End of training

README.md

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+---
+license: bsd-3-clause
+base_model: MIT/ast-finetuned-audioset-10-10-0.4593
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- wer
+model-index:
+- name: ast-finetuned-audioset-10-10-0.4593-keyword_spotting2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ast-finetuned-audioset-10-10-0.4593-keyword_spotting2
+
+This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0123
+- Accuracy: 0.8228
+- Wer: 0.1772
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.0008        | 1.0   | 28   | 1.3229          | 0.8228   | 0.1772 |
+| 0.3356        | 2.0   | 56   | 1.3607          | 0.8228   | 0.1772 |
+| 0.0035        | 3.0   | 84   | 1.0123          | 0.8228   | 0.1772 |
+| 0.0051        | 4.0   | 112  | 1.5980          | 0.8228   | 0.1772 |
+| 0.0001        | 5.0   | 140  | 1.4630          | 0.8228   | 0.1772 |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.14.7.dev0
+- Tokenizers 0.14.1

config.json

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+{
+  "_name_or_path": "MIT/ast-finetuned-audioset-10-10-0.4593",
+  "architectures": [
+    "ASTForAudioClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "frequency_stride": 10,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "abr",
+    "1": "fech"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "abr": "0",
+    "fech": "1"
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 1024,
+  "model_type": "audio-spectrogram-transformer",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_mel_bins": 128,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "time_stride": 10,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e8e66cbc83c3782f13b5cec172f809c164f5ad0afaefc6746e47b5bb0d305dcb
+size 344790040

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_extractor_type": "ASTFeatureExtractor",
+  "feature_size": 1,
+  "max_length": 1024,
+  "mean": -4.2677393,
+  "num_mel_bins": 128,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": false,
+  "sampling_rate": 16000,
+  "std": 4.5689974
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98d2f2513002ce3539e391d18917f84c5efac0b77992915a371591dea3c7271d
+size 4664