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	---
	language: bem
	datasets:
	- BembaSpeech
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Bemba by Claytone Sikasote
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: BembaSpeech bem
	type: bembaspeech
	args: bem
	metrics:
	- name: Test WER
	type: wer
	value: 42.17
	---

	# Wav2Vec2-Large-XLSR-53-Bemba

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Bemba language of Zambia using the [BembaSpeech](https://csikasote.github.io/BembaSpeech). 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("csv", data_files={"test": "/content/test.csv"}, delimiter="\t")["test"] # Adapt the path to test.csv

	processor = Wav2Vec2Processor.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
	model = Wav2Vec2ForCTC.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")

	#BembaSpeech is sample at 16kHz so we you do not need to resample
	#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"] = 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.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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 Bemba test data of BembaSpeech.


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

	test_dataset = load_dataset("csv", data_files={"test": "/content/test.csv"}, delimiter="\\t")["test"]
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
	model = Wav2Vec2ForCTC.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
	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"] = 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: 42.17 %

	## Training

	The BembaSpeech `train`, `dev` and `test` datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found [here](https://colab.research.google.com/drive/1aplFHfaXE68HGDwBYV2KqUWPasrk7bXv?usp=sharing).