Update README.md

README.md

@@ -1,3 +1,59 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- ARTPARK-IISc/Vaani
+language:
+- kn
+base_model:
+- openai/whisper-medium
+pipeline_tag: automatic-speech-recognition
+---
+```python
+
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperTokenizer,WhisperFeatureExtractor
+import soundfile as sf
+
+
+model="ARTPARK-IISc/whisper-small-vaani-medium"
+
+# Load tokenizer and feature extractor individually
+feature_extractor = WhisperFeatureExtractor.from_pretrained(model)
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-medium", language="Kannada", task="transcribe")
+
+
+# Create the processor manually
+processor = WhisperProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+# Load and preprocess the audio file
+audio_file_path = "Sample_Audio.wav"  # replace with your audio file path
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the processor and model
+model = WhisperForConditionalGeneration.from_pretrained(model).to(device)
+
+
+# load audio
+audio_data, sample_rate = sf.read(audio_file_path)
+# Ensure the audio is 16kHz (Whisper expects 16kHz audio)
+if sample_rate != 16000:
+    import torchaudio
+    resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+    audio_data = resampler(torch.tensor(audio_data).unsqueeze(0)).squeeze().numpy()
+
+
+# Use the processor to prepare the input features
+input_features = processor(audio_data, sampling_rate=16000, return_tensors="pt").input_features.to(device)
+
+# Generate transcription (disable gradient calculation during inference)
+with torch.no_grad():
+    predicted_ids = model.generate(input_features)
+
+# Decode the generated IDs into human-readable text
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+
+print(transcription)
+
+```