inference_code_snippet_added

README.md

@@ -31,14 +31,59 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) 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.
+
+## Usage
+
+In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
+
+The same repository also provides the scripts for faster inference using whisper-jax.
+
+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="vasista22/whisper-kannada-small", 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"])
+```
+
+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:
+
+```python
+>>> import jax.numpy as jnp
+>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
+
+>>> # path to the audio file to be transcribed
+>>> audio = "/path/to/audio.format"
+
+>>> transcribe = FlaxWhisperPipline("vasista22/whisper-kannada-small", batch_size=16)
+>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="kn", task="transcribe")
+
+>>> print('Transcription: ', transcribe(audio)["text"])
+```
+
 ## Training and evaluation data
 
-Training Data: MILE ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Google/Fleurs Train+Dev set.
-Evaluation Data: Google/Fleurs Test set, MILE Test set, OpenSLR.
+Training Data: 
+  - [IISc-MILE Kannada ASR Corpus](https://www.openslr.org/126/)
+  - [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#kannada-labelled-total-duration-is-60891-hours)
+  - [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
+  - [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
 
-## Training procedure
+Evaluation Data: 
+  - [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
+  - [IISc-MILE Test Set](https://www.openslr.org/126/)
+  - [OpenSLR](https://www.openslr.org/79/)
 
-### Training hyperparameters
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 1.7e-05
@@ -52,5 +97,6 @@ The following hyperparameters were used during training:
 - mixed_precision_training: True
 
 ## Acknowledgement
-This work was done at Speech Lab, IITM.
+This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
+
 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.