primeline
/

whisper-large-v3-turbo-german

@@ -1,199 +1,103 @@
 ---
 library_name: transformers
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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
+---
+### 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) |
+| 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) |
+### 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
+### 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)