Update README.md

README.md

@@ -12,21 +12,21 @@ pipeline_tag: automatic-speech-recognition
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# GRAG-WHISPER-LARGE-v3-TURBO-HESSIAN-AI
+# German-RAG-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 | 
+| Test-Dataset                             | openai-whisper-large-v3-turbo | **German-RAG-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)
+The data and code for evaluations are available [here](https://huggingface.co/datasets/avemio/ASR-GERMAN-MIXED-EVALS-German-RAG)
 
 ### 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 The dataset will not be published because of unclear situation if the data would be used for voice-cloning.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.
@@ -39,7 +39,7 @@ 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_id = "avemio/German-RAG-WHISPER-LARGE-v3-TURBO"
 model = AutoModelForSpeechSeq2Seq.from_pretrained(
     model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
 )
@@ -69,4 +69,11 @@ print(result["text"])
 - Transformers 4.47.1
 - Pytorch 2.5.1+cu121
 - Datasets 3.2.0
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0
+
+
+## The German-RAG AI Team
+[Marcel Rosiak](https://de.linkedin.com/in/marcel-rosiak)
+[Soumya Paul](https://de.linkedin.com/in/soumya-paul-1636a68a)
+[Siavash Mollaebrahim](https://de.linkedin.com/in/siavash-mollaebrahim-4084b5153?trk=people-guest_people_search-card)
+[Zain ul Haq](https://de.linkedin.com/in/zain-ul-haq-31ba35196)