End of training

README.md

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+---
+base_model: Vignesh-M/Indic-whisper
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: whisper-indic-audio-abuse-feature
+  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. -->
+
+# whisper-indic-audio-abuse-feature
+
+This model is a fine-tuned version of [Vignesh-M/Indic-whisper](https://huggingface.co/Vignesh-M/Indic-whisper) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5056
+- Accuracy: 0.8868
+- Macro Precision: 0.8642
+- Macro Recall: 0.8509
+- Macro F1-score: 0.8572
+
+## 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: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.01
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1-score |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|
+| 0.4635        | 0.4367 | 50   | 0.4010          | 0.8020   | 0.8176          | 0.8096       | 0.8014         |
+| 0.3403        | 0.8734 | 100  | 0.3162          | 0.8684   | 0.8685          | 0.8668       | 0.8675         |
+| 0.2689        | 1.3100 | 150  | 0.3025          | 0.8807   | 0.8838          | 0.8774       | 0.8793         |
+| 0.2339        | 1.7467 | 200  | 0.3019          | 0.8782   | 0.8776          | 0.8777       | 0.8776         |
+| 0.1723        | 2.1834 | 250  | 0.3715          | 0.8868   | 0.8870          | 0.8854       | 0.8861         |
+| 0.1027        | 2.6201 | 300  | 0.3472          | 0.8930   | 0.8937          | 0.8912       | 0.8921         |
+| 0.123         | 3.0568 | 350  | 0.3690          | 0.8795   | 0.8855          | 0.8751       | 0.8776         |
+| 0.0497        | 3.4934 | 400  | 0.4423          | 0.8918   | 0.8916          | 0.8907       | 0.8911         |
+| 0.0534        | 3.9301 | 450  | 0.3937          | 0.9041   | 0.9048          | 0.9024       | 0.9033         |
+| 0.0235        | 4.3668 | 500  | 0.4753          | 0.8979   | 0.8993          | 0.8958       | 0.8970         |
+| 0.0196        | 4.8035 | 550  | 0.5204          | 0.8967   | 0.8982          | 0.8944       | 0.8957         |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2
+- Datasets 2.19.2
+- Tokenizers 0.19.1

model.safetensors

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+oid sha256:a9a561efc6ca1600538147382a34b73d757782811a7113a2ee9a43fd676cdad6
+size 1229957888

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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+size 5112