Update README.md

README.md

@@ -4,27 +4,28 @@ library_name: peft
 ---
 
 ## Config
-model_name_or_path = "openai/whisper-large-v2"
-language = "Marathi"
-language_abbr = "mr"
-task = "transcribe"
-dataset_name = "mozilla-foundation/common_voice_11_0"
 
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path)
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, language=language, task=task)
-processor = AutoProcessor.from_pretrained(model_name_or_path, language=language, task=task)
-
-
-common_voice["train"] = load_dataset(dataset_name, language_abbr, split="train+validation", use_auth_token=True)
-common_voice["test"] = load_dataset(dataset_name, language_abbr, split="test", use_auth_token=True)
-
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name_or_path, load_in_8bit=True, device_map="auto")
-config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")
-
-model = get_peft_model(model, config)
-model.print_trainable_parameters()
-#"trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203"
+  model_name_or_path = "openai/whisper-large-v2"
+  language = "Marathi"
+  language_abbr = "mr"
+  task = "transcribe"
+  dataset_name = "mozilla-foundation/common_voice_11_0"
+  
+  feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path)
+  tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, language=language, task=task)
+  processor = AutoProcessor.from_pretrained(model_name_or_path, language=language, task=task)
+  
+  
+  common_voice["train"] = load_dataset(dataset_name, language_abbr, split="train+validation", use_auth_token=True)
+  common_voice["test"] = load_dataset(dataset_name, language_abbr, split="test", use_auth_token=True)
+  
+  
+  model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name_or_path, load_in_8bit=True, device_map="auto")
+  config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")
+  
+  model = get_peft_model(model, config)
+  model.print_trainable_parameters()
+  #"trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203"
 
 
 ## Training procedure