whisper-large-v3-turbo-turkish/README.md

library_name, language, license, base_model, tags, datasets, metrics, model-index

library_name	language	license	base_model	tags	datasets	metrics	model-index
transformers	tr	mit	openai/whisper-large-v3-turbo	generated_from_trainer	mozilla-foundation/common_voice_17_0	wer	name results Whisper Large v3 Turbo TR - Selim Çavaş task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args Common Voice 17.0 mozilla-foundation/common_voice_17_0 tr test config: tr, split: test name type value Wer wer 18.92291759135967

Whisper Large v3 Turbo TR - Selim Çavaş

This model is a fine-tuned version of 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

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