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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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- hi |
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pipeline_tag: automatic-speech-recognition |
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--- |
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--- |
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language: |
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- hi |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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metrics: |
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- wer |
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model-index: |
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- name: LLM-HINDI-LARGE - Manan Raval |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs |
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type: google/fleurs |
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config: hn_in |
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split: test |
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metrics: |
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- type: wer |
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value: 12.33 |
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name: WER |
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## Usage |
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In order to infer a single audio file using this model, the following code snippet can be used: |
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```python |
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>>> import torch |
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>>> from transformers import pipeline |
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>>> # path to the audio file to be transcribed |
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>>> audio = "/path/to/audio.format" |
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>>> device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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>>> transcribe = pipeline(task="automatic-speech-recognition", model="web30india/LLM-Hindi-Large", chunk_length_s=30, device=device) |
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>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe") |
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>>> print('Transcription: ', transcribe(audio)["text"]) |
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``` |
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## Acknowledgement |
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This work was done at [Virtual Height IT Services Pvt. Ltd.] |
