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---
license: apache-2.0
language:
- de
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
# distil-whisper-german
This model is a German Speech Recognition model based on the [distil-whisper](https://github.com/huggingface/distil-whisper) technique.
The model weights count 756M parameters and with a size of 1.51GB in bfloat16 format.
As a follow-up to the [Whisper large v3 german](https://huggingface.co/primeline/whisper-large-v3-german) we decided to create a distilled version for a faster inference with minimal quality loss.
## Intended uses & limitations
The model is intended to be used for German speech recognition tasks.
It can be used as local transkription service or as a part of a larger pipeline for speech recognition tasks.
While counting only half of the parameters of the large model, the quality is still very good and can be used for most tasks.
The latency is low enough to be used in real-time applications when using optimization toolkits like tensorrt.
## Dataset
The dataset used for training is a filtered subset of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, multilingual librispeech and some internal data.
The data was filtered and double checked for quality and correctness.
We did some normalization to the text data, especially for casing and punctuation.
## Model family
| Model | Parameters | link |
|----------------------------------|------------|--------------------------------------------------------------|
| Whisper large v3 german | 1.54B | [link](https://huggingface.co/primeline/whisper-large-v3-german) |
| Distil-whisper large v3 german | 756M | [link](https://huggingface.co/primeline/distil-whisper-large-v3-german) |
| tiny whisper | 37.8M | [link](https://huggingface.co/primeline/whisper-tiny-german) |
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- total_train_batch_size: 512
- num_epochs: 5.0
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.18.0
- Tokenizers 0.15.2
### How to use
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "primeline/distil-whisper-large-v3-german"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
## [About us](https://primeline-ai.com/en/)
[](https://primeline-ai.com/en/)
Your partner for AI infrastructure in Germany <br>
Experience the powerful AI infrastructure that drives your ambitions in Deep Learning, Machine Learning & High-Performance Computing. Optimized for AI training and inference.
Model author: [Florian Zimmermeister](https://huggingface.co/flozi00) |