DOLMA ASR Models
Collection
Models trained on very low-resource Middle Eastern languages • 9 items • Updated
Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("razhan/whisper-base-zza")
model = AutoModelForSpeechSeq2Seq.from_pretrained("razhan/whisper-base-zza")Quick Links
whisper-base-zza
This model is a fine-tuned version of openai/whisper-base on the razhan/DOLMA-speech zazaki dataset. It achieves the following results on the evaluation set:
- Loss: 4.0956
- Wer: 1.2574
- Cer: 1.0132
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: 192
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 384
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| No log | 1.0 | 1 | 4.7831 | 1.2622 | 1.0146 |
| No log | 2.0 | 2 | 4.7831 | 1.2622 | 1.0146 |
| No log | 3.0 | 3 | 4.7831 | 1.2622 | 1.0146 |
| No log | 4.0 | 4 | 4.7831 | 1.2622 | 1.0146 |
| No log | 5.0 | 5 | 4.0956 | 1.2574 | 1.0132 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for razhan/whisper-base-zza
Base model
openai/whisper-baseDataset used to train razhan/whisper-base-zza
Collection including razhan/whisper-base-zza
Evaluation results
- Wer on razhan/DOLMA-speech zazakiself-reported1.257
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="razhan/whisper-base-zza")