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	---
	language: mr
	datasets:
	- openslr
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: OpenSLR mr
	type: openslr
	metrics:
	- name: Test WER
	type: wer
	value: 14.53
	---

	# Wav2Vec2-Large-XLSR-53-Marathi

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that your speech input is sampled at 16kHz.

	## Usage

	The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields:

	```python
	import torch
	import torchaudio
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.

	processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
	model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")

	resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

	with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

	predicted_ids = torch.argmax(logits, dim=-1)

	print("Prediction:", processor.batch_decode(predicted_ids))
	print("Reference:", test_dataset["sentence"][:2])
	```


	## Evaluation

	The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.

	```python
	import torch
	import torchaudio
	from datasets import load_dataset, load_metric
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import re

	# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.

	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
	model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
	model.to("cuda")

	chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'
	resampler = torchaudio.transforms.Resample(48_000, 16_000)

	# Preprocessing the datasets.
	# We need to read the aduio files as arrays
	def speech_file_to_array_fn(batch):
	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)

	# Preprocessing the datasets.
	# We need to read the aduio files as arrays
	def evaluate(batch):
	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
	with torch.no_grad():
	logits = model(inputs.input_values.to("cuda"),
	attention_mask=inputs.attention_mask.to("cuda")).logits
	pred_ids = torch.argmax(logits, dim=-1)
	batch["pred_strings"] = processor.batch_decode(pred_ids)
	return batch

	result = test_dataset.map(evaluate, batched=True, batch_size=8)

	print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
	```

	Test Result: 14.53 %

	## Training

	90% of the OpenSLR Marathi dataset was used for training.
	The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing).