End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+datasets:
+- balbus-classifier
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: miosipof/whisper-small-ft-balbus-sep28k-v1.9
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: Apple dataset
+      type: balbus-classifier
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6554730384242816
+    - name: Precision
+      type: precision
+      value: 0.6662764347644506
+    - name: Recall
+      type: recall
+      value: 0.6536533800437693
+    - name: F1
+      type: f1
+      value: 0.6584516062723008
+---
+
+<!-- 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. -->
+
+# miosipof/whisper-small-ft-balbus-sep28k-v1.9
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Apple dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4792
+- Accuracy: 0.6555
+- Precision: 0.6663
+- Recall: 0.6537
+- F1: 0.6585
+
+## 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: 4e-05
+- train_batch_size: 64
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.5
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.8584        | 1.0   | 436  | 0.7060          | 0.6810   | 0.6897    | 0.6911 | 0.6834 |
+| 0.6584        | 2.0   | 872  | 0.6886          | 0.6878   | 0.6994    | 0.6816 | 0.6849 |
+| 0.5415        | 3.0   | 1308 | 0.7227          | 0.6742   | 0.6934    | 0.6708 | 0.6779 |
+| 0.3032        | 4.0   | 1744 | 0.9611          | 0.6522   | 0.6647    | 0.6513 | 0.6564 |
+| 0.0734        | 5.0   | 2180 | 1.4792          | 0.6555   | 0.6663    | 0.6537 | 0.6585 |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

config.json

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+{
+  "_name_or_path": "openai/whisper-small",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForAudioClassification"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
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+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
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+  "dropout": 0.0,
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+  "init_std": 0.02,
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+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
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+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}

model.safetensors

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preprocessor_config.json

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+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

training_args.bin

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