Update README.md

README.md

@@ -12,8 +12,6 @@ model-index:
   results: []
 ---
 
-<!-- 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. -->
 
 # ru_whisper_small - Val123val
 
@@ -22,7 +20,7 @@ This model is a fine-tuned version of [openai/whisper-small](https://huggingface
 ## Model description
 
 Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. Russian language is only 5k hours within all.
-ru_whisper_small is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sberdevices_golos_10h_crowd dataset. ru-whisper is also potentially quite useful as an ASR solution for developers, especially for Russian speech recognition. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks
+ru_whisper_small is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Sberdevices_golos_10h_crowd dataset. ru-whisper is also potentially quite useful as an ASR solution for developers, especially for Russian speech recognition. They may exhibit additional capabilities, particularly if fine-tuned on business certain tasks.
 
 ## Intended uses & limitations
 
@@ -120,7 +118,6 @@ assistant_model = AutoModelForSpeechSeq2Seq.from_pretrained(
 
 assistant_model.to(device);
 
-
 # make pipe
 pipe = pipeline(
     "automatic-speech-recognition",