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	---
	language: hi
	datasets:
	- openslr_hindi
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	- xlsr-hindi
	license: apache-2.0
	model-index:
	- name: Fine-tuned Hindi XLSR Wav2Vec2 Large
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	datasets:
	- name: Common Voice hi
	type: common_voice
	args: hi
	- name: OpenSLR Hindi
	url: https://www.openslr.org/resources/103/
	metrics:
	- name: Test WER
	type: wer
	value: 46.05
	---

	# Wav2Vec2-Large-XLSR-Hindi

	Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using OpenSLR Hindi dataset for training and Common Voice Hindi Test dataset for Evaluation. The OpenSLR Hindi data used for training was of size 10000 and it was randomly sampled. The OpenSLR train and test sets were combined and used as training data in order to increase the amount of variations. The evaluation was done on Common Voice Test set. The OpenSLR data is 8kHz and hence it was upsampled to 16kHz for training.

	When using this model, make sure that your speech input is sampled at 16kHz.

	Note: This is the first iteration of the fine-tuning. Will update this model if WER improves in future experiments.

	## Test Results

	\| Dataset \| WER \|
	\| ------- \| --- \|
	\| Test split Common Voice Hindi \| 46.055 % \|


	## Usage

	The model can be used directly (without a language model) as follows:

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

	test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")

	processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
	model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")

	resampler = torchaudio.transforms.Resample(48_000, 16_000)

	# 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[:2]["speech"], 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[:2]["sentence"])
	```

	## Evaluation

	The model can be evaluated as follows on the Hindi test data of Common Voice.

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

	test_dataset = load_dataset("common_voice", "hi", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
	model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
	model.to("cuda")

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

	# Preprocessing the datasets.
	# We need to read the audio 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)

	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"])))
	```

	## Code

	The Notebook used for training this model can be found at [shiwangi27/googlecolab](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.ipynb).
	I used a modified version of [run_common_voice.py](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.py) for training.