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# Whisper Hindi Medium

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Hindi 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-hindi-medium", 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"])
```

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-hindi-medium", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")

>>> print('Transcription: ', transcribe(audio)["text"])
```

## Training and evaluation data

Training Data: 
  - [GramVaani ASR Corpus](https://sites.google.com/view/gramvaaniasrchallenge/dataset?authuser=0)
  - [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#hindi-labelled--total-duration-is-239876-hours)
  - [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
  - [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)

Evaluation Data: 
  - [GramVaani ASR Corpus Test Set](https://sites.google.com/view/gramvaaniasrchallenge/dataset?authuser=0)
  - [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)

## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 38754 (Initially set to 129180 steps)
- mixed_precision_training: True

## Acknowledgement
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.
{"language": ["hi"], "license": "apache-2.0", "tags": ["whisper-event"], "metrics": ["wer"], "model-index": [{"name": "Whisper Hindi Medium - Vasista Sai Lodagala", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google/fleurs", "config": "hi_in", "split": "test"}, "metrics": [{"type": "wer", "value": 6.82, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "mozilla-foundation/common_voice_11_0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "test"}, "metrics": [{"type": "wer", "value": 11.38, "name": "WER"}]}]}]}
transformers pytorch jax whisper automatic-speech-recognition whisper-event hi license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #hi #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 




# Whisper Hindi Medium

This model is a fine-tuned version of openai/whisper-medium on the Hindi 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 repository.

## Usage

In order to evaluate this model on an entire dataset, the evaluation codes available in the 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:



For faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:



## Training and evaluation data

Training Data: 
  - GramVaani ASR Corpus
  - ULCA ASR Corpus
  - Shrutilipi ASR Corpus
  - Google/Fleurs Train+Dev set

Evaluation Data: 
  - GramVaani ASR Corpus Test Set
  - Google/Fleurs Test Set

## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 38754 (Initially set to 129180 steps)
- mixed_precision_training: True

## 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.
# Whisper Hindi Medium

This model is a fine-tuned version of openai/whisper-medium on the Hindi 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 repository. ## Usage

In order to evaluate this model on an entire dataset, the evaluation codes available in the 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:



For faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet: ## Training and evaluation data

Training Data: 
  - GramVaani ASR Corpus
  - ULCA ASR Corpus
  - Shrutilipi ASR Corpus
  - Google/Fleurs Train+Dev set

Evaluation Data: 
  - GramVaani ASR Corpus Test Set
  - Google/Fleurs Test Set ## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 38754 (Initially set to 129180 steps)
- mixed_precision_training: True ## 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.
TAGS
#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #hi #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Whisper Hindi Medium

This model is a fine-tuned version of openai/whisper-medium on the Hindi 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 repository. ## Usage

In order to evaluate this model on an entire dataset, the evaluation codes available in the 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:



For faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet: ## Training and evaluation data

Training Data: 
  - GramVaani ASR Corpus
  - ULCA ASR Corpus
  - Shrutilipi ASR Corpus
  - Google/Fleurs Train+Dev set

Evaluation Data: 
  - GramVaani ASR Corpus Test Set
  - Google/Fleurs Test Set ## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 38754 (Initially set to 129180 steps)
- mixed_precision_training: True ## 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.
TAGS
#transformers #pytorch #jax #whisper #automatic-speech-recognition #whisper-event #hi #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Whisper Hindi Medium

This model is a fine-tuned version of openai/whisper-medium on the Hindi 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 repository.## Usage

In order to evaluate this model on an entire dataset, the evaluation codes available in the 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:



For faster inference of whisper models, the whisper-jax library can be used. Please follow the necessary installation steps as mentioned here, before using the following code snippet:## Training and evaluation data

Training Data: 
  - GramVaani ASR Corpus
  - ULCA ASR Corpus
  - Shrutilipi ASR Corpus
  - Google/Fleurs Train+Dev set

Evaluation Data: 
  - GramVaani ASR Corpus Test Set
  - Google/Fleurs Test Set## Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 48
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 38754 (Initially set to 129180 steps)
- mixed_precision_training: True## 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.