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---
license: apache-2.0
language:
- de
library_name: transformers
pipeline_tag: automatic-speech-recognition
model-index:
- name: whisper-large-v3-turbo-german by Florian Zimmermeister @primeLine
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: German ASR Data-Mix
type: flozi00/asr-german-mixed
metrics:
- type: wer
value: 2.628 %
name: Test WER
datasets:
- flozi00/asr-german-mixed
- flozi00/asr-german-mixed-evals
base_model:
- primeline/whisper-large-v3-german
---
## Important note:
This model is just a CTranslate2 Translation, for usage in CTranslate conform frameworks such as faster-whisper.
For any questions about the fine tuning method or the dataset used please refer to the original Repo [primeline/whisper-large-v3-turbo-german](https://huggingface.co/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) |
## Evaluations - Word error rate
| Dataset | openai-whisper-large-v3-turbo | openai-whisper-large-v3 | primeline-whisper-large-v3-german | nyrahealth-CrisperWhisper (large)| primeline-whisper-large-v3-turbo-german |
|-------------------------------------|-------------------------------|-------------------------|-----------------------------------|---------------------------|-----------------------------------------|
| Tuda-De | 8.300 | 7.884 | 7.711 | **5.148** | 6.441 |
| common_voice_19_0 | 3.849 | 3.484 | 3.215 | **1.927** | 3.200 |
| multilingual librispeech | 3.203 | 2.832 | 2.129 | 2.815 | **2.070** |
| All | 3.649 | 3.279 | 2.734 | 2.662 | **2.628** |
The data and code for evaluations are available [here](https://huggingface.co/datasets/flozi00/asr-german-mixed-evals)
### 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: 12288
- Epochs: 3
- Learning rate: 1e-6
- Data augmentation: No
- Optimizer: [Ademamix](https://arxiv.org/abs/2409.03137)
### 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-turbo-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) |