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
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 |
| Whisper large v3 turbo german | 809M | link |
| Distil-whisper large v3 german | 756M | link |
| tiny whisper | 37.8M | link |
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
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
How to use
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
Your partner for AI infrastructure in Germany 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