Update README.md

README.md

@@ -44,33 +44,18 @@ The original code repository can be found [here](https://github.com/openai/whisp
 ## Usage
 
 ```python
-import torch
-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 = "openai/whisper-large-v3"
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-processor = AutoProcessor.from_pretrained(model_id)
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=30,
-    batch_size=16,
-    return_timestamps=True,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
-sample = dataset[0]["audio"]
-result = pipe(sample)
-print(result["text"])
+import whisper
+from transformers import pipeline
+
+model_name = "Aismantas/whisper-base-lithuanian"
+asr_pipeline = pipeline("automatic-speech-recognition", model=model_name)
+
+# Assuming the file is named 'audio.wav'
+audio_file = "example_1.wav"
+
+# Run the transcription
+transcription = asr_pipeline(audio_file)
+print(transcription)
 ```
 
 ## Fine-Tuning