Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Shamik/whisper-tiny-polyAI-minds14")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Shamik/whisper-tiny-polyAI-minds14")Quick Links
Whisper Tiny finetuned on PolyAI Minds14 English US
This model is a fine-tuned version of openai/whisper-tiny on the Speech Transcription in English from e-banking domain. dataset. It achieves the following results on the evaluation set:
- Loss: 0.8668
- Wer Ortho: 0.4009
- Wer: 0.3823
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: 16
- eval_batch_size: 64
- 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: 400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.3501 | 3.57 | 100 | 0.7134 | 0.4568 | 0.4212 |
| 0.044 | 7.14 | 200 | 0.7639 | 0.4096 | 0.3746 |
| 0.0048 | 10.71 | 300 | 0.8265 | 0.4109 | 0.3854 |
| 0.0021 | 14.29 | 400 | 0.8668 | 0.4009 | 0.3823 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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Model tree for Shamik/whisper-tiny-polyAI-minds14
Base model
openai/whisper-tinyDataset used to train Shamik/whisper-tiny-polyAI-minds14
Evaluation results
- Wer on Speech Transcription in English from e-banking domain.self-reported0.382
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shamik/whisper-tiny-polyAI-minds14")