Update README.md

README.md

@@ -1,3 +1,38 @@
----
-license: mit
----
+---
+license: mit
+---
+
+```
+!pip install --upgrade bitsandbytes peft==0.5.0 transformers torch
+
+from transformers import (
+    AutomaticSpeechRecognitionPipeline,
+    WhisperForConditionalGeneration,
+    WhisperTokenizer,
+    WhisperProcessor,
+)
+from peft import PeftModel, PeftConfig
+import torch
+
+peft_model_id = "aisha-org/faster-whisper-uz" # Use the same model ID as before.
+language = "uz"
+task = "transcribe"
+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"
+)
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
+processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
+feature_extractor = processor.feature_extractor
+forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
+pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+
+def transcribe(audio):
+    with torch.cuda.amp.autocast():
+        text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
+    return text
+transcribe('path/to/audio.wav')
+```