whisper-large-v3-turbo-german-ggml

Quant

This is only a ggml from primeline/whisper-large-v3-turbo-german made with https://github.com/ggerganov/whisper.cpp/blob/master/models/convert-h5-to-ggml.py (minimally changed).

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

Evaluations

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

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 (GGML). 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.