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Model: ozguntosun/whisper-large-v3-tr
Source: Original Platform

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README.md

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+---
+language:
+- tr
+license: apache-2.0
+base_model: openai/whisper-large-v3
+tags:
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: "Whisper Large TR - \xD6zg\xFCn Tosun"
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: tr
+      split: None
+      args: 'config: tr, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 11.727918051936383
+---
+
+<!-- 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 Large TR - Özgün Tosun
+
+This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1323
+- Wer: 11.7279
+
+## 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: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 5000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Wer     |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 0.1372        | 0.3652 | 1000 | 0.1810          | 16.0805 |
+| 0.1103        | 0.7305 | 2000 | 0.1628          | 14.5458 |
+| 0.0563        | 1.0957 | 3000 | 0.1513          | 12.9302 |
+| 0.0657        | 1.4609 | 4000 | 0.1383          | 12.4198 |
+| 0.0444        | 1.8262 | 5000 | 0.1323          | 11.7279 |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.2+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "openai/whisper-large-v3",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
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+  "classifier_proj_size": 256,
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+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 32,
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+  "dropout": 0.0,
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+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}

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