Update README.md

README.md

@@ -5,4 +5,46 @@ language:
 - el
 metrics:
 - wer
----
+---
+
+# Whisper small finetuned for Greek transcription
+
+## How to use
+You can use the model for Greek ASR:
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import Audio, load_dataset
+
+# load model and processor
+processor = WhisperProcessor.from_pretrained("voxreality/whisper-small-el-finetune")
+model = WhisperForConditionalGeneration.from_pretrained("voxreality/whisper-small-el-finetune")
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="greek", task="transcribe")
+
+# load streaming dataset and read first audio sample
+ds = load_dataset("mozilla-foundation/common_voice_11_0", "el", split="test", streaming=True)
+ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+input_speech = next(iter(ds))["audio"]
+input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
+
+# generate token ids
+predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+
+# decode token ids to text
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+```
+
+You can also use an HF pipeline:
+```python
+from transformers import pipeline
+from datasets import Audio, load_dataset
+
+ds = load_dataset("mozilla-foundation/common_voice_11_0", "el", split="test", streaming=True)
+ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+input_speech = next(iter(ds))["audio"]
+
+pipe = pipeline("automatic-speech-recognition", model='voxreality/whisper-small-el-finetune',
+                             device='cpu', batch_size=32)
+
+transcription = pipe(input_speech['array'], generate_kwargs = {"language":f"<|el|>","task": "transcribe"})
+```