# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Imxxn/AudioCourseU5-ASR")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Imxxn/AudioCourseU5-ASR")Quick Links
AudioCourseU5-ASR
This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6438
- Wer Ortho: 34.4849
- Wer: 0.3406
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: 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: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.3065 | 3.57 | 100 | 0.4921 | 36.8908 | 0.3577 |
| 0.0391 | 7.14 | 200 | 0.5425 | 35.3486 | 0.3436 |
| 0.0042 | 10.71 | 300 | 0.5878 | 35.6570 | 0.3495 |
| 0.0012 | 14.29 | 400 | 0.6206 | 34.2998 | 0.3377 |
| 0.0007 | 17.86 | 500 | 0.6438 | 34.4849 | 0.3406 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for Imxxn/AudioCourseU5-ASR
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Imxxn/AudioCourseU5-ASR")