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: 4.77 %
name: Test WER
datasets:
- flozi00/asr-german-mixed
- flozi00/asr-german-mixed-evals
base_model:
- primeline/whisper-large-v3-german
Quant
This is only a int8 quantization from primeline/whisper-large-v3-turbo-german per ctranslate2-converter, for usage e.g. in ctranslate2, faster-whisper, etc.
Modelcard from primeline/whisper-large-v3-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
| Dataset | openai-whisper-large-v3-turbo | openai-whisper-large-v3 | primeline-whisper-large-v3-german | nyrahealth-CrisperWhisper | primeline-whisper-large-v3-turbo-german |
|---|---|---|---|---|---|
| common_voice_19_0 | 6.31 | 5.84 | 4.30 | 4.14 | 4.28 |
| Tuda-De | 11.45 | 11.21 | 9.89 | 13.88 | 8.10 |
| multilingual librispeech | 18.03 | 17.69 | 13.46 | 10.10 | 4.71 |
| All | 14.16 | 13.79 | 10.51 | 8.48 | 4.75 |
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"])
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Model author: Florian Zimmermeister