Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("sgangireddy/whisper-largev2-mls-german")
model = AutoModelForSpeechSeq2Seq.from_pretrained("sgangireddy/whisper-largev2-mls-german")Quick Links
Whisper largeV2 German MLS
This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/multilingual_librispeech german dataset. It achieves the following results on the evaluation set:
- Loss: 0.1370
- Wer: 6.0483
Model description
The model is fine-tuned for 4000 updates/steps on multilingual librispeech German train data.
- Zero-shot - 5.5 (MLS German test)
- Fine-tune MLS German train - 6.04 (MLS German test)
Even after fine-tuning the model is doing slightly worse than the zero-shot.
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: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1755 | 0.25 | 1000 | 0.1844 | 7.7118 |
| 0.1185 | 0.5 | 2000 | 0.1636 | 7.0659 |
| 0.1081 | 0.75 | 3000 | 0.1396 | 6.0844 |
| 0.1222 | 1.0 | 4000 | 0.1370 | 6.0483 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Dataset used to train sgangireddy/whisper-largev2-mls-german
Evaluation results
- Wer on facebook/multilingual_librispeech germantest set self-reported6.048
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sgangireddy/whisper-largev2-mls-german")