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Dhivehi Voice AI Collection: Tools for Thaana speech recognition (ASR), text-to-speech (TTS), and audio processing • 32 items • Updated • 4
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("alakxender/whisper-large-v3-dv-syn-md")
model = AutoModelForMultimodalLM.from_pretrained("alakxender/whisper-large-v3-dv-syn-md")Quick Links
Whisper Large Dv Syn - Alakxender
This model is a fine-tuned version of openai/whisper-large-v3 on the dv_voice dataset.
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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for alakxender/whisper-large-v3-dv-syn-md
Base model
openai/whisper-large-v3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-large-v3-dv-syn-md")