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--- |
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license: apache-2.0 |
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language: |
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- de |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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model-index: |
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- name: whisper-large-v3-german by Florian Zimmermeister @primeLine |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: Common Voice de |
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type: common_voice_15 |
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args: de |
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metrics: |
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- type: wer |
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value: 3.002 % |
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name: Test WER |
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- type: cer |
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value: 0.81 % |
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name: Test CER |
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--- |
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### Summary |
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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. |
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### Applications |
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This model can be used in various application areas, including |
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- Transcription of spoken German language |
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- Voice commands and voice control |
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- Automatic subtitling for German videos |
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- Voice-based search queries in German |
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- Dictation functions in word processing programs |
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## Model family |
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| Model | Parameters | link | |
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|----------------------------------|------------|--------------------------------------------------------------| |
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| Whisper large v3 german | 1.54B | [link](https://huggingface.co/primeline/whisper-large-v3-german) | |
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| Distil-whisper large v3 german | 756M | [link](https://huggingface.co/primeline/distil-whisper-large-v3-german) | |
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| tiny whisper | 37.8M | [link](https://huggingface.co/primeline/whisper-tiny-german) | |
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### Training data |
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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. |
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### Training process |
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The training of the model was performed with the following hyperparameters |
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- Batch size: 1024 |
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- Epochs: 2 |
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- Learning rate: 1e-5 |
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- Data augmentation: No |
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### How to use |
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```python |
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import torch |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline |
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from datasets import load_dataset |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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model_id = "primeline/whisper-large-v3-german" |
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model = AutoModelForSpeechSeq2Seq.from_pretrained( |
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True |
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) |
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model.to(device) |
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processor = AutoProcessor.from_pretrained(model_id) |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=model, |
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tokenizer=processor.tokenizer, |
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feature_extractor=processor.feature_extractor, |
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max_new_tokens=128, |
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chunk_length_s=30, |
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batch_size=16, |
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return_timestamps=True, |
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torch_dtype=torch_dtype, |
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device=device, |
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) |
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dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") |
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sample = dataset[0]["audio"] |
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result = pipe(sample) |
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print(result["text"]) |
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``` |
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## [About us](https://primeline-ai.com/en/) |
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[](https://primeline-ai.com/en/) |
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Your partner for AI infrastructure in Germany <br> |
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Experience the powerful AI infrastructure that drives your ambitions in Deep Learning, Machine Learning & High-Performance Computing. Optimized for AI training and inference. |
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Model author: [Florian Zimmermeister](https://huggingface.co/flozi00) |
