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primeline
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Model Details
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primeline
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Output Modalities
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text
+-----------------------------------------+-------+-----------+----------------------------+---------------------+| 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 |+-----------------------------------------+-------+-----------+----------------------------+---------------------+| Size | Parameters |
|---|---|
| tiny | 39 M |
| base | 74 M |
| small | 244 M |
| medium | 769 M |
| large | 1550 M |
| large-v2 | 1550 M |
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
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.
The training of the model was performed with the following hyperparameters
python
import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_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"])
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Model author: Florian Zimmermeister
Disclaimer
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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.+-----------------------------------------+-------+-----------+----------------------------+---------------------+| 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 |+-----------------------------------------+-------+-----------+----------------------------+---------------------+import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_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"])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.