---
library_name: transformers
language:
- de
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# GRAG-WHISPER-LARGE-v3-TURBO-HESSIAN-AI

This model is fine-tuned on a carefully curated 13 hour dataset.


## Evaluations - Word error rate

| Test-Dataset                             | openai-whisper-large-v3-turbo | **GRAG-WHISPER-LARGE-v3-TURBO** | primeline-whisper-large-v3-turbo-german | 
|-------------------------------------|-------------------------------|-------------------------|-----------------------------------|
| Tuda-De                             | 8.195                         | **6.360**                   | 6.441                             | 
| common_voice_19_0                   | 3.839                         | 3.249                   | **3.217**                             | 
| multilingual librispeech            | 3.202                         | 2.071                   | **2.067**                             | 
| All                                 | 3.641                         | 2.633                   | **2.630**                             | 

The data and code for evaluations are available [here](https://huggingface.co/datasets/avemio/ASR-GERMAN-MIXED-EVALS-GRAG)

### Training data
The training data for this model includes conversations of spoken German with a mix of english business phrases included. The data was carefully selected and processed to optimize recognition performance. The dataset will not be published because of unclear situation if the data would be used for voice-cloning. The rights to use the collected data are only for the intended use to train speech-to-text models.

### 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 = "avemio/GRAG-WHISPER-LARGE-v3-TURBO"
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"])
```


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0