List of task names to run (default: AccentClassification AccentSexClassification AcousticSceneClassification)')
args = parser.parse_args()
# model_name = 'Qwen2-Audio-7B-Instruct'
# data_dir = './understanding/'
# output_dir = f'./predictions/understanding/{model_name}'
# root_path = './'
model = AudioComprehensionModel(model_name=args.model_name)
task_name_list = [
'AccentClassification', 'AccentSexClassification', 'AcousticSceneClassification',
'AnimalSoundClassification', 'AudioCaptioning', 'AudioCaptioningClotho',
'AudioQA', 'BirdSoundDetection', 'EnvironmentSoundRecognition',
'IntentClassification', 'MusicGenreClassification',
'MusicInstrumentClassification', 'MusicInstrumentSourceAnalysis',
'MusicPitchAnalysis', 'NoteQualitiesAnalysis', 'OpenAQA',
'SingerIdentification', 'SoundEventClassification',
'SpeakerIdentification', 'SpeechCommand',
'SpeechEmotionRecognition', 'VocalSoundClassification',
'VocalTechniqueDetection'
]
if args.task_names is None or len(args.task_names) == 0:
args.task_names = task_name_list
for task_name in args.task_names: # os.listdir(data_dir):
# Dynamically get the class by its name
if task_name in globals(): # Ensure the class is defined in the current scope
task_class = globals()[task_name]
else:
# Optionally, handle cases where the class is not found
print(f"Task {task_name} is not defined in the current scope.")
continue
# Initialize the task class
import glob
json_file_list = glob.glob(os.path.join(args.data_dir, task_name, "*.json"))
if len(json_file_list) == 0:
print(f"No JSON files found for task: {task_name}")
continue
elif len(json_file_list) > 1:
print(f"Multiple JSON files found for task: {task_name}, using the first one: {json_file_list[0]}")
task_annotation_data = json_file_list[0]
else:
task_annotation_data = json_file_list[0]
task = task_class(
task_data=task_annotation_data,
model=model,
audio_dir=os.path.join(args.data_dir, task_name, 'audios'),
output_dir=args.output_dir
)
# Run inference for the task
# This should generate audio files based on the task's data
print(f"Running inference for task: {task_name}")
task.run_inference()
# if you want to save the predictions, you need to rewrite the save_predictions() in each Task class depending on your need, and call task.save_predictions() after task.run_inference() or inside the run_inference method.
# Evaluate the task, return a dictionary of metrics
# For example, {'FAD_score': 0.123}
eval_results = task.evaluate()
print("Task name: ", task_name, "Evaluation results:", eval_results)
log_performance_json(
model_name=args.model_name,
task_name=task_name,
metric=list(eval_results.keys())[0].split('_')[0], # CLAP_score
score=eval_results[list(eval_results.keys())[0]], # e.g., 0.123
root_path=args.data_dir)
# or you can run the tasks one by one like below:
# task_name = 'AcousticSceneClassification'
# task = AcousticSceneClassification(
# task_data=os.path.join(data_dir, f"{task_name}/annotation.json"),
# model=model,
# audio_dir=os.path.join(data_dir, f"{task_name}/audios"),
# output_dir=output_dir)
# task.run_inference()
# print(task.evaluate())
|