Create README.md

README.md

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+---
+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-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](https://huggingface.co/primeline/whisper-large-v3-german) |
+| Whisper large v3 turbo german    | 809M       | [link](https://huggingface.co/primeline/whisper-large-v3-turbo-german)
+| Distil-whisper large v3 german   | 756M       | [link](https://huggingface.co/primeline/distil-whisper-large-v3-german) |
+| tiny whisper                     | 37.8M      | [link](https://huggingface.co/primeline/whisper-tiny-german) |
+
+
+## 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](https://arxiv.org/abs/2409.03137)
+
+
+### 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](https://primeline-ai.com/en/)
+
+[![primeline AI](https://primeline-ai.com/wp-content/uploads/2024/02/pl_ai_bildwortmarke_original.svg)](https://primeline-ai.com/en/)
+
+
+Your partner for AI infrastructure in Germany <br>
+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](https://huggingface.co/flozi00)