Update README.md

README.md

@@ -28,11 +28,22 @@ Trained on RTX 3070 for 30 hours using SwissDial all Dialects with following gui
 
 ## Uses
 
+```
+model_name_or_path = "openai/whisper-large-v3"
+task = "transcribe"
+import json
+import os
+from transformers import WhisperFeatureExtractor
+from transformers import WhisperTokenizer
+
+feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)
+tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, task=task)
+
 
 from peft import PeftModel, PeftConfig
 from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer
 
-peft_model_id = "Flurin17/whisper-large-v3-peft-swiss-german" # Use the same model ID as before.
+peft_model_id = "flurin17/whisper-large-v3-peft-swiss-german" # Use the same model ID as before.
 peft_config = PeftConfig.from_pretrained(peft_model_id)
 model = WhisperForConditionalGeneration.from_pretrained(
     peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto"
@@ -41,21 +52,14 @@ model = PeftModel.from_pretrained(model, peft_model_id)
 model.config.use_cache = True
 
 
-from transformers import WhisperFeatureExtractor
-
-feature_extractor = WhisperFeatureExtractor.from_pretrained(peft_model_id)
-
-from transformers import WhisperTokenizer
-
-tokenizer = WhisperTokenizer.from_pretrained(peft_model_id, language=language, task=task)
-
 from transformers import AutomaticSpeechRecognitionPipeline
 import torch
 pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
 
 with torch.cuda.amp.autocast():
-    result = pipe(r"L:\Coding\random\audio.mp3", generate_kwargs={"language": "german"})
+    result = pipe(r"L:\random\audio.mp3", generate_kwargs={"language": "german"})
 print(result["text"])
+```