diff --git a/llm/finetune/audio_spectrogram_transformer/README.md b/llm/finetune/audio_spectrogram_transformer/README.md
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+# audio_spectrogram_transformer 模型微调报告
+
+## 任务
+- 模型：MIT/ast-finetuned-audioset-10-10-0.4593
+- 数据集: ashraq/esc50
+
+## 结果对比：
+
+**Mindnlp+D910B**
+
+| Epoch | Training Loss | Eval Loss | Accuracy |
+|------:|-------------:|----------------:|----------:|
+| 1 | 3.0928 | 2.2305 | 0.8150 |
+| 2 | 1.4845 | 0.9815 | 0.8950 |
+| 3 | 0.5733 | 0.4876 | 0.9250 |
+| 4 | 0.2061 | 0.3770 | 0.9125 |
+| 5 | 0.0742 | 0.3207 | 0.9300 |
+| 6 | 0.0443 | 0.1821 | **0.9600**|
+| 7 | 0.0178 | 0.2144 | 0.9575 |
+| 8 | 0.0111 | 0.2155 | 0.9575 |
+| 9 | 0.0094 | 0.2167 | 0.9575 |
+| 10 | 0.0087 | 0.2174 | 0.9575 |
+
+---
+
+**Pytorch+3090**
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|------:|-------------:|----------------:|---------:|
+| 1 | 1.2550 | 0.3934 | 0.8825 |
+| 2 | 0.4477 | 0.3656 | 0.8925 |
+| 3 | 0.3289 | 0.2777 | 0.9200 |
+| 4 | 0.2200 | 0.3645 | 0.9175 |
+| 5 | 0.1679 | 0.2345 | 0.9350 |
+| 6 | 0.1140 | 0.1877 | 0.9575 |
+| 7 | 0.0925 | 0.1641 | 0.9575 |
+| 8 | 0.0648 | 0.1810 | 0.9475 |
+| 9 | 0.0593 | 0.1285 | 0.9550 |
+| 10 | 0.0269 | 0.1222 | **0.9575** |
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diff --git a/llm/finetune/audio_spectrogram_transformer/finetune.py b/llm/finetune/audio_spectrogram_transformer/finetune.py
new file mode 100644
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+++ b/llm/finetune/audio_spectrogram_transformer/finetune.py
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+import mindspore as ms
+import numpy as np
+from datasets import Audio, ClassLabel, load_dataset
+from mindspore.dataset import GeneratorDataset
+from sklearn.metrics import accuracy_score
+from mindnlp.engine import Trainer, TrainingArguments
+from mindnlp.transformers import (ASTConfig, ASTFeatureExtractor,
+ ASTForAudioClassification)
+
+ms.set_context(mode=ms.PYNATIVE_MODE, device_target="Ascend")
+
+# 加载esc50数据集
+esc50 = load_dataset("ashraq/esc50", split="train")
+
+df = esc50.select_columns(["target", "category"]).to_pandas()
+class_names = df.iloc[np.unique(df["target"], return_index=True)[
+ 1]]["category"].to_list()
+
+esc50 = esc50.cast_column("target", ClassLabel(names=class_names))
+esc50 = esc50.cast_column("audio", Audio(sampling_rate=16000))
+esc50 = esc50.rename_column("target", "labels")
+num_labels = len(np.unique(esc50["labels"]))
+
+# 初始化AST
+pretrained_model = "MIT/ast-finetuned-audioset-10-10-0.4593"
+feature_extractor = ASTFeatureExtractor.from_pretrained(pretrained_model)
+model_input_name = feature_extractor.model_input_names[0]
+SAMPLING_RATE = feature_extractor.sampling_rate
+
+
+# 预处理音频
+def preprocess_audio(batch):
+ wavs = [audio["array"] for audio in batch["input_values"]]
+ inputs = feature_extractor(
+ wavs, sampling_rate=SAMPLING_RATE, return_tensors="ms")
+ return {model_input_name: inputs.get(model_input_name), "labels": list(batch["labels"])}
+
+
+dataset = esc50
+label2id = dataset.features["labels"]._str2int
+
+# 构造训练集和测试集
+if "test" not in dataset:
+ dataset = dataset.train_test_split(
+ test_size=0.2, shuffle=True, seed=0, stratify_by_column="labels")
+
+
+dataset = dataset.cast_column("audio", Audio(
+ sampling_rate=feature_extractor.sampling_rate))
+dataset = dataset.rename_column("audio", "input_values")
+
+dataset["train"].set_transform(
+ preprocess_audio, output_all_columns=False)
+dataset["test"].set_transform(preprocess_audio, output_all_columns=False)
+
+# 加载config
+config = ASTConfig.from_pretrained(pretrained_model)
+config.num_labels = num_labels
+config.label2id = label2id
+config.id2label = {v: k for k, v in label2id.items()}
+
+model = ASTForAudioClassification.from_pretrained(
+ pretrained_model, config=config, ignore_mismatched_sizes=True)
+
+
+def convert_mindspore_datatset(hf_dataset, batch_size):
+ data_list = list(hf_dataset)
+
+ def generator():
+ for item in data_list:
+ yield item[model_input_name], item["labels"]
+ # 构造MindSpore的GeneratorDataset
+ ds = GeneratorDataset(
+ source=generator,
+ column_names=[model_input_name, "labels"],
+ shuffle=False
+ )
+ ds = ds.batch(batch_size, drop_remainder=True)
+ return ds
+
+
+# 初始化训练参数
+training_args = TrainingArguments(
+ output_dir="./checkpoint",
+ logging_dir="./logs",
+ learning_rate=5e-5,
+ num_train_epochs=10,
+ per_device_train_batch_size=8,
+ evaluation_strategy="epoch",
+ save_strategy="epoch",
+ eval_steps=1,
+ save_steps=1,
+ load_best_model_at_end=True,
+ metric_for_best_model="accuracy",
+ logging_strategy="epoch",
+ logging_steps=20,
+)
+
+train_ms_dataset = convert_mindspore_datatset(
+ dataset["train"], training_args.per_device_train_batch_size)
+eval_ms_dataset = convert_mindspore_datatset(
+ dataset["test"], training_args.per_device_train_batch_size)
+
+
+def compute_metrics(eval_pred):
+ logits = eval_pred.predictions
+ labels = eval_pred.label_ids
+ predictions = np.argmax(logits, axis=1)
+ return {"accuracy": accuracy_score(predictions, labels)}
+
+
+# 初始化trainer
+trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=train_ms_dataset,
+ eval_dataset=eval_ms_dataset,
+ compute_metrics=compute_metrics,
+)
+
+trainer.train()