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	---
	language: mr
	datasets:
	- openslr
	- interspeech_2021_asr
	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, InterSpeech 2021 ASR mr
	type: openslr, interspeech_2021_asr
	metrics:
	- name: Test WER
	type: wer
	value: 19.05
	---

	# 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 and [InterSpeech 2021](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. 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 `text` and `audio_path` fields:

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

	# test_data = #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-3")
	model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3")

	# 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["audio_path"])
	batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary
	return batch

	test_data= test_data.map(speech_file_to_array_fn)
	inputs = processor(test_data["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_data["text"][:2])
	```


	## Evaluation

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

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

	# test_data = #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-3")
	model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3")
	model.to("cuda")

	chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'


	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	batch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
	speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
	batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000)
	return batch

	test_data= test_data.map(speech_file_to_array_fn)

	# Preprocessing the datasets.
	# We need to read the audio 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_data.map(evaluate, batched=True, batch_size=8)
	print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
	```

	Test Result: 19.05 % (157+157 examples)

	Test Result on OpenSLR test: 14.15 % (157 examples)

	Test Results on InterSpeech test: 27.14 % (157 examples)

	## Training

	1412 examples of the OpenSLR Marathi dataset and 1412 examples of InterSpeech 2021 Marathi ASR dataset were used for training. For testing, 157 examples from each were used.

	The colab notebook used for training and evaluation can be found [here](https://colab.research.google.com/drive/15fUhb4bUFFGJyNLr-_alvPxVX4w0YXRu?usp=sharing).