---
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