Automatic Speech Recognition
Transformers
PyTorch
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("steja/whisper-small-sindhi")
model = AutoModelForSpeechSeq2Seq.from_pretrained("steja/whisper-small-sindhi")Quick Links
Whisper small Sindhi
This model is a fine-tuned version of openai/whisper-small on the google/fleurs sd_in dataset. It achieves the following results on the evaluation set:
- Loss: 0.8761
- Wer: 39.3604
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: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0125 | 30.74 | 400 | 0.7639 | 43.5485 |
| 0.0007 | 61.52 | 800 | 0.8301 | 39.4873 |
| 0.0003 | 92.3 | 1200 | 0.8761 | 39.3604 |
| 0.0002 | 123.07 | 1600 | 0.8949 | 39.3604 |
| 0.0002 | 153.81 | 2000 | 0.9013 | 39.4196 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 4
Dataset used to train steja/whisper-small-sindhi
Space using steja/whisper-small-sindhi 1
Evaluation results
- Wer on google/fleurstest set self-reported39.360
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="steja/whisper-small-sindhi")