primeline
/

whisper-tiny-german-1224

@@ -1,199 +1,129 @@
 ---
 library_name: transformers
-tags: []
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 ---
+license: apache-2.0
+language:
+- de
 library_name: transformers
+pipeline_tag: automatic-speech-recognition
 ---
+### Summary
+This model card provides information about a model based on the tiny whisper architecture that has been trained for speech recognition in German.
+Whisper is a powerful speech recognition platform developed by OpenAI.
+### 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
+## Evaluations - Word error rate
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| Model                                   |   All |   Tuda-De |   multilingual librispeech |   common_voice_19_0 |
++=========================================+=======+===========+============================+=====================+
+| openai-whisper-large-v3                 |  3.28 |      7.86 |                       2.85 |                3.46 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| openai-whisper-large-v3-turbo           |  3.64 |      8.20 |                       3.19 |                3.85 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| openai-whisper-medium                   |  5.49 |     11.13 |                       5.04 |                5.53 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| primeline-whisper-tiny-german-1224      |  6.26 |      9.62 |                       4.97 |                8.46 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| openai-whisper-small                    |  9.54 |     15.94 |                       8.77 |               10.15 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| openai-whisper-base                     | 18.75 |     33.58 |                      17.15 |               19.74 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+| openai-whisper-tiny                     | 28.80 |     47.33 |                      26.47 |               30.76 |
++-----------------------------------------+-------+-----------+----------------------------+---------------------+
+The results are calculated in December 2024 and may change over the time with updates to the eval corpus.
+For always the newest results please check the code and dataset page.
+The data and code for evaluations are available [here](https://huggingface.co/datasets/flozi00/asr-german-mixed-evals)
+### 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.
+The dataset size is about 6.000 hours of public, proprietary and synthetic data.
+### Training process
+The training of the model was performed with the following hyperparameters
+- Batch size: 32768
+- Epochs: 48
+- Learning rate: 1e-4
+- 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-tiny-german-1224"
+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
+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)
+**Disclaimer**
+```
+This model is not a product of the primeLine Group.
+It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine.
+The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH.
+Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur.
+Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model.
+```