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cstr/
whisper-large-v3-turbo-german-int8_float32

Name: whisper-large-v3-turbo-german-int8_float32
Availability: InStock

transformers

automatic-speech-recognition

flozi00/asr-german-mixed

flozi00/asr-german-mixed-evals

2409.03137

primeline/whisper-large-v3-german

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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

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

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Model author: Florian Zimmermeister

Provenance and EU AI Act Art. 53 note

Upstream model: primeline/whisper-large-v3-german — published by primeline.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (INT8 precision). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

cstr/whisper-large-v3-turbo-german-int8_float32