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---
license: apache-2.0
tags:
- generated_from_trainer
- dutch
- whisper-event
metrics:
- wer
base_model: qmeeus/whisper-small-nl
model-index:
- name: whisper-small-nl
results: []
---
# whisper-small-nl
This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3034
- Wer: 14.5354
## Model description
More information needed
## Intended uses & limitations
Transcribe files in Dutch:
```python
import soundfile as sf
from transformers import pipeline
whisper_asr = pipeline("automatic-speech-recognition", model="qmeeus/whisper-small-nl", device=0)
whisper_asr.model.config.forced_decoder_ids = whisper_asr.tokenizer.get_decoder_prompt_ids(
task="transcribe", language="nl"
)
waveform, sr = sf.read(filename)
def iter_chunks(waveform, sampling_rate=16_000, chunk_length=30.):
assert sampling_rate == 16_000
n_frames = math.floor(sampling_rate * chunk_length)
for start in range(0, len(waveform), n_frames):
end = min(len(waveform), start + n_frames)
yield waveform[start:end]
for sentence in whisper_asr(iter_chunks(waveform, sr), max_new_tokens=448):
print(sentence["text"])
```
## 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: 16
- total_train_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: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2045 | 2.49 | 1000 | 0.3194 | 16.1628 |
| 0.0652 | 4.97 | 2000 | 0.3425 | 16.3672 |
| 0.0167 | 7.46 | 3000 | 0.3915 | 15.8187 |
| 0.0064 | 9.95 | 4000 | 0.4190 | 15.7298 |
| 0.1966 | 2.02 | 5000 | 0.3298 | 15.0881 |
| 0.1912 | 4.04 | 6000 | 0.3266 | 14.8764 |
| 0.1008 | 7.02 | 7000 | 0.3261 | 14.8086 |
| 0.0899 | 9.04 | 8000 | 0.3196 | 14.6487 |
| 0.1126 | 12.02 | 9000 | 0.3283 | 14.5894 |
| 0.1071 | 14.04 | 10000 | 0.3034 | 14.5354 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|