Update README.md

README.md

@@ -1,3 +1,52 @@
 ---
 license: creativeml-openrail-m
+language:
+- en
+- hi
+pipeline_tag: automatic-speech-recognition
 ---
+---
+language:
+- hi
+license: apache-2.0
+tags:
+- whisper-event
+metrics:
+- wer
+model-index:
+- name: LLM-HINDI-LARGE - Manan Raval
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: google/fleurs
+      type: google/fleurs
+      config: hn_in
+      split: test
+    metrics:
+    - type: wer
+      value: 12.33
+      name: WER
+
+
+## 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="web30india/LLM-Hindi-Large", chunk_length_s=30, device=device)
+>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")
+
+>>> print('Transcription: ', transcribe(audio)["text"])
+```
+
+## Acknowledgement
+This work was done at [Virtual Height IT Services Pvt. Ltd.]