fonxlsr / README.md

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	---
	language: fon
	datasets:
	- fon_dataset
	metrics:
	- wer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	- hf-asr-leaderboard
	license: apache-2.0
	model-index:
	- name: Fon XLSR Wav2Vec2 Large 53
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: fon
	type: fon_dataset
	args: fon
	metrics:
	- name: Test WER
	type: wer
	value: 14.97
	---
	# Wav2Vec2-Large-XLSR-53-Fon

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).

	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 json
	import random
	import torch
	import torchaudio
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor


	#Load test_dataset from saved files in folder
	from datasets import load_dataset, load_metric

	#for test
	for root, dirs, files in os.walk(test/):
	test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")

	#Remove unnecessary chars
	chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
	def remove_special_characters(batch):
	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
	return batch

	test_dataset = test_dataset.map(remove_special_characters)

	processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
	model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")

	#No need for resampling because audio dataset already at 16kHz
	#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"]=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():
	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["sentence"][:2])
	```


	## Evaluation

	The model can be evaluated as follows on our unique Fon test data.

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

	for root, dirs, files in os.walk(test/):
	test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")

	chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
	return batch

	test_dataset = test_dataset.map(remove_special_characters)
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
	model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
	model.to("cuda")

	# 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[0].numpy()
	batch["sampling_rate"] = sampling_rate
	batch["target_text"] = batch["sentence"]
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)

	#Evaluation on test dataset
	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: 14.97 %

	## Training

	The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples).

	The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing)


	# Collaborators on this project

	- Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))\|(chris.emezue@gmail.com)
	- Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))\|([Twitter](https://twitter.com/bonadossou))\|(femipancrace.dossou@gmail.com)

	## This is a joint project continuing our research on [OkwuGbé: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762)