Automatic Speech Recognition
Transformers
PyTorch
Chinese
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("thomas0104/large_v2_nan_tw_so_short_30s")
model = AutoModelForMultimodalLM.from_pretrained("thomas0104/large_v2_nan_tw_so_short_30s")Quick Links
openai/large_v2_nan_tw_so_short_30s
This model is a fine-tuned version of openai/whisper-large-v2 on the thomas0104/nan_tw_soap_opera nan-tw dataset. It achieves the following results on the evaluation set:
- Loss: 1.3322
- Wer: 343.5629
- Cer: 63.42
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.1133 | 1.0 | 1000 | 1.3322 | 343.5629 | 416.4573 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
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Model tree for thomas0104/large_v2_nan_tw_so_short_30s
Base model
openai/whisper-large-v2Dataset used to train thomas0104/large_v2_nan_tw_so_short_30s
Evaluation results
- Cer on thomas0104/nan_tw_soap_opera nan-twtest set self-reported63.420
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="thomas0104/large_v2_nan_tw_so_short_30s")