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---
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/)
[](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) |