đ Whisper Kannada Tiny
This model is a fine - tuned version of [openai/whisper - tiny](https://huggingface.co/openai/whisper - tiny) on Kannada data from multiple publicly available ASR corpuses. It was fine - tuned as part of the Whisper fine - tuning sprint, offering a powerful solution for Kannada automatic speech recognition.
đ Quick Start
This model is a fine - tuned version of [openai/whisper - tiny](https://huggingface.co/openai/whisper - tiny) on the Kannada data available from multiple publicly available ASR corpuses. It has been fine - tuned as a part of the Whisper fine - tuning sprint.
NOTE: The code used to train this model is available for re - use in the [whisper - finetune](https://github.com/vasistalodagala/whisper - finetune) repository.
⨠Features
- Fine - tuned on Kannada data from multiple ASR corpuses.
- Code for training is available for re - use.
- Can be evaluated on an entire dataset using provided evaluation codes.
- Supports faster inference with whisper - jax.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
Basic 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
>>>
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-tiny", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="kn", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
Advanced Usage
For faster inference of whisper models, the [whisper - jax](https://github.com/sanchit - gandhi/whisper - jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper - finetune#faster - evaluation - with - whisper - jax), before using the following code snippet:
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>>
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-kannada-tiny", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="kn", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
đ Documentation
Training and evaluation data
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 05
- train_batch_size: 88
- eval_batch_size: 88
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 15008 (terminated upon convergence. Initially set to 51570 steps)
- mixed_precision_training: True
đ§ Technical Details
The model is a fine - tuned version of [openai/whisper - tiny](https://huggingface.co/openai/whisper - tiny) on Kannada data. It was trained with specific hyperparameters and evaluated on multiple datasets as described above.
đ License
This model is released under the Apache 2.0 license.
Acknowledgement
This work was done at Speech Lab, IIT Madras. The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.