license: apache-2.0
language:
- de
library_name: transformers
pipeline_tag: automatic-speech-recognition
whisper-tiny-german
This model is a German Speech Recognition model based on the whisper-tiny model. The model weights count 37.8M parameters and with a size of 73MB in bfloat16 format.
As a follow-up to the Whisper large v3 german we decided to create a tiny version to be used in edge cases where the model size is a concern.
Intended uses & limitations
The model is intended to be used for German speech recognition tasks. It is designed to be used for edge cases where the model size is a concern. 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.
Dataset
The dataset used for training is a filtered subset of the Common Voice dataset, multilingual librispeech and some internal data. The data was filtered and double checked for quality and correctness. We did some normalization to the text data, especially for casing and punctuation.
Model family
| Model | Parameters | link |
|---|---|---|
| Whisper large v3 german | 1.54B | link |
| Distil-whisper large v3 german | 756M | link |
| tiny whisper | 37.8M | link |
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- total_train_batch_size: 512
- num_epochs: 5.0
Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.18.0
- Tokenizers 0.15.2
How to use
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"
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