Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
Generated from Trainer
whisper-event
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("luigisaetta/whisper-tiny-it")
model = AutoModelForSpeechSeq2Seq.from_pretrained("luigisaetta/whisper-tiny-it")Quick Links
whisper-tiny-it
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11 dataset.
Language: Italian
It achieves the following results on the evaluation set:
- Loss: 0.3958
- Wer: 26.61
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: 64
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3997 | 1.68 | 1000 | 0.4651 | 0.3038 |
| 0.3026 | 3.35 | 2000 | 0.4086 | 0.2743 |
| 0.2874 | 5.03 | 3000 | 0.3958 | 0.2661 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
- Downloads last month
- 6
Evaluation results
- Wer on mozilla-foundation/common_voice_11_0 ittest set self-reported26.610
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="luigisaetta/whisper-tiny-it")