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
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: hn_in
split: test
metrics:
Usage
In order to infer a single audio file using this model, the following code snippet can be used:
>>> 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.]
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