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	---
	language: pt
	datasets:
	- common_voice
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: Wav2Vec2 Large 53 Portugese by Gunjan Chhablani
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice pt
	type: common_voice
	args: pt
	metrics:
	- name: Test WER
	type: wer
	value: 17.22
	---

	# Wav2Vec2-Large-XLSR-53-Portuguese

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
	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", "pt", split="test[:2%]")

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

	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):
	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 the Portuguese 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", "pt", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
	model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
	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: 17.22 %

	## Training

	The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py).
	The parameters passed were:

	```bash
	#!/usr/bin/env bash
	python run_common_voice.py \
	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
	--dataset_config_name="pt" \
	--output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \
	--cache_dir=/workspace/data \
	--overwrite_output_dir \
	--num_train_epochs="30" \
	--per_device_train_batch_size="32" \
	--per_device_eval_batch_size="32" \
	--evaluation_strategy="steps" \
	--learning_rate="3e-4" \
	--warmup_steps="500" \
	--fp16 \
	--freeze_feature_extractor \
	--save_steps="500" \
	--eval_steps="500" \
	--save_total_limit="1" \
	--logging_steps="500" \
	--group_by_length \
	--feat_proj_dropout="0.0" \
	--layerdrop="0.1" \
	--gradient_checkpointing \
	--do_train --do_eval \
	```

	Notebook containing the evaluation can be found [here](https://colab.research.google.com/drive/14e-zNK_5pm8EMY9EbeZerpHx7WsGycqG?usp=sharing).