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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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--- |
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# whisper-tiny-german |
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This model is a German Speech Recognition model based on the [whisper-tiny](https://huggingface.co/openai/whisper-tiny) model. |
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The model weights count 37.8M parameters and with a size of 73MB in bfloat16 format. |
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As a follow-up to the [Whisper large v3 german](https://huggingface.co/primeline/whisper-large-v3-german) we decided to create a tiny version to be used in edge cases where the model size is a concern. |
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## Intended uses & limitations |
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The model is intended to be used for German speech recognition tasks. |
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It is designed to be used for edge cases where the model size is a concern. |
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It's not recommended to use this model for critical use cases, as it is a tiny model and may not perform well in all scenarios. |
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## Dataset |
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The dataset used for training is a filtered subset of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, multilingual librispeech and some internal data. |
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The data was filtered and double checked for quality and correctness. |
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We did some normalization to the text data, especially for casing and punctuation. |
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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 hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- total_train_batch_size: 512 |
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- num_epochs: 5.0 |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0a0+ebedce2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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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-tiny-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) |
