Update README.md

README.md

@@ -7,6 +7,7 @@ language:
 license: apache-2.0
 metrics:
 - wer
+- cer
 tags:
 - hf-asr-leaderboard
 - generated_from_trainer
@@ -26,12 +27,9 @@ model-index:
       name: Wer
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # Whisper Small Gujarati OpenSLR
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Gujarati OpenSLR dataset.
+This model is a fine-tuned version of [vasista22/whisper-gujarati-small](https://huggingface.co/vasista22/whisper-gujarati-small) on the Gujarati OpenSLR dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0472
 - Wer: 35.3258
@@ -77,3 +75,21 @@ The following hyperparameters were used during training:
 - Pytorch 2.3.0+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1
+
+## Usage
+
+In order to infer a single audio file using this model, the following code snippet can be used:
+
+```python
+>>> import torch
+>>> from transformers import pipeline
+
+>>> # path to the audio file to be transcribed
+>>> audio = "/path/to/audio.format"
+>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+>>> transcribe = pipeline(task="automatic-speech-recognition", model="1rsh/whisper-small-gu", chunk_length_s=30, device=device)
+>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="gu", task="transcribe")
+
+>>> print('Transcription: ', transcribe(audio)["text"])
+```