初始化项目，由ModelHub XC社区提供模型

Model: selimc/whisper-large-v3-turbo-turkish Source: Original Platform
2026-05-14 02:27:35 +08:00
commit b00d6d2293
15 changed files with 232074 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,117 @@
+---
+library_name: transformers
+language:
+- tr
+license: mit
+base_model: openai/whisper-large-v3-turbo
+tags:
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_17_0
+metrics:
+- wer
+model-index:
+- name: "Whisper Large v3 Turbo TR - Selim \xC7ava\u015F"
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 17.0
+      type: mozilla-foundation/common_voice_17_0
+      config: tr
+      split: test
+      args: 'config: tr, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 18.92291759135967
+---
+
+<!-- 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 v3 Turbo TR - Selim Çavaş
+
+This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3123
+- Wer: 18.9229
+
+## Intended uses & limitations
+
+This model can be used in various application areas, including
+
+  - Transcription of Turkish language
+  - Voice commands
+  - Automatic subtitling for Turkish videos
+    
+## How To Use
+
+```python
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "selimc/whisper-large-v3-turbo-turkish"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+result = pipe("test.mp3")
+print(result["text"])
+```
+
+## Training
+
+Due to colab GPU constraints I was able to train using only the 25% of the Turkish data available in the Common Voice 17.0 dataset. 😔
+
+Got a GPU to spare? Let's collaborate and take this model to the next level! 🚀
+
+### 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: 4000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer     |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|
+| 0.1223        | 1.6   | 1000 | 0.3187          | 24.4415 |
+| 0.0501        | 3.2   | 2000 | 0.3123          | 20.9720 |
+| 0.0226        | 4.8   | 3000 | 0.3010          | 19.6183 |
+| 0.001         | 6.4   | 4000 | 0.3123          | 18.9229 |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.1