---
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.]