Model reference · open weights

whisper-large-german

Available as managed deployment Audio primeline Speech→text 2 variants 425k dl/mo

whisper-large-german is an open-weight audio or speech model from primeline. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byprimeline
TypeAudio & music
TaskSpeech→text
Parameters (lead)809M
Runs withtransformers
Based onprimeline/whisper-large-v3-german
Released2024-10-02
Popularity425k downloads / month
LicenceOpen weights

About

What whisper-large-german is

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

Read the full model card
  • 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

ModelParameterslink
Whisper large v3 german1.54Blink
Whisper large v3 turbo german809Mlink
Distil-whisper large v3 german756Mlink
tiny whisper37.8Mlink

Evaluations - Word error rate

Datasetopenai-whisper-large-v3-turboopenai-whisper-large-v3primeline-whisper-large-v3-germannyrahealth-CrisperWhisper (large)primeline-whisper-large-v3-turbo-german
Tuda-De8.3007.8847.7115.1486.441
common_voice_19_03.8493.4843.2151.9273.200
multilingual librispeech3.2032.8322.1292.8152.070
All3.6493.2792.7342.6622.628

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

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Model author: Florian Zimmermeister

Disclaimer

This model is not a product of the primeLine Group.

It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine.

The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH.

Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur.

Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model.

From the published model card. Full card on the HuggingFace links in the sidebar.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
Speech RecognitionGerman ASR Data-MixTest WER

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys whisper-large-german for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (whisper-large-german below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="whisper-large-german" -F file=@audio.mp3

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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