Boris Dayma

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	---
	language: en
	datasets:
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	license: apache-2.0
	model-index:
	- name: English XLSR Wav2Vec2 Large 53 with punctuation
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice en
	type: common_voice
	args: en
	metrics:
	- name: Test WER
	type: wer
	value: 1.0
	---

	# Wav2Vec2-Large-XLSR-53-English

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice).
	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:

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

	test_dataset = load_dataset("common_voice", "{lang_id}", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of [this](https://huggingface.co/languages) site.

	processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, e.g. `elgeish/wav2vec2-large-xlsr-53-arabic`
	model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, e.g. `elgeish/wav2vec2-large-xlsr-53-arabic`

	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):
	\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
	\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
	\treturn 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():
	\tlogits = 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 {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, e.g. French


	```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", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of [this](https://huggingface.co/languages) site.
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, e.g. `elgeish/wav2vec2-large-xlsr-53-arabic`
	model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, e.g. `elgeish/wav2vec2-large-xlsr-53-arabic`
	model.to("cuda")

	chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' # TODO: adapt this list to include all special characters you removed from the data
	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):
	\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
	\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
	\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
	\treturn 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):
	\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	\twith torch.no_grad():
	\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

	\tpred_ids = torch.argmax(logits, dim=-1)
	\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
	\treturn 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: XX.XX % # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.


	## Training

	The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO: adapt to state all the datasets that were used for training.

	The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.