---
license: apache-2.0
language:
- de
library_name: transformers
pipeline_tag: automatic-speech-recognition
model-index:
- name: whisper-large-v3-german by Florian Zimmermeister @primeLine
  results:
  - task:
      type: automatic-speech-recognition
      name: Speech Recognition
    dataset:
      name: Common Voice de
      type: common_voice_15
      args: de
    metrics:
    - type: wer
      value: 3.002 %
      name: Test WER
    - type: cer
      value: 0.81 %
      name: Test CER
new_version: primeline/whisper-large-v3-turbo-german
---


### Summary
This model map provides information about a model based on Whisper Large v3 that has been fine-tuned for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI. This model has been specially optimized for processing and recognizing German speech.



### Applications
This model can be used in various application areas, including

- Transcription of spoken German language
- Voice commands and voice control
- Automatic subtitling for German videos
- Voice-based search queries in German
- Dictation functions in word processing programs


## Model family

| Model                            | Parameters | link                                                         |
|----------------------------------|------------|--------------------------------------------------------------|
| Whisper large v3 german          | 1.54B      | [link](https://huggingface.co/primeline/whisper-large-v3-german) |
| Whisper large v3 turbo german    | 809M       | [link](https://huggingface.co/primeline/whisper-large-v3-turbo-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 data
The training data for this model includes a large amount of spoken German from various sources. The data was carefully selected and processed to optimize recognition performance.


### Training process
The training of the model was performed with the following hyperparameters

- Batch size: 1024
- Epochs: 2
- Learning rate: 1e-5
- Data augmentation: No


### 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/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/)

[![primeline AI](https://primeline-ai.com/wp-content/uploads/2024/02/pl_ai_bildwortmarke_original.svg)](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)